Critical AI Engagement Framework, Versions 1.0 and 1.4 (scroll down…)

NB: This page was original published 24 March 2026, and has since been updated on 21 May 2026 to reflect the latest framework iteration.

This framework draws on Hosseini's (2026) forthcoming paper in The Geographical Journal, which examines how generative AI tools reproduce racial, gendered, and class-based representations through algorithmic coloniality (Mohamed et al., 2020). That work demonstrates how seemingly neutral prompts encode dominant cultural assumptions, producing outputs that reflect and reinforce existing inequalities.

The Critical AI Engagement Framework extends this analysis into a practical pedagogical tool, mapping how individuals engage with generative AI across two axes: epistemic posture and structural consciousness. It is designed for use across educational contexts, supporting educators and learners in moving beyond prompt refinement toward critical, collective, and structurally informed engagement with AI systems.

April 2026 update: the paper informing this framework has now been published and is available below

Hosseini, D. D. 2026. “Generative AI: A Problematic Illustration of the Intersections of Race, Gender and Class.” The Geographical Journal 192, no. 2: e70085. https://doi.org/10.1111/geoj.70085.

Critical AI Engagement Framework — Hosseini
Critical AI Engagement Framework
Grounded in Mohamed et al. (2020), Benjamin (2019), Noble (2018), Zembylas (2023), Crenshaw (1991)
Hosseini — Version 1.0, March 2026
For academic and workshop use
Not for citation without permission
Design developed with Claude Sonnet 4.6 (Anthropic, 2026)
With special thanks to Dr Sara Camacho Felix

Generative AI tools are now embedded in higher education, used to draft text, generate images, produce code, and synthesise research. Yet these tools are not neutral. They encode the assumptions, hierarchies, and exclusions of the data they were trained on, reproducing patterns of racial, gendered, and class-based harm even as their outputs become more sophisticated (Benjamin, 2019; Mohamed et al., 2020; Noble, 2018). Understanding how students engage with these tools, and what that engagement does or does not interrogate, is therefore an urgent task for educators.

The Critical AI Engagement Framework maps educational engagement with generative AI across two axes. The horizontal axis describes an individual’s epistemic posture toward AI: from treating its outputs as authoritative, through questioning them, to recognising the colonial and structural conditions that shape what AI knows, whose knowledge it centres, and what it silences (Hosseini, 2026; Maalsen, 2023). The vertical axis describes how far an individual recognises AI as a socio-technical artefact shaped by relations of power, rather than simply a tool with fixable flaws (Benjamin, 2019; Quijano, 2000; Zembylas, 2023). The positions individuals occupy are not freely chosen but reflect the institutional, curricular, and social conditions that shape how they learn and work. The framework’s aspiration is not a more capable individual user but collective action: sustained, community-grounded engagement that works toward structural change (Camacho Felix, 2025; Mohamed et al., 2020).

Epistemic deference
AI as neutral oracle
Critical interrogation
Outputs questioned
Epistemic agency
Whose knowledge?
Collective / relational
Critique with others
Individualized
engagement
AI as personal tool,
neutral & apolitical
The Uncritical Receiver
Accepts outputs; AI naturalized as neutral knowledge source
Theoretical anchor
Algorithmic coloniality (Mohamed et al., 2020): student treats AI outputs as objective, without recognizing that GenAI encodes "opaque, inconsistent cultural assumptions" shaped by historically racist and sexist training data (Keshishi & Hosseini, 2023; Benjamin, 2019).
For educators
Make the assumption of neutrality visible through practical experiments. Ask: whose perspective does a GenAI output reflect by default — in an image, a summary, a clinical recommendation, or a generated essay? Drawing on Day & Esson (2025) and Hosseini (2026), show how seemingly neutral prompts reproduce skewed cultural defaults across output types.
For students
The student experiences AI outputs as "natural" rather than constructed. As Benjamin (2019) argues, socio-technical artefacts are not static reflections — they are shaped by the feedback and values of those who built them. Students need a framework to see this, not just permission to question.
The Cautious Pragmatist
Checks outputs; AI still framed as neutral instrument
Theoretical anchor
The student audits outputs for factual errors but not for the cultural assumptions encoded in them. As Day & Esson (2025) show, even anomalous outputs require users to "remain vigilant to the opaque and shifting nature of generative AI tools" — vigilance the Cautious Pragmatist applies technically but not epistemically.
For educators
Shift from "is this accurate?" to "whose accuracy?" Introduce temporal dynamism (Kleinman, 2024): improved outputs — whether images, text, or code — do not mean underlying biases have been addressed. Use Spennemann & Oddone's (2025) technique of asking GenAI to explain its own outputs as a critical exercise.
For students
May believe that better prompting solves the problem. However, prompt refinement "would not address the underlying biases within the datasets themselves" (Hosseini, 2026). The student needs to move from refining inputs to interrogating the training data and the colonial logics embedded within it.
The Epistemically Alert
Interrogates whose knowledge is centered; notices silences
Theoretical anchor
Algorithmic coloniality (Mohamed et al., 2020): student recognizes that AI systems embed a "dominant, Eurocentric worldview" that upholds hierarchical, racialized, and gendered ways of knowing. Connects to Noble's (2018) algorithms of oppression and Maalsen's (2023) algorithmic epistemologies.
For educators
Move from naming bias to interrogating its origin. Use Quijano's (2000) coloniality of power to show that AI's racial and gender defaults are not errors but expressions of colonial hierarchies embedded in training data. Ask: what would an AI trained on non-Eurocentric datasets produce differently?
For students
May feel isolated, especially when institutional AI guidance frames the issue as a technical problem. Wilby & Esson's (2024) call for "capabilities, caveats, and criticality" provides legitimizing language. Connect to communities of practice doing this work.
The Isolated Disruptor
Critiques AI alone; change without solidarity
Theoretical anchor
Individual critique of algorithmic coloniality, however sophisticated, cannot address structural problems in proprietary and inaccessible training datasets (Amoore et al., 2024). Mohamed et al. (2020) are explicit: structural change requires "political coalitions and communities," not individual actors.
For educators
Connect students to collective and cross-disciplinary action. Addressing algorithmic coloniality requires breaking down "disciplinary and departmental silos" (Hosseini, 2026; Maalsen, 2023). Individual insight without structural leverage changes nothing about the datasets or systems producing harmful outputs.
For students
Risk of cynicism or disengagement when individual critique runs up against inaccessible, proprietary datasets and opaque systems. As Hosseini (2026) demonstrates, surface improvements in GenAI outputs can mask rather than resolve the underlying colonial logics — students need community and strategy, not just analysis.
Partial structural
awareness
Senses bias or harm,
lacks systemic account
The Uneasy Adapter
Senses something wrong; lacks language to name it
Theoretical anchor
Pre-conceptual awareness of algorithmic harm: student senses that something is "off" in AI outputs — perhaps noticing racial or gender skew — but has not yet encountered the theoretical vocabulary to name it. This is the moment described by Day & Esson (2025) when outputs produce "surprising results."
For educators
This is a threshold moment. Offer concepts — algorithmic coloniality (Mohamed et al., 2020), algorithms of oppression (Noble, 2018), socio-technical artefacts (Benjamin, 2019) — as language for what is already felt. Hosseini's (2026) method of prompting GenAI and critically analyzing outputs is a replicable pedagogical entry point adaptable across text, image, and code generation.
For students
High potential. Already doing affective critical work. Avoid rushing to resolution — the unease is epistemically productive. GenAI outputs should be approached "not [as] surprising, but as symptomatic of racialised and gendered logics" (Hosseini, 2026) embedded in training data across all output modalities.
The Informed Skeptic
Identifies bias in outputs; most common profile
Theoretical anchor
Can identify racial and gender skew in outputs — consistent with quantitative evidence (Cheong et al., 2024; Currie et al., 2024, 2025) — but frames it as a dataset problem rather than an expression of algorithmic coloniality (Mohamed et al., 2020). The systemic account is absent.
For educators
Move from "bias as glitch" to "bias as design." Use Benjamin's (2019, p. 59) argument that training datasets carry "the prejudices of the individuals who compiled them." Ask: why does a GenAI default encode particular assumptions about race, class, gender, or expertise — whether producing an image, drafting a clinical summary, or generating a curriculum resource?
For students
May believe that surface improvements — more realistic outputs, more diverse teams, better prompts — will resolve the issue. Hosseini (2026) demonstrates directly that successive GenAI model versions produced aesthetically improved outputs while reproducing the same racial and gendered logic. The technical fix does not address colonial logics in the training data.
The Structural Analyst
Names AI harms systemically; connects to power
Theoretical anchor
Understands AI as a socio-technical artefact (Benjamin, 2019) shaped by Silicon Valley's role as "part of the United States, a global hegemon and a successor to European colonial powers" (Keshishi & Hosseini, 2023). Connects algorithmic coloniality (Mohamed et al., 2020) to concrete outputs.
For educators
Deepen from analysis to action. Introduce reparative description (Parry, 2023): how might geographers work with public image repositories to revise false past categorizations? Introduce Zembylas's (2023) strategies for "undoing the ethics of digital neocolonialism."
For students
May become frustrated that structural analysis does not translate into change. Channel into cross-disciplinary collaboration. Addressing problematic training data requires collective action and "relational approaches that emphasise the spatial and political contexts of algorithms" (Maalsen, 2023; Hosseini, 2026). Analysis without community and outlet risks paralysis.
The Emerging Ally
Seeks solidarity; building shared critical vocabulary
Theoretical anchor
Transitional position between individual and collective consciousness (Freire). Recognises that critique must be collective but lacks the structural analysis to ground it yet.
For educators
Facilitate cross-disciplinary collaboration explicitly. Addressing algorithmic harm requires breaking "disciplinary and departmental silos" (Hosseini, 2026; Maalsen, 2023) — across education, geography, data science, and activism. Connect emerging allies to existing coalitions and communities of practice doing this work.
For students
Motivated by justice but may lack the analytical vocabulary to sustain critique under institutional pressure. Pairing with theoretically grounded peers — including those with lived experience of the harms being analyzed (cf. acknowledgments in Keshishi & Hosseini, 2023) — is more generative than educator-only support.
Structural
consciousness
AI as site of
coloniality & harm
Conscientized but Constrained
Sees the system; defers under institutional pressure
Theoretical anchor
Understands algorithmic coloniality and its harms but operates in institutional systems — curriculum, assessment, professional bodies — that have not caught up with the critique. Within many national contexts "there are nascent discussions on the ethical issues of using Gen AI technologies within tertiary education" (Hosseini, 2026) — the institutional conversation is beginning but remains uneven.
For educators
Name the institutional lag explicitly. Developing "algorithmic literacy as part of wider digital literacy initiatives" (Kong et al., 2023; Zembylas, 2023; Hosseini, 2026) is a growing expectation — the conversation is beginning, and students can actively contribute to shaping it rather than waiting for institutions to catch up.
For students
Risk of internalizing structural constraint as personal inadequacy. The student's tension is not a sign of failure — it is evidence of structural contradictions that institutions have not yet resolved. Validate the critique while building pathways to act within and against institutional constraints.
The Critical Refuser
Refuses metaphorical framing; acts on structural critique
Theoretical anchor
Tuck & Yang (2012): decolonization is not a metaphor. Student refuses cosmetic diversity framings and demands structural change to what AI produces and whom it serves.
For educators
Support with Mohamed et al.'s (2020) practical recommendations: identifying sites of coloniality in AI systems, understanding where and how algorithms are made, engaging in reparative description (Parry, 2023), and developing local and national policy challenges to colonial algorithmic logics.
For students
May encounter resistance from colleagues who frame AI critique as technophobia or obstructionism. Documentation and publication — as Hosseini (2026) demonstrates — transforms resistant practice into sharable pedagogical resource. Connect to communities doing this work across disciplines; the argument gains force collectively.
The Critical Collaborator
Challenges AI's epistemic order; builds alternatives
Theoretical anchor
Actively participates in co-creating "instructional materials that transcend boundaries" (Hosseini, 2026) — resources that make algorithmic coloniality visible and addressable across GenAI modalities. Draws on intersectionality (Crenshaw, 1991; Hill Collins, 2019) to hold race, gender, and class in simultaneous analysis rather than treating each as a separate problem.
For educators
Commission rather than assess. Meaningful critique of algorithmic coloniality requires centering those with lived expertise in the harms being analyzed — not as informants but as co-authors (Hosseini, 2026). This student's contribution should shape pedagogy, not merely illustrate it. Invite co-authorship, co-design, and co-delivery.
For students
Risk of co-option — being absorbed as institutional evidence of diversity without structural change. Hosseini's (2026) reflexive positioning — centering colleagues with lived expertise in racial and gender inequity — models how genuine co-production differs from performative consultation. Support students to name and resist this distinction.
The Praxis Collective aspirational*
Reflection + action with others; pluriversal praxis
Theoretical anchor
Camacho Felix's (2025) decolonial imaginations and collective imagination — "unveiling different possibilities for addressing injustices" through relational, mutual aid. Mohamed et al.'s (2020) political coalitions. Benjamin's (2019) abolitionist tools for dismantling the New Jim Code in AI systems.
For educators
Collective praxis around GenAI requires institutional conditions: time, resource, partnership, and willingness to redistribute epistemic authority. It demands cross-disciplinary collaboration, reparative dataset work (Parry, 2023), and policy advocacy (Hosseini, 2026; Mohamed et al., 2020) — none of which individual pedagogy alone can produce. Educators must build the structures, not just model the position.
For students
Students here are co-researchers and co-educators. Hosseini (2026) models this directly: conducting experiments, publishing findings, and encouraging readers to replicate and extend the work with a critical eye. Sustain rather than assess — the goal is ongoing collective action that outlasts the course, not a demonstration of competence for a grade.
← epistemic deference
collective / relational agency →
Movement across these axes is non-linear — students may hold multiple positions simultaneously across different contexts and knowledge domains
Theoretical grounding
Horizontal axis: Mohamed et al. (2020) — algorithmic coloniality; Noble (2018) — algorithms of oppression; Maalsen (2023) — algorithmic epistemologies and situated knowledge  ·  Vertical axis: Benjamin (2019) — socio-technical artefacts encoding racial inequity; Zembylas (2023) — decolonial AI in HE; Quijano (2000) — coloniality of power; Camacho Felix (2025) — decolonial imaginations and collective action

Critical AI Engagement Framework Version 1.4

This version adds in the researcher lens and concepts such as epistemic accountability and the research lifecycle.

Critical AI Engagement Framework — Hosseini (v1.4)
Critical AI Engagement Framework
Grounded in Mohamed et al. (2020), Benjamin (2019), Noble (2018), Zembylas (2023), Crenshaw (1991)
Hosseini — Version 1.4, May 2026
Originally published Version 1.0, March 2026
For academic and workshop use
Not for citation without permission
Design developed with Claude Sonnet 4.6 (Anthropic, March 2026)
With special thanks to Dr Sara Camacho Felix
View
Researcher

Generative AI tools are now embedded in higher education, used to draft text, generate images, produce code, and synthesise research. Yet these tools are not neutral. They encode the assumptions, hierarchies, and exclusions of the data they were trained on, reproducing patterns of racial, gendered, and class-based harm even as their outputs become more sophisticated (Benjamin, 2019; Mohamed et al., 2020; Noble, 2018). Understanding how students engage with these tools, and what that engagement does or does not interrogate, is therefore an urgent task for educators.

The Critical AI Engagement Framework maps educational engagement with generative AI across two axes. The horizontal axis describes an individual’s epistemic posture toward AI: from treating its outputs as authoritative, through questioning them, to recognizing the colonial and structural conditions that shape what AI knows, whose knowledge it centers, and what it silences (Hosseini, 2026; Maalsen, 2023). The vertical axis describes how far an individual recognizes AI as a socio-technical artefact shaped by relations of power, rather than simply a tool with fixable flaws (Benjamin, 2019; Quijano, 2000; Zembylas, 2023). The positions individuals occupy are not freely chosen but reflect the institutional, curricular, and social conditions that shape how they learn and work. The framework’s aspiration is not a more capable individual user but collective action: sustained, community-grounded engagement that works toward structural change (Camacho Felix, 2025; Mohamed et al., 2020).

Epistemic deference
AI as neutral oracle
Critical interrogation
Outputs questioned
Epistemic agency
Whose knowledge?
Collective / relational
Critique with others
Individualized
engagement
AI as personal tool,
neutral & apolitical
The Uncritical Receiver
Accepts outputs; AI naturalized as neutral knowledge source
Theoretical anchor
Algorithmic coloniality (Mohamed et al., 2020): student treats AI outputs as objective, without recognizing that GenAI encodes "opaque, inconsistent cultural assumptions" shaped by historically racist and sexist training data (Keshishi & Hosseini, 2023; Benjamin, 2019).
For educators
Make the assumption of neutrality visible through practical experiments. Ask: whose perspective does a GenAI output reflect by default — in an image, a summary, a clinical recommendation, or a generated essay? Drawing on Day & Esson (2025) and Hosseini (2026), show how seemingly neutral prompts reproduce skewed cultural defaults across output types.
For students
The student experiences AI outputs as "natural" rather than constructed. As Benjamin (2019) argues, socio-technical artefacts are not static reflections — they are shaped by the feedback and values of those who built them. Students need a framework to see this, not just permission to question.
The Cautious Pragmatist
Checks outputs; AI still framed as neutral instrument
Theoretical anchor
The student audits outputs for factual errors but not for the cultural assumptions encoded in them. As Day & Esson (2025) show, even anomalous outputs require users to "remain vigilant to the opaque and shifting nature of generative AI tools" — vigilance the Cautious Pragmatist applies technically but not epistemically.
For educators
Shift from "is this accurate?" to "whose accuracy?" Note that improved outputs do not mean underlying biases have been addressed (Hosseini, 2026). Use Spennemann & Oddone's (2025) technique of asking GenAI to explain its own outputs as a critical exercise.
For students
May believe that better prompting solves the problem. However, prompt refinement "would not address the underlying biases within the datasets themselves" (Hosseini, 2026). The student needs to move from refining inputs to interrogating the training data and the colonial logics embedded within it.
The Epistemically Alert
Interrogates whose knowledge is centered; notices silences
Theoretical anchor
Algorithmic coloniality (Mohamed et al., 2020): student recognizes that AI systems embed a "dominant, Eurocentric worldview" that upholds hierarchical, racialized, and gendered ways of knowing. Connects to Noble's (2018) algorithms of oppression and Maalsen's (2023) algorithmic epistemologies.
For educators
Move from naming bias to interrogating its origin. Use Quijano's (2000) coloniality of power to show that AI's racial and gender defaults are not errors but expressions of colonial hierarchies embedded in training data. Ask: what would an AI trained on non-Eurocentric datasets produce differently?
For students
May feel isolated, especially when institutional AI guidance frames the issue as a technical problem. Wilby & Esson's (2024) call for "capabilities, caveats, and criticality" provides legitimizing language. Connect to communities of practice doing this work.
The Isolated Disruptor
Critiques AI alone; change without solidarity
Theoretical anchor
Individual critique of algorithmic coloniality, however sophisticated, cannot address structural problems in proprietary and inaccessible training datasets (Amoore et al., 2024). Mohamed et al. (2020) are explicit: structural change requires "political coalitions and communities," not individual actors.
For educators
Connect students to collective and cross-disciplinary action. Addressing algorithmic coloniality requires breaking down "disciplinary and departmental silos" (Hosseini, 2026; Maalsen, 2023). Individual insight without structural leverage changes nothing about the datasets or systems producing harmful outputs.
For students
Risk of cynicism or disengagement when individual critique runs up against inaccessible, proprietary datasets. As Hosseini (2026) demonstrates, surface improvements in GenAI outputs can mask rather than resolve underlying colonial logics — students need community and strategy, not just analysis.
Partial structural
awareness
Senses bias or harm,
lacks systemic account
The Uneasy Adapter
Senses something wrong; lacks language to name it
Theoretical anchor
Pre-conceptual awareness of algorithmic harm: student senses that something is "off" in AI outputs — perhaps noticing racial or gender skew — but has not yet encountered the theoretical vocabulary to name it. This is the moment described by Day & Esson (2025) when outputs produce "surprising results."
For educators
This is a threshold moment. Offer concepts — algorithmic coloniality (Mohamed et al., 2020), algorithms of oppression (Noble, 2018), socio-technical artefacts (Benjamin, 2019) — as language for what is already felt. Hosseini's (2026) method of prompting GenAI and critically analyzing outputs is a replicable pedagogical entry point.
For students
High potential. Already doing affective critical work. Avoid rushing to resolution — the unease is epistemically productive. GenAI outputs should be approached "not [as] surprising, but as symptomatic of racialised and gendered logics" (Hosseini, 2026) embedded in training data across all output modalities.
The Informed Skeptic
Identifies bias in outputs; most common profile
Theoretical anchor
Can identify racial and gender skew in outputs — consistent with quantitative evidence (Cheong et al., 2024; Currie et al., 2024, 2025) — but frames it as a dataset problem rather than an expression of algorithmic coloniality (Mohamed et al., 2020). The systemic account is absent.
For educators
Move from "bias as glitch" to "bias as design." Use Benjamin's (2019, p. 59) argument that training datasets carry "the prejudices of the individuals who compiled them." Ask: why does a GenAI default encode particular assumptions about race, class, gender, or expertise?
For students
May believe that surface improvements will resolve the issue. Hosseini (2026) demonstrates directly that successive GenAI model versions produced aesthetically improved outputs while reproducing the same racial and gendered logic. The technical fix does not address colonial logics in the training data.
The Structural Analyst
Names AI harms systemically; connects to power
Theoretical anchor
Understands AI as a socio-technical artefact (Benjamin, 2019) shaped by Silicon Valley's role as "part of the United States, a global hegemon and a successor to European colonial powers" (Keshishi & Hosseini, 2023). Connects algorithmic coloniality (Mohamed et al., 2020) to concrete outputs.
For educators
Deepen from analysis to action. Introduce reparative description (Parry, 2023): how might geographers work with public image repositories to revise false past categorizations? Introduce Zembylas's (2023) strategies for "undoing the ethics of digital neocolonialism."
For students
May become frustrated that structural analysis does not translate into change. Channel into cross-disciplinary collaboration. Addressing problematic training data requires collective action and "relational approaches that emphasize the spatial and political contexts of algorithms" (Maalsen, 2023; Hosseini, 2026).
The Emerging Ally
Seeks solidarity; building shared critical vocabulary
Theoretical anchor
Transitional position between individual and collective consciousness (Freire). Recognizes that critique must be collective but lacks the structural analysis to ground it yet.
For educators
Facilitate cross-disciplinary collaboration explicitly. Addressing algorithmic harm requires breaking "disciplinary and departmental silos" (Hosseini, 2026; Maalsen, 2023). Connect emerging allies to existing coalitions and communities of practice doing this work.
For students
Motivated by justice but may lack the analytical vocabulary to sustain critique under institutional pressure. Pairing with theoretically grounded peers — including those with lived experience of the harms being analyzed — is more generative than educator-only support.
Structural
consciousness
AI as site of
coloniality & harm
Conscientized but Constrained
Sees the system; defers under institutional pressure
Theoretical anchor
Understands algorithmic coloniality and its harms but operates in institutional systems — curriculum, assessment, professional bodies — that have not caught up with the critique. Within many national contexts "there are nascent discussions on the ethical issues of using Gen AI technologies within tertiary education" (Hosseini, 2026).
For educators
Name the institutional lag explicitly. Developing "algorithmic literacy as part of wider digital literacy initiatives" (Kong et al., 2023; Zembylas, 2023; Hosseini, 2026) is a growing expectation — students can actively contribute to shaping it rather than waiting for institutions to catch up.
For students
Risk of internalizing structural constraint as personal inadequacy. The student's tension is not a sign of failure — it is evidence of structural contradictions that institutions have not yet resolved. Validate the critique while building pathways to act within and against institutional constraints.
The Critical Refuser
Refuses metaphorical framing; acts on structural critique
Theoretical anchor
Tuck & Yang (2012): decolonization is not a metaphor. Student refuses cosmetic diversity framings and demands structural change to what AI produces and whom it serves.
For educators
Support with Mohamed et al.'s (2020) practical recommendations: identifying sites of coloniality in AI systems, understanding where and how algorithms are made, engaging in reparative description (Parry, 2023), and developing local and national policy challenges to colonial algorithmic logics.
For students
May encounter resistance from colleagues who frame AI critique as technophobia. Documentation and publication — as Hosseini (2026) demonstrates — transforms resistant practice into sharable pedagogical resource. Connect to communities doing this work across disciplines.
The Critical Collaborator
Challenges AI's epistemic order; builds alternatives
Theoretical anchor
Actively participates in co-creating "instructional materials that transcend boundaries" (Hosseini, 2026). Draws on intersectionality (Crenshaw, 1991; Hill Collins, 2019) to hold race, gender, and class in simultaneous analysis rather than treating each as a separate problem.
For educators
Commission rather than assess. Meaningful critique of algorithmic coloniality requires centering those with lived expertise in the harms being analyzed — not as informants but as co-authors (Hosseini, 2026). Invite co-authorship, co-design, and co-delivery.
For students
Risk of co-option — being absorbed as institutional evidence of diversity without structural change. Hosseini's (2026) reflexive positioning models how genuine co-production differs from performative consultation. Support students to name and resist this distinction.
The Praxis Collective aspirational*
Reflection + action with others; pluriversal praxis
Theoretical anchor
Camacho Felix's (2025) decolonial imaginations and collective imagination. Mohamed et al.'s (2020) political coalitions. Benjamin's (2019) abolitionist tools for dismantling the New Jim Code in AI systems.
For educators
Collective praxis around GenAI requires institutional conditions: time, resource, partnership, and willingness to redistribute epistemic authority. It demands cross-disciplinary collaboration, reparative dataset work (Parry, 2023), and policy advocacy — none of which individual pedagogy alone can produce.
For students
Students here are co-researchers and co-educators. Hosseini (2026) models this directly: conducting experiments, publishing findings, and encouraging readers to replicate and extend the work with a critical eye. Sustain rather than assess.
← epistemic deference
collective / relational agency →
Movement across these axes is non-linear — individuals may hold multiple positions simultaneously across different contexts and knowledge domains
Theoretical grounding
Horizontal axis: Mohamed et al. (2020) — algorithmic coloniality; Noble (2018) — algorithms of oppression; Maalsen (2023) — algorithmic epistemologies  ·  Vertical axis: Benjamin (2019) — socio-technical artefacts encoding racial inequity; Zembylas (2023) — decolonial AI in HE; Quijano (2000) — coloniality of power; Camacho Felix (2025) — decolonial imaginations and collective action

This view reframes the framework for researchers engaging with generative AI across the research process. The two applied sections in each cell address the researcher's own practice and the dimension of epistemic accountability — a concept that holds together citational justice (the obligation to credit ideas and intellectual labour whether or not they appear in a formal reference list) and research integrity (the obligation to be transparent about the conditions, limitations, and role of AI in knowledge production). Together they ask: is this knowledge claim accountable — to its sources, its methods, and the communities it draws on?

Positions are not fixed. A researcher may occupy different cells across different phases of the research lifecycle, and across different relationships — with their own field, with collaborators, with communities whose knowledge they draw on.

Epistemic deference
AI as neutral oracle
Critical interrogation
Outputs questioned
Epistemic agency
Whose knowledge?
Collective / relational
Critique with others
Individualized
engagement
AI as personal tool,
neutral & apolitical
The Uncritical Receiver
Accepts AI-generated syntheses as authoritative; does not interrogate sources
Theoretical anchor
Algorithmic coloniality (Mohamed et al., 2020): researcher treats AI literature syntheses, methodology suggestions, and summaries as objective, without recognising that GenAI encodes dominant epistemological hierarchies in what it surfaces and what it buries.
For researchers
The most immediate risk is invisible citational erasure — AI synthesises ideas without naming their origins, and the researcher reproduces that synthesis without tracing it back. This is not plagiarism in the conventional sense; it is the structural absorption of others' intellectual labour without acknowledgment. Check: could you trace every conceptual move in an AI-assisted draft back to a named source?
Epistemic accountability
AI-generated text disproportionately surfaces and centres Western, English-language, and institutionally prestigious scholarship. Accepting this output without interrogation reproduces existing patterns of citational exclusion at scale — particularly for Indigenous, Global South, and community-based knowledge traditions. Whose ideas are being absorbed here, and are they being named?
The Cautious Pragmatist
Verifies factual accuracy; does not interrogate epistemological defaults
Theoretical anchor
The researcher fact-checks AI outputs but accepts their epistemological framing — which fields count as relevant, whose scholars are cited, what counts as evidence. Improved fluency in AI outputs does not mean the underlying knowledge hierarchies have shifted (Hosseini, 2026).
For researchers
Shift from "is this reference real?" to "whose scholarship does this AI treat as foundational?" Run the same research question through AI and check: which traditions, languages, and geographic contexts are centred in the output? Whose frameworks are presented as universal? This is the citational audit that fact-checking alone does not perform.
Epistemic accountability
The Cautious Pragmatist may catch hallucinated citations but will not notice that the scholars being cited are systematically drawn from particular traditions. AI reproduces the citational habits of its training data — which over-represents certain journals, languages, and institutional contexts. Active remediation requires deliberately seeking out and naming scholarship that AI sidelines.
The Epistemically Alert
Interrogates whose knowledge AI centres; actively traces silences
Theoretical anchor
Algorithmic coloniality (Mohamed et al., 2020): researcher recognises that AI embeds a dominant, Eurocentric worldview in what it treats as relevant, credible, and foundational. Connects to Maalsen's (2023) algorithmic epistemologies: AI does not just reflect knowledge hierarchies — it reproduces and legitimises them.
For researchers
Use AI-generated literature maps as a diagnostic, not a foundation. Where are the gaps? Which fields, languages, and traditions are absent? Supplement systematically rather than incidentally. Consider naming the epistemological limits of any AI-assisted literature review explicitly in your methodology — this is a form of reflexive transparency that strengthens rather than weakens the work.
Epistemic accountability
Epistemic alertness is the precondition for citational justice. It is not enough to add diversity to a reference list; the question is whether the conceptual architecture of the work — its framing, its definitions, its theoretical moves — draws on a genuinely plural set of traditions. This researcher is positioned to ask that question; the task is to act on it consistently.
The Isolated Disruptor
Critiques AI's epistemic defaults alone; lacks relational infrastructure
Theoretical anchor
Individual critique of algorithmic coloniality, however rigorous, cannot change proprietary training datasets or the citational norms of major journals (Amoore et al., 2024). Mohamed et al. (2020): structural change requires political coalitions, not individual scholarly positioning.
For researchers
The risk here is that critique becomes a scholarly identity rather than a lever for change. Publishing papers that name AI's citational failures is necessary but not sufficient if those papers circulate only within already-convinced communities. The question is: what collective structures — journal policies, funder requirements, disciplinary norms — could be shifted, and who are the coalitions that could shift them?
Epistemic accountability
Citational justice enacted alone — by one researcher choosing to diversify their own reference lists — is valuable but limited. It does not change the structural conditions that produce citational inequality: impact factor systems, language hierarchies in publishing, paywalled access to scholarship from the Global South. Connecting individual practice to structural advocacy is the work this position points toward.
Partial structural
awareness
Senses bias or harm,
lacks systemic account
The Uneasy Adapter
Senses something off in AI-assisted research; lacks conceptual language
Theoretical anchor
Pre-conceptual awareness of algorithmic harm: the researcher senses that AI-assisted literature searches, writing suggestions, or methodology outputs are skewed — but lacks the vocabulary to name what the skew is or where it comes from (Fricker, 2007, hermeneutical injustice).
For researchers
The unease is epistemically productive — it is evidence that the researcher is already doing critical work at an affective level. The conceptual vocabulary that names this unease includes algorithmic coloniality (Mohamed et al., 2020), citational injustice (Ahmed, 2017), and epistemic extractivism — the process by which AI absorbs and repackages knowledge without crediting its origins. Naming the problem is the first step to acting on it.
Epistemic accountability
The sense that "something is missing" from an AI-generated literature map is often precisely citational: the scholars whose work would reframe the question are absent. This researcher is close to articulating that absence — what is needed is the vocabulary to name which traditions are missing and why their exclusion is not accidental.
The Informed Skeptic
Identifies specific AI biases; does not yet connect to structural account
Theoretical anchor
Can identify specific instances where AI outputs reflect racial, gendered, or disciplinary bias — but frames this as a data quality problem rather than an expression of algorithmic coloniality (Mohamed et al., 2020). Instance-level critique without structural account.
For researchers
The next move is from "this AI output is biased" to "this AI output reflects a system trained on scholarship that was already biased toward particular traditions, and my use of it without intervention reproduces that bias in my work." Benjamin's (2019) account of how socio-technical systems encode the assumptions of their makers provides the structural bridge this position needs.
Epistemic accountability
The Informed Skeptic notices citational gaps but treats them as omissions to be corrected individually rather than as expressions of structural inequality. The shift required is recognising that AI's citational habits are not random: they reflect and reinforce the hierarchies of academic publishing — impact factors, language, geography, institutional prestige — that already disadvantage particular scholars and traditions.
The Structural Analyst
Accounts for AI's epistemic harms systemically in research practice
Theoretical anchor
Understands AI as a socio-technical artefact (Benjamin, 2019) whose outputs in research contexts — literature syntheses, methodology suggestions, analytical framings — encode the colonial hierarchies of their training data. Connects Mohamed et al.'s (2020) algorithmic coloniality directly to their own methodological choices.
For researchers
Analysis must connect to action in the research process itself: not just naming AI's structural harms in a methods section, but making methodological choices that actively counter them — using AI as one input among many, supplementing systematically from excluded traditions, and being explicit in publications about what AI cannot see. Describing and contextualising AI outputs in ways that make their structural conditions visible to others offers a model for how this transparency can be enacted.
Epistemic accountability
Structural analysis of AI's citational defaults should translate into active citational practice: naming the scholars whose ideas inform the work even when they are not formally cited, acknowledging intellectual debts to communities and interlocutors, and refusing the convention that only peer-reviewed, institutionally affiliated sources count as citable knowledge. Sara Ahmed's (2017) citational politics offers a principled framework for this.
The Emerging Ally
Building shared critical research practice; seeking community
Theoretical anchor
Transitional position between individual and collective critical consciousness (Freire, 1970). Recognises that AI's structural harms cannot be addressed by individual researchers alone — but has not yet developed the theoretical grounding or the relational infrastructure to act collectively.
For researchers
The institutional structures for collective critical research practice around AI are still nascent — but they exist: research networks, journal special issues, funding calls, and disciplinary working groups focused on AI ethics and decolonisation. Connecting to these structures — research networks, disciplinary working groups, and journal special issues focused on AI ethics and decolonisation — is more generative than developing critique in isolation.
Epistemic accountability
Building shared critical citational practice is more effective than individual reform. Research groups that collectively commit to citational justice — explicitly discussing whose scholarship they are drawing on, whose they are missing, and why — create the conditions for structural change that individual practice cannot. This researcher is positioned to initiate those conversations.
Structural
consciousness
AI as site of
coloniality & harm
Conscientized but Constrained
Understands AI's structural harms; institutional pressures limit action
Theoretical anchor
Understands algorithmic coloniality and its research implications but operates within institutional structures — REF metrics, funder requirements, journal conventions, impact factor pressures — that have not caught up with the critique. The constraint is structural, not personal.
For researchers
Identify the specific points in the research lifecycle where structural consciousness can be enacted without institutional penalty — and the points where it cannot yet. Methodology transparency, supplementary citational notes, and collaborative publications are lower-risk entry points. The longer-term task is building the disciplinary arguments that shift what counts as rigorous and responsible AI use in research.
Epistemic accountability
The pressure to cite high-impact, English-language, institutionally prestigious scholarship is real and career-consequential. Citational justice in this position requires identifying which aspects of that pressure can be resisted now — in acknowledgments sections, in working papers, in teaching materials — and building the disciplinary case for broader reform over time.
The Critical Refuser
Refuses AI's epistemic defaults; enacts structural critique in research practice
Theoretical anchor
Tuck & Yang (2012): decolonization is not a metaphor. The researcher refuses to treat AI as a neutral research tool and refuses the cosmetic diversity framings — diverse prompt outputs, diverse research team images — that leave colonial data structures intact.
For researchers
Refusal in research practice is not disengagement from AI but a theoretically grounded decision about what it will and will not be used for. Mohamed et al.'s (2020) practical framework — identifying sites of coloniality, understanding where algorithms are made, engaging in reparative description — provides the action vocabulary this position needs. Document and publish these choices: refusal is more powerful as shared practice than as individual stance.
Epistemic accountability
Refusing AI's citational defaults means refusing to let AI determine whose scholarship is foundational to the work. It means actively tracing ideas to their origins — including unpublished work, conference presentations, community knowledge, and conversations — and naming those origins in the text, not just in the references. Ahmed's (2017) practice of making the politics of citation explicit is the model here.
The Critical Collaborator
Co-produces research that challenges AI's epistemic order; centers lived expertise
Theoretical anchor
Draws on intersectionality (Crenshaw, 1991; Hill Collins, 2019) to hold race, gender, and class in simultaneous analysis. Hosseini (2026) models this directly: centering colleagues with lived expertise in the harms being analyzed — not as data sources or acknowledgment entries but as co-authors and co-thinkers whose intellectual contributions shape the work.
For researchers
The question this position raises is not just who is on the research team but whose ideas are structuring the research — and whether that is reflected in authorship, acknowledgment, and citation. Co-production is not adding diverse voices to an existing framework; it is allowing those voices to reshape the framework itself. This is the distinction between consultation and genuine epistemic collaboration.
Epistemic accountability
Citational justice at this position means that intellectual contributions shape authorship decisions — not just acknowledgment entries. It also means naming the ideas of collaborators, interlocutors, and community members in the text of the work, even where formal co-authorship is not possible. The goal is to make the relational and collective character of knowledge production visible, against the convention of the solo scholarly voice.
The Praxis Collective aspirational*
Collective research action; pluriversal knowledge production
Theoretical anchor
Camacho Felix's (2025) decolonial imaginations: the collective envisioning and construction of research practices that do not reproduce colonial knowledge hierarchies. Freire's (1970) praxis: reflection and action undertaken with others, not on behalf of them. The goal is not one critical research community but the recognition that multiple knowledge traditions have equal standing (Mignolo & Walsh, 2018).
For researchers
Collective praxis in research requires institutional conditions that most researchers do not currently have: protected time for collaborative methodological reflection, funding structures that recognise community knowledge, and publishing conventions that can accommodate collective and non-Western authorship. Building those conditions is itself a research and advocacy task — one that this position is equipped to pursue.
Epistemic accountability
At this position, citational justice is not a corrective add-on but constitutive of the research itself. The work is designed from the outset to make its knowledge relations visible: who contributed what, whose frameworks are being used and from where, what intellectual debts are owed and to whom. This is scholarship that models the knowledge relations it advocates for — pluriversal, relational, and transparent about the conditions of its own production.
← epistemic deference
collective / relational agency →
Movement across these axes is non-linear — researchers may hold different positions across different phases of the research lifecycle and different collaborative relationships
Theoretical grounding
Horizontal axis: Mohamed et al. (2020) — algorithmic coloniality; Noble (2018) — algorithms of oppression; Maalsen (2023) — algorithmic epistemologies  ·  Vertical axis: Benjamin (2019) — socio-technical artefacts; Zembylas (2023) — decolonial AI in HE; Quijano (2000) — coloniality of power; Camacho Felix (2025) — decolonial imaginations  ·  Epistemic accountability: Ahmed (2017) — citational politics and naming intellectual genealogies; Fricker (2007) — hermeneutical injustice as structural condition of not having vocabulary to name epistemic harm; Tuck & Yang (2012) — decolonization as structural and political; UK Research Integrity Office / Singapore Statement (2010) — research integrity as transparency about conditions of knowledge production

This view adds epistemic accountability as a fourth dimension alongside the original three sections. Epistemic accountability holds together two obligations that AI use makes newly urgent: citational justice — recognising whose ideas are absorbed through AI-mediated synthesis and naming intellectual contributions that fall outside formal citation conventions — and research integrity — being transparent about the conditions, limitations, and role of AI in knowledge production. Neither subordinates the other. Citational justice asks whose labour is acknowledged; research integrity asks whether the conditions of production are honestly disclosed. Together they ask: is this knowledge claim accountable — to its sources, its methods, and the communities it draws on?

This view preserves the educational framing (theoretical anchor, for educators, for students) and adds epistemic accountability as a cross-cutting dimension that applies regardless of whether the user is a researcher, educator, or student. AI use in educational contexts raises the same questions of intellectual credit, attribution transparency, and relational accountability as it does in formal research.

Epistemic deference
AI as neutral oracle
Critical interrogation
Outputs questioned
Epistemic agency
Whose knowledge?
Collective / relational
Critique with others
Individualized
engagement
AI as personal tool,
neutral & apolitical
The Uncritical Receiver
Accepts outputs; AI naturalized as neutral knowledge source
Theoretical anchor
Algorithmic coloniality (Mohamed et al., 2020): treats AI outputs as objective, without recognising that GenAI encodes dominant cultural assumptions shaped by historically racist and sexist training data (Benjamin, 2019).
For educators
Make the assumption of neutrality visible. Ask: whose perspective does a GenAI output reflect by default? Show how neutral prompts reproduce skewed cultural defaults across output types (Day & Esson, 2025; Hosseini, 2026).
For students
AI outputs feel "natural" rather than constructed. Socio-technical artefacts are shaped by the values of those who built them (Benjamin, 2019). Students need a framework to see this, not just permission to question.
Epistemic accountability
At this position, ideas absorbed through AI are unlikely to be traced to their origins at all. This is invisible intellectual debt — the uncritical receiver inherits AI's citational erasures without knowing it. The first step is simply asking: where did this idea come from, and who first articulated it?
The Cautious Pragmatist
Checks outputs; AI still framed as neutral instrument
Theoretical anchor
Audits outputs for factual errors but not cultural assumptions. Improved outputs do not mean the underlying biases have been addressed (Hosseini, 2026).
For educators
Shift from "is this accurate?" to "whose accuracy?" Ask students to run the same question through AI and identify which scholarly traditions dominate the output and which are absent.
For students
Prompt refinement "would not address the underlying biases within the datasets themselves" (Hosseini, 2026). Move from refining inputs to interrogating the colonial logics embedded in training data.
Epistemic accountability
Verifying that citations are real is not the same as asking whose scholarship the AI treats as foundational. The Cautious Pragmatist may catch hallucinated references but miss the systematic over-representation of Western, English-language scholarship. Active remediation means deliberately supplementing AI outputs from excluded traditions and naming those sources.
The Epistemically Alert
Interrogates whose knowledge is centered; notices silences
Theoretical anchor
Algorithmic coloniality (Mohamed et al., 2020): AI embeds a dominant, Eurocentric worldview in what it treats as relevant, credible, and foundational (Noble, 2018; Maalsen, 2023).
For educators
Move from naming bias to interrogating its origin. Use Quijano's (2000) coloniality of power to show that AI's defaults are not errors but expressions of colonial hierarchies in training data.
For students
May feel isolated when institutional AI guidance frames the issue as technical. Wilby & Esson's (2024) call for "capabilities, caveats, and criticality" provides legitimizing language. Connect to communities doing this work.
Epistemic accountability
Epistemic alertness is the precondition for citational justice. This position asks not just "whose knowledge is missing?" but "whose intellectual labour has been absorbed without credit?" — including ideas circulating in communities, conferences, and conversations that predate their appearance in peer-reviewed form.
The Isolated Disruptor
Critiques AI alone; change without solidarity
Theoretical anchor
Individual critique cannot address structural problems in proprietary datasets (Amoore et al., 2024). Structural change requires political coalitions, not individual actors (Mohamed et al., 2020).
For educators
Connect to collective and cross-disciplinary action. Individual insight without structural leverage changes nothing about the datasets producing harmful outputs (Hosseini, 2026; Maalsen, 2023).
For students
Risk of cynicism when individual critique runs up against inaccessible, proprietary systems. Students need community and strategy, not just analysis (Hosseini, 2026).
Epistemic accountability
Diversifying one's own reference list in isolation does not change the structural conditions that produce citational inequality. Collective citational politics — research groups, journals, funders — is what changes norms. This position points toward the need for that collective, even if it has not yet been built.
Partial structural
awareness
Senses bias or harm,
lacks systemic account
The Uneasy Adapter
Senses something wrong; lacks language to name it
Theoretical anchor
Pre-conceptual awareness of algorithmic harm: senses skew but lacks vocabulary to name it — Fricker's (2007) hermeneutical injustice at the level of AI engagement.
For educators
Threshold moment. Offer concepts — algorithmic coloniality (Mohamed et al., 2020), algorithms of oppression (Noble, 2018) — as language for what is already felt (Hosseini, 2026).
For students
High potential. The unease is epistemically productive. GenAI outputs are "not surprising, but symptomatic of racialised and gendered logics" (Hosseini, 2026). Avoid rushing to resolution.
Epistemic accountability
The sense that something is missing from AI outputs is often precisely citational — the scholars who would reframe the question are absent. This position is close to naming that absence; the vocabulary of citational injustice (Ahmed, 2017) and epistemic extractivism can give it form.
The Informed Skeptic
Identifies bias in outputs; lacks structural account
Theoretical anchor
Identifies racial and gender skew (Cheong et al., 2024; Currie et al., 2024) but frames it as a dataset problem rather than algorithmic coloniality (Mohamed et al., 2020).
For educators
Move from "bias as glitch" to "bias as design." Training datasets carry "the prejudices of the individuals who compiled them" (Benjamin, 2019, p. 59). Ask: why does GenAI encode particular assumptions about race, class, and expertise?
For students
Surface improvements do not resolve colonial logics. Hosseini (2026) demonstrates that successive model versions reproduced the same racial and gendered logic despite aesthetic improvement.
Epistemic accountability
The Informed Skeptic notices citational gaps but treats them as individual omissions rather than structural patterns. The shift required is recognising that AI's citational habits reflect publishing hierarchies — impact factors, language, geography, prestige — that systematically disadvantage certain scholars. Correction requires structural awareness, not just addition.
The Structural Analyst
Names AI harms systemically; connects to power
Theoretical anchor
Understands AI as a socio-technical artefact (Benjamin, 2019) shaped by colonial power structures (Keshishi & Hosseini, 2023). Connects algorithmic coloniality (Mohamed et al., 2020) to concrete outputs.
For educators
Deepen from analysis to action. Introduce reparative description (Parry, 2023) and Zembylas's (2023) strategies for "undoing the ethics of digital neocolonialism."
For students
May be frustrated that structural analysis does not translate into change. Channel into cross-disciplinary collaboration (Maalsen, 2023; Hosseini, 2026). Analysis without community and outlet risks paralysis.
Epistemic accountability
Structural analysis should translate into active citational practice: naming scholars whose ideas inform the work even outside formal citation, acknowledging intellectual debts to communities, and refusing the convention that only peer-reviewed institutional sources count as knowledge. Ahmed's (2017) citational politics is the principled framework here.
The Emerging Ally
Seeks solidarity; building shared critical vocabulary
Theoretical anchor
Transitional between individual and collective consciousness (Freire). Recognises critique must be collective but lacks structural grounding and relational infrastructure yet.
For educators
Facilitate cross-disciplinary collaboration explicitly. Addressing algorithmic harm requires breaking disciplinary silos (Hosseini, 2026; Maalsen, 2023). Connect to existing coalitions and communities of practice.
For students
Motivated by justice but may lack analytical vocabulary to sustain critique under institutional pressure. Pairing with theoretically grounded peers — including those with lived experience — is more generative than educator-only support.
Epistemic accountability
Building shared critical citational practice within a group — explicitly discussing whose scholarship is being drawn on and whose is absent — creates conditions for structural change that individual practice cannot. This researcher is positioned to initiate those conversations and model collective accountability in citation.
Structural
consciousness
AI as site of
coloniality & harm
Conscientized but Constrained
Sees the system; defers under institutional pressure
Theoretical anchor
Understands algorithmic coloniality and its harms but operates within institutional systems that have not caught up with the critique (Hosseini, 2026). The constraint is structural, not personal.
For educators
Name the institutional lag explicitly. "Algorithmic literacy as part of wider digital literacy initiatives" is a growing expectation (Kong et al., 2023; Zembylas, 2023) — students can contribute to shaping it rather than waiting for institutions to catch up.
For students
The tension is not personal failure — it is evidence of structural contradictions institutions have not resolved. Validate the critique while building pathways to act within and against institutional constraints.
Epistemic accountability
The pressure to cite high-impact, English-language scholarship is real and career-consequential. Citational justice in this position means identifying where it can be enacted now — in acknowledgments, working papers, teaching materials — and building the disciplinary argument for broader reform over time, without waiting for permission.
The Critical Refuser
Refuses metaphorical framing; acts on structural critique
Theoretical anchor
Tuck & Yang (2012): decolonization is not a metaphor. Refuses cosmetic diversity framings and demands structural change to what AI produces and whom it serves.
For educators
Support with Mohamed et al.'s (2020) practical recommendations: identifying sites of coloniality, engaging in reparative description (Parry, 2023), and developing policy challenges to colonial algorithmic logics.
For students
May encounter resistance framing AI critique as technophobia. Documentation and publication transforms resistant practice into shareable resource (Hosseini, 2026). Connect to communities doing this work across disciplines.
Epistemic accountability
Refusing AI's citational defaults means actively tracing ideas to their origins — including unpublished work, conference presentations, community knowledge, and conversations — and naming those origins in the text, not just the reference list. The politics of citation are made explicit, not hidden in a bibliography (Ahmed, 2017).
The Critical Collaborator
Challenges AI's epistemic order; builds alternatives with others
Theoretical anchor
Intersectionality (Crenshaw, 1991; Hill Collins, 2019). Hosseini (2026): centering colleagues with lived expertise as co-authors and co-thinkers whose contributions shape the work, not merely validate it.
For educators
Commission rather than assess. Centering lived expertise means co-authorship, co-design, and co-delivery — not consultation. This student's contribution should shape pedagogy, not merely illustrate it (Hosseini, 2026).
For students
Risk of co-option — absorbed as institutional evidence of diversity without structural change. Hosseini's (2026) reflexive positioning models how genuine co-production differs from performative consultation. Support students to name and resist this distinction.
Epistemic accountability
At this position, citational justice means that intellectual contributions shape authorship — not just acknowledgment entries. It means naming collaborators', interlocutors', and community members' ideas in the body of the work. The relational and collective character of knowledge production is made visible, against the convention of the solo scholarly voice.
The Praxis Collective aspirational*
Reflection + action with others; pluriversal praxis
Theoretical anchor
Camacho Felix (2025): decolonial imaginations and collective action. Mohamed et al. (2020): political coalitions. Freire (1970): praxis undertaken with others, not on behalf of them. Mignolo & Walsh (2018): pluriversality — multiple knowledge traditions with equal standing.
For educators
Collective praxis requires institutional conditions: time, resource, partnership, willingness to redistribute epistemic authority, reparative dataset work (Parry, 2023), and policy advocacy. Educators must build the structures, not just model the position.
For students
Students here are co-researchers and co-educators. Hosseini (2026) models this: conducting experiments, publishing findings, encouraging replication. The goal is ongoing collective action that outlasts the course.
Epistemic accountability
At this position, citational justice is constitutive of the work — not an add-on. The research is designed from the outset to make its knowledge relations visible: who contributed what, whose frameworks are being used and from where, what intellectual debts are owed and to whom. This is scholarship that enacts the knowledge relations it advocates for — pluriversal, relational, and transparent about the conditions of its own production.
← epistemic deference
collective / relational agency →
Movement across these axes is non-linear — individuals may hold multiple positions simultaneously across different contexts, relationships, and knowledge domains
Theoretical grounding
Horizontal axis: Mohamed et al. (2020); Noble (2018); Maalsen (2023)  ·  Vertical axis: Benjamin (2019); Zembylas (2023); Quijano (2000); Camacho Felix (2025)  ·  Epistemic accountability: Ahmed, S. (2017). Living a Feminist Life. Duke University Press — the practice of naming intellectual genealogies explicitly in the text, including debts outside formal citation conventions. Epistemic accountability extends this to research integrity: transparent disclosure of AI use, its limitations, and the structural conditions that shaped what could be known — following the Singapore Statement on Research Integrity (2010) and Fricker's (2007) account of hermeneutical injustice as the structural absence of vocabulary to name epistemic harm.

This view maps AI engagement across the research lifecycle, tracing how epistemic posture — from deference to collective agency — manifests differently at each phase of research practice. Each phase carries distinct risks and possibilities: the literature and framing phase is where AI most aggressively shapes what counts as relevant scholarship; methodology and design is where colonial defaults in AI recommendations become embedded in the research architecture; data and evidence is where AI's training hierarchies most directly affect what gets surfaced and what gets buried; analysis and interpretation is where AI can flatten the conceptual moves that distinguish critical from descriptive work; writing and synthesis is where citational erasure is most invisible; and publication and dissemination is where structural change is most possible — and most resisted.

The epistemic accountability dimension (gold) runs through all phases but intensifies toward dissemination. It addresses two obligations simultaneously: citational justice — whose intellectual labour is acknowledged, including named authors, unnamed collaborators, community knowledge, conversations, and ideas that circulate before they are published — and research integrity — transparent disclosure of how AI was used, what it could not see, and what structural conditions shaped the knowledge produced. Both are present at every phase; the balance between them shifts as the research progresses.

Epistemic deference
AI as oracle
Critical interrogation
Outputs questioned
Epistemic agency
Whose knowledge?
Collective / relational
Critique with others
1. Literature & framing
Identifying what is known and from where

The phase where AI most aggressively determines whose scholarship counts as foundational. Epistemic deference here shapes the entire conceptual architecture of the research.

Delegates framing to AI
Uses AI-generated literature maps as the basis for research framing without interrogating which traditions, languages, or epistemological positions are absent from the output.
Epistemic accountability
Inherits AI's citational exclusions wholesale. Scholars from the Global South, Indigenous traditions, and community-based knowledge are systematically absent — and this absence shapes what the research can ask and how it frames its answers.
Fact-checks; accepts framing
Verifies that AI-cited sources are real and accurate but accepts the epistemological frame AI provides — which fields count as relevant, whose scholars are foundational, what counts as a gap in the literature.
Epistemic accountability
Catches hallucinated references but misses systematic citational bias. The canon AI constructs is accepted as the canon; active supplementation from excluded traditions requires a deliberate second pass.
Interrogates the canon AI constructs
Uses AI-generated literature maps as a diagnostic: where are the gaps? Which languages, disciplines, and geographic traditions are absent? Treats AI output as a partial and interested map, not a neutral survey of the field.
Epistemic accountability
Actively seeks out scholarship that AI sidelines. Names these sources explicitly in the literature review, including discussion of why they were absent from AI outputs — making the politics of knowledge production visible in the text itself (Ahmed, 2017).
Co-constructs the literature frame
Works with collaborators, communities, and interlocutors to identify what counts as relevant literature — rather than delegating that determination to AI. AI is one input among many, weighted by a collectively agreed epistemological framework.
Epistemic accountability
The literature frame itself reflects a collective decision about whose knowledge matters. Intellectual contributions from collaborators and communities that fall outside formal publication are named in the text, not just in acknowledgments.
2. Methodology & design
How the research will be conducted and why

AI methodology recommendations embed epistemological defaults — which methods count as rigorous, whose frameworks are treated as standard — that shape what the research can find before data collection begins.

Follows AI methodology suggestions
Treats AI-recommended methods as neutral best practice, without interrogating which epistemological tradition they come from or whose research contexts they were designed for.
Epistemic accountability
Methodology chapters that draw on AI-recommended frameworks without attribution embed intellectual debts that are never acknowledged. Which scholars developed the approaches being used, and are they named?
Questions specific method recommendations
Identifies when AI-recommended methods seem ill-suited to the research context, population, or question — but does not yet connect this to the colonial and epistemological conditions that produced those defaults.
Epistemic accountability
Begins to notice that the methodological literature AI surfaces over-represents particular contexts and traditions. Starts to supplement deliberately, but may not yet name the structural reasons for the gap.
Designs methodology against AI's defaults
Explicitly asks: what epistemological tradition does this AI-recommended method come from, and is it appropriate for this research? Draws on methodological frameworks from outside the defaults AI provides — including participatory, decolonial, and community-based approaches.
Epistemic accountability
Names the scholars who developed the methodological approaches being used, particularly where those scholars come from traditions AI sidelines. Methodological transparency includes an account of what AI recommended and why those recommendations were departed from.
Co-designs methodology with communities
Methodology is co-designed with research participants, communities, or collaborators rather than determined by the researcher alone. AI may be consulted but its suggestions are filtered through a collectively agreed epistemological framework.
Epistemic accountability
Methodological contributions from community collaborators are attributed in the methods section, not just the acknowledgments. Co-design is named as intellectual contribution, not just procedural participation.
3. Data & evidence
Gathering, generating, and appraising material

AI's training hierarchies directly affect what gets surfaced and what gets buried. At this phase, algorithmic coloniality is most materially consequential — shaping whose experiences, voices, and data are treated as valid evidence.

Accepts AI-mediated data as representative
Treats AI-generated, AI-retrieved, or AI-summarised data as representative of the phenomenon being studied, without interrogating whose experiences and knowledge are encoded in the training data.
Epistemic accountability
Data generated or retrieved through AI carries the citational exclusions of its training data. Whose voices, experiences, and knowledge are structurally absent from what AI can find or generate — and how does that shape what the research can claim?
Supplements AI data with additional sources
Recognises that AI-mediated data has gaps and actively supplements, but frames the gaps as practical limitations rather than expressions of algorithmic coloniality.
Epistemic accountability
Supplementary sources are added but their origins may not be named or their structural exclusion from AI outputs explained. The gap between AI data and supplementary data is a citational and political gap, not just an empirical one.
Interrogates what AI cannot see
Treats AI's data limitations as evidence of algorithmic coloniality in action, not as a technical problem. Asks systematically: what kinds of evidence, experience, and knowledge are structurally excluded from what AI can retrieve or generate, and why?
Epistemic accountability
The sources AI cannot see are named and sought out. Evidence from community archives, oral traditions, unpublished reports, and non-English sources is actively included and attributed. The process of retrieval is documented in the methods.
Generates data with communities, not about them
Data is generated through relational and participatory processes in which communities are co-producers of evidence rather than subjects of AI-mediated analysis. AI tools are used, if at all, within a framework agreed with research partners.
Epistemic accountability
Community-generated evidence is attributed to its sources. Participants and partners who shape data collection and interpretation are named in the research — not anonymised by default, but with agency over how their contributions appear in the final work.
4. Analysis & interpretation
Making sense of what has been found

AI analysis flattens complexity and forecloses interpretive possibilities. The conceptual moves that distinguish critical from descriptive research are most at risk here — as AI tends toward synthesis, consensus, and the resolution of tension.

Accepts AI interpretive frames
Uses AI-generated summaries, thematic analyses, and interpretive framings as the basis for findings, without interrogating the epistemological assumptions that structure them.
Epistemic accountability
Interpretive frameworks absorbed from AI without interrogation carry unattributed intellectual debts. Which theoretical traditions are shaping the analysis — and are their originators named?
Checks AI analysis against own reading
Uses AI analysis as a first pass but checks key interpretations against primary material. Identifies when AI over-simplifies or resolves tensions the researcher considers important — but without a structural account of why AI does this.
Epistemic accountability
Begins to notice that AI analysis tends to foreground certain theoretical traditions and background others. May add citations from excluded traditions but does not yet name why they were excluded from AI outputs.
Refuses AI's interpretive foreclosures
Treats AI's tendency to synthesise and resolve as an epistemological problem, not a feature. Uses AI analysis as a diagnostic of what dominant frameworks would say, then deliberately works against those framings using theoretical resources AI sidelines.
Epistemic accountability
The theoretical frameworks driving the analysis are named and traced — including frameworks from traditions AI sidelines. Interpretive choices are documented and their intellectual origins acknowledged, including where those origins are conversations and collaborations rather than published texts.
Interprets collectively and relationally
Analysis is developed through dialogue — with co-researchers, community partners, and interlocutors — rather than produced by the researcher alone with AI assistance. AI may contribute but does not determine the interpretive frame.
Epistemic accountability
Interpretive contributions from collaborators are attributed in the analysis section, not just in acknowledgments. The collectively produced character of the analysis is made visible — including the names and perspectives of those who shaped how the data was read.
5. Writing & synthesis
Producing the scholarly text

Citational erasure is most invisible at this phase. AI-assisted writing absorbs ideas, framings, and formulations without tracing them to their origins — and researchers may not notice because the output reads as their own.

Uses AI-generated text directly
Incorporates AI-drafted sections into the manuscript with minimal modification, accepting the epistemological framing, conceptual vocabulary, and citational choices embedded in the output.
Epistemic accountability
AI-generated text carries absorbed intellectual debts that are invisible in the output. The ideas, formulations, and framings in AI writing originated somewhere — in scholarship, in discourse, in communities — and those origins are erased by default. This is the deepest citational risk in AI-assisted research.
Edits AI drafts; adds own voice
Uses AI drafts as scaffolding but revises substantially, adding citations, correcting framing, and introducing theoretical moves AI did not make. The epistemological defaults in AI drafts are partially but not systematically interrogated.
Epistemic accountability
Adding citations to an AI draft is not the same as tracing the intellectual genealogy of the ideas in the text. The question is not only whether references are accurate but whether the scholars whose thinking shaped the argument — directly or indirectly — are named.
Writes against AI's framings
Uses AI drafts as a foil: a representation of what the dominant epistemological position would say, against which the researcher's own critical contribution is defined. AI may assist with structure or clarity but does not determine the conceptual moves.
Epistemic accountability
Makes the politics of citation explicit in the text, following Ahmed (2017): naming whose work has shaped the argument, including works that would not appear in a standard literature review, and acknowledging intellectual debts to collaborators and interlocutors who are not formal co-authors.
Writes with others; collective voice
The manuscript is produced through collective writing processes that make the relational character of the work visible — co-authored with communities, collaborators, and interlocutors, with AI as one tool among many rather than a ghostwriter.
Epistemic accountability
The text names all those whose intellectual contributions shaped it — whether or not they are listed as authors. Acknowledges ideas that emerged in conversation, community engagement, or collaborative processes that predate the formal research. The acknowledgments section does substantive intellectual work, not just courtesy.
6. Publication & dissemination
Sharing findings and building knowledge

The phase where structural change is most possible and most resisted. Publishing choices — venue, format, access, co-authorship conventions — are themselves political acts that reproduce or challenge the knowledge hierarchies AI encodes.

Publishes within existing norms
Targets high-impact journals, follows conventional authorship and citation practices, uses AI-assisted writing to meet volume and speed demands without questioning the structural conditions those demands reproduce.
Epistemic accountability
Publishing in high-impact, subscription-access venues while using AI-generated text that absorbs without crediting scholarship from the Global South enacts a double extraction: taking knowledge and returning it inaccessibly, with origins erased.
Questions specific publication norms
Aware that publication conventions — impact factors, language requirements, authorship rules — reproduce inequalities, but navigates them pragmatically rather than challenging them structurally.
Epistemic accountability
May choose open-access venues or add supplementary attribution notes, but does not yet connect these choices to a structural account of how publishing conventions reproduce the same hierarchies as AI's training data.
Makes publishing choices politically
Treats publication venue, access model, co-authorship conventions, and dissemination format as political decisions — not just career calculations. Uses Hosseini's (2026) model of publishing findings that encourage replication and community engagement rather than gatekeeping.
Epistemic accountability
Names the intellectual genealogy of the work in the published text — not just in the reference list but in the body of the argument. Advocates within disciplinary and journal contexts for citational norms that recognise non-Western, non-English, and community-based scholarship. Documents and publishes the citational politics of the research itself.
Disseminates with communities, not to them
Findings are shared with and through the communities whose knowledge and experience informed the research — before, not after, academic publication where possible. Dissemination is designed as part of the research, not an afterthought. Multiple formats, languages, and venues are used to reach the communities the work is accountable to.
Epistemic accountability
Publications credit all intellectual contributions — including those of community partners and interlocutors — in formats those contributors can access and share. The goal is not only to acknowledge intellectual debt but to return knowledge to its sources in forms that are useful, accessible, and accountable. This is the fullest enactment of epistemic accountability — pluriversal, relational, and structurally honest about the conditions of knowledge production.
The lifecycle view maps positions available at each phase — not a required sequence. Researchers will occupy different positions at different phases, and movement is non-linear. The epistemic accountability dimension (gold) is present throughout but intensifies toward publication, where structural inequalities in knowledge recognition are most consequential and most addressable.
Theoretical grounding — lifecycle view
Research lifecycle framing: Mohamed et al. (2020) — algorithmic coloniality across research practice; Maalsen (2023) — algorithmic epistemologies in geographic and social research  ·  Epistemic accountability: Ahmed, S. (2017) — citational politics and naming intellectual genealogies; Singapore Statement on Research Integrity (2010) — transparency about conditions and limitations of knowledge production; Fricker (2007) — hermeneutical injustice as structural absence of vocabulary to name epistemic harm; UK Research Integrity Office guidance on AI use in research (2024) — disclosure of AI role as a dimension of methodological honesty  ·  Relational knowledge production: Camacho Felix (2025) — decolonial imaginations; Tuck & Yang (2012) — decolonization as structural and collective; Hosseini (2026) — reflexive positioning and centering of lived expertise in research
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Decolonizing education, Pedagogy Dustin Hosseini Decolonizing education, Pedagogy Dustin Hosseini

Higher Education Beyond 2030: Principles, pedagogy, and the people we keep leaving out

In mid-late March 2026, I attended a session exploring the future of higher education, structured around the recently published UNESCO roadmap Transforming Higher Education: Global Collaboration on Visioning and Action (UNESCO, 2026). The room brought together academics at every career stage, from doctoral researchers to full professors, and what struck me most was not any single argument made but the collective mood: a genuine appetite for transformation sitting alongside a sober recognition of how wide the gap remains between the sector's stated values and its daily practice.

I am both a doctoral student and a university staff member, and I experience that gap from both sides simultaneously. This post is a reflection on that session and on the roadmap itself. A companion piece follows, which takes the same arguments and grounds them in the specific material conditions of Glasgow, where I work and study. What I want to do here is make the principled case about digital access, pedagogy, and whose knowledge counts, and be direct about what that case asks of educators, researchers, and policymakers.

The visual from UNESCO (2026) uses a ribbon/flow diagram to map a transformation from current problems in higher education (left side) toward desired future states (right side):

The visual from UNESCO (2026) uses a ribbon/flow diagram to map a transformation from current problems in higher education (left side) toward desired future states (right side):

Current challenges (left): exclusion and scarcity, narrow programme focus, disciplinary siloes, hierarchical and fragmented structures, abstract and unanchored learning, traditional pedagogy, and disconnection from local economies.

Transformed vision (right): active learning, flexible and harmonised systems, openness and inclusion, economic opportunity and just transition, lifelong learning orientation, engaged and relevant curricula, and holistic study and connected inquiry.

At the bottom, seven guiding principles anchor the framework: committing resources to equity and pluralism; fostering inquiry, critical thinking and creativity; promoting freedom to learn, teach, research and cooperate internationally; centring sustainability, stewardship and regeneration; embracing an ethic of collaboration and solidarity; establishing a human-centred role for digital technologies and AI; and supporting enriched understandings of quality, excellence and relevance.


Guiding principles to reshape the future of higher education (UNESCO, 2026)

The roadmap proposes seven guiding principles that are interlinked and mutually reinforcing:

  1. Committing resources to equity and pluralism.

  2. Promoting the freedom to learn, teach, research and cooperate internationally.

  3. Fostering inquiry, critical thinking and creativity.

  4. Establishing a human-centred role for digital technologies and artificial intelligence

  5. Embracing an ethic of collaboration and solidarity.

  6. Centring sustainability, stewardship and regeneration.

  7. Supporting enriched understandings of quality, excellence and relevance.


The document and what it is asking of us

The UNESCO roadmap is the outcome of a remarkable consultative process: over 15,000 participants, more than 1,500 comments on a draft roadmap, and 250 knowledge products submitted from across the world. It sets out seven guiding principles and a set of lines of transformation intended to move higher education toward what it calls a new social contract. The principles call for committing resources to equity and pluralism; promoting the freedom to learn, teach, research, and cooperate internationally; fostering inquiry, critical thinking, and creativity; establishing a human-centered role for digital technologies and AI; embracing an ethic of collaboration and solidarity; centering sustainability, stewardship, and regeneration; and supporting enriched understandings of quality, excellence, and relevance (UNESCO, 2026).

What the session made clear is that many people working in higher education find these principles genuinely compelling. The hunger for a more collaborative, more inclusive, and more epistemically honest sector is real and broadly shared. What is less clear is whether institutions, as opposed to the individuals within them, are prepared to act on these principles when doing so would cost something: revenue, convenience, prestige, or the comfort of familiar ways of working.

Digital access as an equity question, not a technical one

The roadmap is explicit that enriching higher education with "the possibility to study online and/or in hybrid formats would open higher education to more diversified learner motivations and interests, as well as to those who pursue it alongside full-time employment or care work" (p. 41). It also recognizes that "students can learn in different settings and spaces, whether that be in workplaces, in communities, or different cultural settings" (p. 41).

These are not technical observations about learning management systems. They are equity arguments. And yet across the sector, a counter-movement is underway. Universities that expanded hybrid and online provision during the pandemic, often discovering in the process that engagement did not collapse and that participation from previously excluded groups increased, are now reverting to in-person-only defaults. The rationale is rarely made explicit. When it is, it tends to appeal to the value of campus community, the richness of in-person dialogue, or concerns about student isolation. These are not trivial considerations. But they are being invoked selectively, in ways that consistently favor the preferences of those for whom in-person attendance is easy over those for whom it is costly or impossible.

The roadmap's call to move from "a scarcity and exclusion mindset to an openness and inclusion paradigm" (p. 35) applies here directly. Decisions about session formats, whether a seminar, a meeting, a public lecture, or a research event is offered in hybrid form or in-person only, are not logistical defaults. They are choices about whose participation the institution is prepared to resource and whose it is prepared to make contingent on circumstances that are not equally distributed. For educators, this means thinking carefully about the assumptions embedded in format decisions that are often made without much thought at all. For policymakers, it means recognizing that guidance on hybrid provision, and the resourcing to support it properly, has not kept pace with the rhetoric of inclusion.

Collaborative assessment and the gap between what we teach and what we test

The roadmap calls for pedagogical approaches to move away from "traditional listen-and-repeat methods" and toward active, problem-based, and project-based learning. It argues that "significant learning experiences often begin with a genuinely felt problem motivating the learner" and that student-centeredness means "involving learners in their own learning, so they are the ones making connections and shaping meaning" (p. 45). The document describes the overarching aim of higher education as building "collective and individual capacities for facing our common challenges together" (p. 29).

The conversations in our session pointed to strong agreement with this direction. And yet the dominant model of assessment in higher education remains resolutely individual. Students may be invited to collaborate in seminars, workshops, and project groups. But when grades are assigned, it is nearly always the individual who is evaluated. The group is, in practice, a scaffold for solo performance.

This is not a minor inconsistency. It sends a clear signal to students about what the institution actually values, regardless of what it says about collaboration, communication, and citizenship. If we believe, as the roadmap argues, that higher education's purpose is to build people who can face shared challenges together, then assessing them only as isolated individuals is a structural contradiction at the heart of the enterprise.

The objection that collaborative assessment is difficult to do fairly is real but insufficient. These are design problems, and they are solvable. What they require is institutional will: the willingness to invest in assessment literacy among staff, to create conditions for genuine pedagogical experimentation, and to accept that the discomfort of change is not a reason to preserve a model that is increasingly misaligned with the capabilities higher education claims to develop. For researchers, this is an area where practice-based educational research can make a direct contribution. For policymakers, it is an area where quality frameworks and professional standards could do more to reward innovation in assessment design rather than defaulting to the legibility of individual grades.

Decolonization and the question of whose knowledge counts

Perhaps the most resonant theme of the session was the need to take seriously the decolonization of knowledge: not as a metaphor, a branding exercise, or a curriculum add-on, but as a fundamental rethinking of whose ways of knowing are recognized, valued, and built upon in higher education.

The roadmap is direct about this. It acknowledges that "the claims of local and indigenous knowledge systems are increasingly prevalent, with voices from the global south dismantling knowledge gatekeeping" (p. 17), and calls for universities to engage with "plural forms of knowing as these are practiced by various communities around the globe" (p. 23). In its sixth guiding principle, it calls for research and scholarship to be "democratized, decolonized and disseminated to serve the common good" (p. 31). It argues that universities must go beyond respect and tolerance to ensure that "heterogenous ways of knowing and being become a welcome and respected foundation for building futures together" (p. 23).

For educators, this demands more than adding readings from the Global South to an otherwise unchanged curriculum. It requires examining the epistemic assumptions embedded in how disciplines are structured, what counts as rigorous methodology, which citations carry authority, and whose theoretical frameworks are treated as universal while others are marked as regional or merely applied. These are uncomfortable questions for many established academics, precisely because they put the foundations of expertise under scrutiny rather than merely its contents.

For policymakers, the decolonization agenda has implications for hiring practices, research funding priorities, quality assurance frameworks, and the governance structures of universities themselves. Institutions that talk about decolonization without addressing who sits on their hiring panels, whose research agendas attract institutional investment, and how their quality metrics are constructed are engaging in a form of performativity that the roadmap is trying to move us beyond.

Crucially, as I argue in the companion piece, epistemic justice and material justice are not separable. The question of whose knowledge is centered in a curriculum cannot be fully addressed while the question of who can afford to show up to engage with it remains unresolved. Universities are simultaneously asking people to think more expansively about knowledge while making the conditions of intellectual participation materially harder for precisely the communities whose perspectives the curriculum most needs.

A note on institutional culture and the cost of belonging

There is a question worth putting to any institution that claims to take equity seriously: what does it cost someone to spend a day on your campus? Not in tuition or fees, but in the accumulated small expenditures, transport, food, a hot drink, that constitute the texture of belonging. The answer varies considerably across institutions and across the sector, and the variation is not random. It tends to reflect how seriously an institution has thought about whose comfort and whose finances it has designed itself around.

For policymakers and institutional leaders, this is worth attending to. The grand language of transformation in documents like the UNESCO roadmap finds its test not only in curriculum reform or strategic plans but in the daily, material conditions of the people the institution is supposed to serve. I take this up in much more concrete terms in the companion piece, which looks specifically at Glasgow.

Calls to action

For educators: examine the assumptions embedded in your default practices. When you schedule an in-person-only session, ask who that decision excludes and whether the exclusion is pedagogically justified. When you design an assessment, ask whether it tests the capabilities you claim to value or merely the ones that are easiest to grade individually. When you design or teach a course, ask whose knowledge its theoretical foundations are built on and whether that foundation is as universal as it has been presented.

For researchers: the gap between the transformative agenda the UNESCO roadmap describes and the practices of actual institutions is a rich and urgent site for educational research. Work that documents the equity impacts of hybrid provision decisions, evaluates collaborative assessment models, and traces the relationship between material precarity and epistemic participation is directly actionable. It does not need to wait for the sector to catch up. It can help create the conditions for it to do so.

For policymakers: the seven principles in the UNESCO roadmap are only as meaningful as the frameworks, funding mechanisms, and accountability structures that support them. Guidance on hybrid provision, investment in integrated transport where universities are anchor institutions, reform of quality frameworks to reward pedagogical innovation, and serious attention to student financial support are not peripheral concerns. They are the infrastructure on which the new social contract the roadmap calls for either stands or falls.

The roadmap closes with the observation that "transforming higher education will always be an iterative, ongoing, multilateral and intergenerational process" (p. 55). That is true. It is also, if we are not careful, a way of making peace with the distance between vision and practice. The question is not whether transformation is possible. It is whether we are prepared to begin it, seriously, now.

A companion piece, focusing on what these arguments look like in the specific context of Glasgow, its housing emergency, its fragmented transport system, and the daily material conditions of the students and staff who make up its universities, follows shortly.

References & further information


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Reflections on work - from the past

I originally authored this post in April 2020 not too long after the COVID-19 pandemic caused cities and nations to lockdown. I found this as a draft post that I hadn’t published perhaps due to all that was going on at the time.

What I wrote here in April 2020, still holds in September 2023.

An image of a person standing on still water, which causes a reflection of the mountains and sky. Source: Unsplash

If we cannot recognize the truth, then it cannot liberate us from untruth. To know the truth is to prepare for it; for it is not mainly reflection and theory. Truth is divine action entering our lives and creating the human action of liberation.
— bell hooks quotes Black theologian James Cone in hooks, b. (2003). Teaching Community: A Pedagogy of Hope. Routledge.

Reflections on work

I've worked as a 'digital education facilitator / senior teaching associate' for almost 3.5 years at the Lancaster University Management School. I arrived hopeful, looking ahead to entering a new phase of my career within higher education where I would be explicitly working with a range of colleagues - academics, administrators, subject librarians and students - to develop both blended and online learning experiences. My time in my current role is coming to an end as I plan to move on to a new challenge and to both undertake a doctorate. In many ways, it makes sense to do these in the same place. In this post, I share some thoughts, reflections and hopes.

Synergy is key, when enacted

My current role is based in a large management school - a business school - where you have a range of business subjects divided into departments ranging from Accounting and Finance; Economics; Organization, Work and Technology; Management Science; and Marketing. There is also the Undergraduate Office where consortial programmes are situated.

One of the best part of working in such a large school was getting to meet a range of people from all walks of life and experiences. There are a lot of colleagues who care about their students. Indeed, the Dean at the time of this writing has had a project that sought to develop a cross-departmental community for students which takes the form of a module called MNGT160: Future Global Leaders: Sustainability Across Business.

This module has often been a source of contention as it sought to create a community that cuts across departmental boundaries, and thus, requires both contribution from each department and some hours to be workloaded from each department. Since it is not 'owned' by any one department, this module has, at times, not received a welcoming view. However, the aim and ethos of the module are fairly sound: to create a community while developing some graduate attributes within students through getting them to work together across their subject silos. Idealistic? Perhaps. Doable? Definitely.

With the amount of expertise and experience across the management school, such a module has great potential to create a very collaborative, cross-departmental community of learning and teaching that could strengthen the identity of the school itself while creating networks of students (and staff) who could work closely together in order to grow, develop as students, people and future professionals and subject experts.

Synergy is key for such a module to happen. Working together and drawing upon the expertise of such a large school to create good curricula, well-structured systems and a positive, welcoming environment for learning can only be a good thing surely.

The pandemic and the move to digital

Covid19 has upended a lot of systems, processes and practices. Initially, there was a lot of uncertainty that allowed some leaders to emerge in order to mitigate some for the panic and anxiety that the sudden shift or pivot to digital education that the pandemic caused.

During these first weeks and months, a lot of educational technologists were doing their utmost to help staff however and wherever possible. In fact, this is still continuing. What has been at the back of our minds - some of us - has been those little fleeting thoughts of ah, if we only had more blended learning before, we'd be more prepared for this!

Of course, learning/educational technologists have been trying for years to get academic and teaching staff to integrate in the digital into learning and teaching. We do this because we understand that, on the whole, students require a full range of digital literacies in order to live and work within the 21st Century to the full. People can live without collaborative and smart technologies, sure, but the world is generally progressing in the direction of closer collaboration and working together through digital means. Sustainability, efficiency and richness of opportunities are just a few reasons that digital literacies and their development are so key for the future. We could not have predicted the pandemic, nor used this as part of a rationale for integrating digital education practices for sounding, at best, alarmist.

That all said, what the pandemic has caused for digital education is a few points:

  • a sudden, renewed interest in digital education, whether blended or fully online;

  • a deeper understanding of working and studying at home, and how this can work;

  • a better appreciation for educational technologists and those who have integrated digital education practices into their teaching;

  • the development of a range of solutions to address issues arising around learning and teaching both remotely and at a distance;

  • and many others.

The fourth point is particularly interesting for me within my current role because I have been able to observe developments locally, nationally and internationally through a mixture of professional networks sustained by email lists, social networks on Microsoft Teams and Facebook and looser networks on Twitter and LinkedIn.

Working in silos: missed opportunities

Initially, I observed the same questions arising from the different places. I frequently saw the same or very similar questions coming from a range of staff that mostly where 'how to?' questions. I helped wherever I could by providing advice, solution and consultations where appropriate.

I began observing with a bit of annoyance and sense of powerlessness a pattern that slowly began to develop: colleagues were working in their departmental silos to create solutions. These solutions were not always shared across the departments at a macro level. As far as I was concerned, given my role and position that allowed somewhat of an overseeing eye, if I did not hear about it, I believed that a potentially valuable idea was not being shared to colleagues whom might need or find value in such solutions.

To my mind, this type of working did not make sense for a few reasons:

  • the problems themselves are common across the faculties - the 'how to?' questions;

  • solutions/ideas created in silos and thus not shared is, in effect, a replication of effort;

  • those with the most experience within digital education were not always consulted first despite their expertise, and in effect, time and attention was misused;

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A cornucopia of ideas: food for thought for digital education

Keep an open mind

Given the sudden shift to digital education that Covid19 has caused during the first half of 2020, we have now an opportunity to better understand digital education in order to prepare for autumn teaching.

However, first we might want to appreciate and understand how much Covid19 has affected and continues to affect both learners and educators. Part of this is understanding the nature of trauma and trauma informed pedagogy. Both learners and educators have been suddenly thrust into environments in which learning and teaching especially within higher education does not normally take place: at home.

Home can be a refuge for some, and it is often a place where we engage in our private lives with our loved ones, our family and our friends. These spaces have now, in part, been both transformed into semi-permanent learning and teaching spaces and been opened up to our colleagues and learners due to physical distancing that Covid19 has caused.

Underlying this sudden shift might be the notions of trauma and trauma informed pedagogy. I encourage you to look through the following articles in order to see how they might inform your teaching practices during and post Covid19. We aren't learning and/or teaching in normal times, and so we should be kind to not only ourselves but also to our colleagues and learners.

Trauma Informed Pedagogy 

Readings on digital pedagogy and education

Since for some of us learning and teaching online is a new approach, taking a bit of time to get to grips with some of the thinking around digital education and pedagogy can help us to get a variety of food-for-thought. This can help us to identify potential solutions to problems that we are likely facing with both a sudden move to digital education and a longer, more (hopefully) well-considered and designed move to blended and digital education.

I have found the following texts particularly useful in understanding how to develop colleagues in terms of shifting to digital education. I believe these texts will give you ideas that can help to inform your teaching practices, whether the autumn term is blended or ends up being fully online.

One note of caution: please remember to put pedagogy first and don't let the technology trip up your good teaching practices! Start with the learning outcomes of your module/course and consider the design accordingly.

Become a learner again, and learn from each other

Part of becoming a 'good' digital educator is giving yourself time to become a student again so that you can experience what it is like to learn online. This experience will give you invaluable insight into how you can design and develop your own digital education practices.

Another way is to learn from others - to learn from fellow students and educators who engage with digital education practices. The following resources might give you some ideas for both learning and teaching from the perspectives of both a learner and educator. I'd highly recommend exploring these and seeing how they might inform your teaching practice within your local context.

  • Online Teaching - learning design Pathway: https://events.educause.edu/lx-learning-experience-pathways. This is a short course on online teaching based upon the community of inquiry framework. I completed the course to experience what it would be like as a learner and to get further insight into digital education.
  • Digital Education Practices Podcasthttp://bit.ly/listentodep. I launched in April 2020 to collect, collate and share the stories of colleagues and students in terms of digital education practices. The episodes will give you some food for thought and ideas on what you could do in your own teaching.
  • How To Teach Online: Providing Continuity for Students https://www.futurelearn.com/courses/teach-online. This is one of a few online courses on FutureLearn and Coursera on how to teach online. Take one. Experience it, especially if you have little or no online learning and teaching experience.
  • Creative Assignment Ideas for Teaching at a Distance: https://eh.bard.edu/covid-19/#1584038372938-7026d698-0a95e470-b8e5. There are a lot of good resources out there related to assessment that Covid19 has motivated colleagues to write up and put on the Internet. This one talks about creative assessments that get away from the standard, in-person exams and other traditional assessments.

Community building & online inductions

One part of ensuring some success for a blended and/or fully digital experience is to create a community between the learners and educators. This should, ideally, be a considered, critical element of all programmes and/or academic modules for developing a cohort culture

One way of doing this, especially for the autumn term, is to have an online induction that begins 2 to 4 weeks prior to the start of the academic year. Using these weeks prior to the start of the academic year, especially for students leaving school and entering college/university, can allow students to form tight-knit relationships between students and lecturers that can lay the groundwork for effective working relationships while also developing students' academic and digital literacies.

How can this be done?

Creating the online induction and creating a community can take place anywhere:

  • through structured readings, activities and discussions on the VLE/LMS such as Moodle, Blackboard or Canvas
  • through semi-structured discussions via Microsoft Teams, Slack, Facebook or even Discord
  • through structured readings, student-authored posts and blogs via WordPress, Ghost or Medium

Suggested questions to develop close communities

Dialogue with new students is one key to forming close relationships. Critical questions can help encourage learners to express themselves in a meaningful way that gets them to open up while allowing networks to form among students based upon mutual personal, academic and/or professional interests that learners share.

Below are some examples of meaningful questions that can encourage students to open up while setting the scene for close relationship formation. These questions go beyond surface level questions such as 'What's your favorite food/drink?' or 'What did you do over the summer?' by getting students to consider at a deeper level questions that tease out meaningful thoughts and ideas.

The suggest questions for dialogue below are inspired by some introductory questions borrowed from https://www.jessestommel.courses/:

  • Where are you from? Or where have you lived? 
  • Describe yourself in 6 emoji.  
  • What are your hopes and fears for starting university?  
  • What are your passions?  
  • What would you like to know about your lecturer?  
  • What excites you most, what are you most worried about? 

Meet learners where they engage: create mobile-friendly spaces for handbooks

Meeting students where they might engaging in learning and that is inclusive of all devices that they use for learning (e.g. smart phones, tablets and laptops/desktops) is another way to ensure success for blended and digital education experiences.

As an example of this, you can modify your module/course handbook into an engaging, mobile-friendly and accessible document using, for example, Microsoft Sway. The simplest way is to create your handbook as normal in Google Docs or Microsoft Word, or similar, try to avoid tables, where possible, and then import this into Sway. One example of a course handbook in Sway is here: https://sway.office.com/W9QOna12DpBekwBQ?ref=Link.

Other good, mobile-friendly and engaging options

If you'd like to do something perhaps even more engaging, sustainable and editable year-on-year, then using either WordPress or Ghost can be used to create handbooks and learning spaces that might lend themselves to an engaging, mobile-friendly experience.

Potential concerns & suggested solutions for using WordPress and others

You maybe worried about the following:

  • access and accessibility
  • intellectual property and copyright
  • using a non-VLE /LMS system

Although these are all valid concerns, there are equally valid answers that can hopefully address these concerns:

  • WordPress-based sites often allow for both mobile and web-friendly design in line with current accessibility requirements. The options are clearly marked for authors/editors due to legal requirements.
  • EU-based WordPress sites will allow you display GDPR-compliant signposting
  • Per copyright and intellectual property, the educator(s) can choose to indicate how their materials should/shouldn't be used with clear signposting and messages on relevant materials, whether these are copy right or made into open educational materials, such as labelling materials using Creative Commons licensing.
  • If your college/university uses WordPress on its domain, such as http://wp.lancs.ac.uk/lums-research-methods/, then you very likely have the ability to restrict access for students by getting them to log in using their university credentials. You can also assign different roles and permissions to students, such as editor, author, contributor.
  • If you use your own WordPress site, you could invite students in and get them to log in, thus preventing whatever you do on WordPress from being publicly available?

You can easily link to the module/course on the VLE on Moodle, etc.  

Why might you use this approach?

Well, in addition to giving your students 'a break' from the traditional VLE, you'll also be creating a space where students can quickly access key, important information without being forced to log into the virtual learning environment, navigating to the respective module (remember, they have a few!) and then accessing/downloading the relevant information.

Examples & inspirations:

Social annotation  

No matter the subject/discipline that you learn/teach in, there are many reasons to use social annotation in learning and teaching. There is also a growing body of scholarship and research on how social annotation can help education, and select literature is listed below.

Social annotation can be used for:

  • understanding, identifying and analyzing ideas within texts, and relating these ideas to other texts;
  • interpretation and meaning-making within texts used in seminar/workshop groups;
  • breaking down, analysing and interpreting data from laboratory experiments and lab sessions;
  • co-creating and construction of ideas and text;
  • teaching the importance of attribution of ideas and literature in papers (especially using this article: https://nyti.ms/2mtgUaU);
  • and many others!

One simple way of using social annotation is to use either Google Docs or Word Online to look at a text. Either of these can work with primarily text-based information with few images. Groups can work together online to create sections of text and the educator can also 'drop in' to review, annotate and suggest ideas as necessary.

Annotating the web

However, you may want to annotate entire webpages, web books and articles that are already online. One way to do this is to use Hypothes.is: https://web.hypothes.is/ which allows private and shared annotation of websites and webpages, web books, PDF documents and many others.

Select literature on social annotation

Clapp, J., DeCoursey, M., Lee, S. W. S., & Li, K. (2020). “Something fruitful for all of us”: Social annotation as a signature pedagogy for literature education. Arts and Humanities in Higher Education, 1474022220915128. https://journals.sagepub.com/doi/pdf/10.1177/1474022220915128?casa_token=JWqh0QLbsFYAAAAA:Y6az7TZR6nzNW3MW5-9Mc3IObkv8InoifJzQWJccU4u5LMfPdzXPixZIb4_c1ZrNIQwWL8ep8DK4

Hedin, B. (2012). Peer feedback in academic writing using Google Docs. Pedagogiska inspirationskonferensen-Genombrottet. https://sociologiskforskning.se/pige/article/download/20822/18729

Kalir, J., Cantrill, C., Dean, J., & Dillon, J. (2020). Iterating the Marginal Syllabus: Social Reading and Annotation while Social Distancing. Journal of Technology and Teacher Education28(2), 463-471. https://www.learntechlib.org/primary/p/216246/paper_216246.pdf

Pargman, D., Hedin, B., & Hrastinski, S. (2013). Using group supervision and social annotation systems to support students’ academic writing. Högre utbildning3(2), 129-134. https://www.diva-portal.org/smash/get/diva2:628110/FULLTEXT01.pdf

Zhu, X., Chen, B., Avadhanam, R. M., Shui, H., & Zhang, R. Z. (2020). Reading and connecting: using social annotation in online classes. Information and Learning Sciences. https://edarxiv.org/2nmxp/download?format=pdf

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Holding virtual office hours - suggestions

A colleague asked about holding virtual office hours. The question entailed both how to do this in terms of the pedagogy and the technology. So, I drafted a document that addresses some suggestions around holding virtual office hours (and tutorials) which can be found here: https://sway.office.com/yqhBJFryvfm46a5e?ref=Link as a Sway document that can be printed and shared.

A colleague asked about holding virtual office hours. The question entailed both how to do this in terms of the pedagogy and the technology. There were quite a few considerations to take in to account namely:

  • class and cohort size;
  • possible apps/tools to be used;
  • getting the most out of a virtual session;
  • preparation required prior to a virtual office hour/tutorial;
  • expectations for students/attendees of said events;
  • and many more!

So, I drafted a document that addresses some suggestions around holding virtual office hours (and tutorials) which can be found here: https://sway.office.com/yqhBJFryvfm46a5e?ref=Link as a Sway document that can be printed and shared.

I should note that, while I focus on using Microsoft Teams, the principles for this apply to Zoom and Google Hangouts, among others. The main differences are that Zoom allows 'breakout rooms'. I haven't used Google Hangouts for a similar purpose, so can't comment.

I hope this is helpful!

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Moving to digital education

The purpose of this post is to shed some light on some thoughts to consider, good practices and tips for moving from face-to-face teaching to digital education.NB: These are suggestions to help you to move to digital education. These solutions depend on your own abilities, desire and time. You have the support of your colleagues both in-intuition and beyond – you only need ask. The solutions here are informed suggestions.No perfect solutions exist.

The purpose of this document is to shed some light on some thoughts to consider, good practices and tips for moving from face-to-face teaching to digital education.

The impetus for sharing these ideas comes in light of the spread of the novel coronavirus, COVID-19, which has reached the status of a pandemic on Wednesday, 11 March 2020. Various countries reacted in different ways. As of the time of writing this document, several UK universities (London School of Economics, Durham University, University College London, Lancaster University and Glasgow University) have suspended classroom-based teaching effective either immediately or from Monday, 16 March. More universities are expected to follow the steps of other schools, colleges and universities that have already taken steps in other countries.

NB: These are suggestions to help you to move to digital education. These solutions depend on your own abilities, desire and time. You have the support of your colleagues both in-intuition and beyond – you only need ask. The solutions here are informed suggestions.

No perfect solutions exist.

NB: I may update sections of this post in the coming days as developments take place.

Developing pedagogy for digital delivery & communities of practice

There are a lot of networks out there that are discussing this right now.

One of those networks is on Twitter and you can find out more by following the Learning & Teaching in Higher Education Chat by looking for #LTHEChat and/or by visiting the following link: https://lthechat.com/2020/03/11/covid19-special-edition/ or by following @LTHEChat on Twitter.

Due to the impact of the coronavirus COVID-19, there is a daily chat on Twitter where you can meet and network with colleagues from across HE who are facing the same issues as you. In addition, you’ll also find a lot of ideas directly related to pedagogy, learning and teaching.

Pandemic Pedagogy

Another space that has sprung up is a large, interdisciplinary community on Facebook called Pandemic Pedagogy that has nearly 15,000 members and is constantly growing. You'll meet colleagues from almost every discipline that universities tend to offer.

Considering this group was set up on Thursday, 12 March, it's very quickly becoming a space for educators especially within higher education to ask questions and get and offer solutions on a grand scale.

NB: you will need to request to join but this should be approved within an hour or so!

Taken from a user on the Pandemic Pedagogy group.
My own take: This is highly relevant and we should maybe think of this while transitioning our teaching. Things won't be picture perfect and we'll be learning as we go!

Repurposing existing content – thoughts to consider

Review what content you have; if this is video content such as a pre-recording lecture that was captured earlier, ask yourself:

  • What, if any, improvements need to be made?
  • Is the content still relevant? Do parts of it require an update?
  • What needs to be cut/curtailed?
  • Can you tolerate sitting through this content for an extended period of time?

As an example of this, if your content has been recorded through lecture capture software you might have to consider the following:

  • does the content have good audiovisual quality, or will this impair the learning experience for students?

What to do with existing slide decks from presentations?

Some of you may have a slide deck that has slowly grown over the last few terms or semesters that have become potentially invaluable teaching tools. It's tempting to take an existing slide deck and place it online without any changes as this might be considered a path of least resistance.

However, even with your voiceover and a recorded video, students might benefit from a bit of structure that neatly breaks down the content. If we refer back to our earlier principles, we need to consider relevance and timeliness. So, when we look at a giant slide deck we've prepared over the years, we should reflect and ask ourselves:

  • How much of the content is suitable for this particular course or module?
  • What needs to be cut?
  • What can I do to make the content more engaging and/or interactive for the students?

Solution: Repurposing existing slide decks

One way of taking a slide deck and making it more engaging is to neatly divide content into easily digestible sections; most good slide decks will have a clear enough structure that this won't present an issue.

The next step is to insert an activity slide or two that gets students to think about the issue, problem, or topic at hand by constructing a task or problem for students to consider, process and/or solve in a meaningful way that helps connect what they've learned to practice.

The activity slide(s) can then be followed up by a worked-out solution (or more, depending upon the subject) that looks at the different solutions and provides some commentary/analysis that break the solutions down.

Of course, adding an activity slide and solution will take some time and this is perhaps a drawback. The advantages, however, are numerous: you will have created a reusable learning resource that students can use to learn, apply, practice and check their learning. Whether or not they do this is a different question! 

How to structure content for effective delivery online

Structuring online learning and teaching is absolutely key to a successful experience for all stakeholders. Although it may seem obvious, since a significant part of learning online takes place without a teacher/lecturer in the room, students must be shown the path(s) to learning in an explicit manner. This path must be shown within the course/module handbook and through a mixture of audiovisual and visual signposting within a virtual learning environment, such as Moodle. 

One way of creating an effective design for learning is to include, at the very least either a video or audio recording that introduces the module/course in brief. A recording of about 5 minutes should generally suffice. The message is best if it's clear, succinct and on point.

A screenshot of a social media postDescription automatically generated
Structuring an online module - an example

Developing netiquette & nurturing a community of online learners

Learning online often entails an increase in text-based communications. In 2020, with a lot of text-based communication already happening via WhatsApp, iMessages and other apps, understanding how to author written messages in an understandable, diplomatic manner is as important as ever.

Therefore, netiquette and how to communicate effectively in text when no visual or body language clues are present is important. This link gives a brief guide on developing good netiquette: https://sway.office.com/ObLxmwHTMKZE4vRB?ref=Link

As far as developing a community of online learners, the link in the post below by Sarah Honeychurch neatly encapsulates a few good thoughts and practices of how to do this. There are a few points to consider when fostering a community of online learners - have a read in the link below!

https://twitter.com/NomadWarMachine/status/1238504043728842753

What to do with seminars?

Although lecture sessions may be recorded and previously recorded lectures may be archived, some of you may be thinking about how you will have seminars.

Seminars are where students get more personal, face-to-face contact with peers and their instructors as these offer an opportunity to discuss, analyze and operationalize concepts and ideas presented during the lectures. There are a few potentially actionable and valuable solutions with the main drawbacks relating to Internet connectivity and access to devices. 

Hosting digital seminars

Live seminars can be conducted through using web conferencing software such as Microsoft Teams, Zoom or Google Hangouts. Think of your digital seminars as webinars for your students. If your students already work in small groups, then they could form their own private chats to discuss ideas during the seminar.

Questions prior to moving to digital seminars

  • What type of connectivity do your learners have access to?
  • What can be pre-recorded?
    • What can be broken down into smaller bites?
    • What interactivity can be built in?
    • Worried about video size? Handbrake (https://handbrake.fr/) can help to shirk file sizes while maintaining audio-visual quality
  • What would be best delivered live?
  • What do we want students to work on together – regardless of when the learning takes place?
  • What type of discussions do we want?
    • Asynchronous vs synchronous?
  • What technical tools do we have at our disposal?
    • PowerPoint presentations – recorded with audio
    • Presenting via Teams – and having this recorded
    • Collaborative documents for team-based group work
      • Invite specific members into each group-specific collaborative document

Practicalities

  • You can schedule an online meeting in Teams that will allow your learners to join a digital seminar – make sure that you invite them or send them a link!
  • You can record a meeting in Teams by using the red record button, and this will allow others to take part in part of the session and catch up if they weren’t able to attend.
  • Make sure that you lay out expectations:
    • Have all learners downloaded and signed into Teams successfully?
    • Have learners had a chance to ‘get to know’ and play with Teams?
    • Do you want students to mute their microphones?
    • Do you aim to have a member of each group participate and/or represent their group?

Tips on good practice for online seminars

These tips below were kindly shared by a couple of colleagues I work with - Emma Watton and Florian Bauer:

  • Make a note of key messages in advance to give attendees a clear direction of where the webinar is going;
  • Keep sentences short and avoid jargon;
  • Use slides/diagrams/models to help communicate ideas;
  • Signpost to follow up web content/reading etc.;
  • Use a booster plug-in speaker if available to improve sound quality, book a room if your office is by the building works;
  • Consider a guest joining on a webinar to add different perspectives;
  • Be mindful of the length, 10 minutes is a lot of content to listen to online so create more shorter webinars rather than one long one;
  • Don’t wear heavily patterned/checked clothing as this can cause pixellation on the screen especially for slower connections;
  • Request students to mute their own microphones when they aren't speaking to reduce feedback caused by mics.

Meme shared by a friend.

Remember: when you're joining a web conferencing video, especially if you'll be speaking, take a moment to adjust your web camera and the level of your seat height. Don't sit too close to the web camera and make sure the lighting is decent!

Traditional online discussions

One simple way of creating a longer, asynchronous (not live) discussions around content is through the use of online discussions, such as the use of discussion forums on Moodle. You can assign content that students can access prior to engaging in the discussion.

Tips for successful discussion forums:

  • Ask open-ended questions that foster critical thinking and analysis
  • Ask questions that get students to reflect and relate their learning back to their own unique contexts (where appropriate)
  • Avoid yes/no questions
    • Yes/no questions can encourage simplistic answering and thinking.
  • Set parameters
    • what do you expect of students in terms of behavior and responses?
    • How often do you want them to post?
    • Will you respond to each and every post?
  • Don’t expect everyone to participate
    • lurkers gain a lot by silently reading what others are posting; these posts can give them food for thought and cause for writing about their own perspectives

Of course, there are tips for students, too. They can get a lot out of discussions in terms of critical thinking, idea development and written communication and abilities development by taking part in online discussions:

What to do with exams?

Exams are often held in person in rooms with an invigilator. Exams can be held online with some limited oversight.

Perhaps more importantly, in light of the circumstances, you might wish to ask yourself:

  • Have students already met their intended learning outcomes through other tasks? If so, then is an exam still necessary?
  • Why is the written exam still necessary?
  • What’s the scope for making the exam ‘open book’?
  • Can time parameters for exam be flexible?

Online exams – one way to do it

If you have an exam that consist of short or longer text-based answers, multiple choice questions (MCQs) or mathematical equations, then you can re-create this exam using Google Forms, Microsoft Forms or using the Quiz feature in Moodle.

One simple way I’ve recently tested with success is to use a Microsoft Form to replace a traditional, face-to-face exam:

  • the exam consisted of relatively short answers of less than 200 words per question;
  • each question was assigned a whole-number mark;
  • the online version was set to start and finish at specified times;
  • the students were required to log in using their normal university login and password which meant that we could identify

Other tips for doing online exams with Microsoft Forms:

  • send students the exam link prior to an online exam with clearly set time parameters;
  • send students an exam link immediately prior to the exam – and set no time limits
  • build in some leeway which often standard with Moodle exams in other faculties, departments;

Advantages of using Forms for quizzes/exams

  • students are required to log in using their university credentials;
  • time restrictions can be set to allow for clear start and end dates and times;
  • questions and submitted answers will be collated into an Excel spreadsheet for easy marking;

Example of how an exam or quiz looks in Microsoft Forms

An example of a quiz/test/exam using Microsoft Forms

Want more information?

To find out more about how to build effective and authentic quizzes and exams, then take a few moments and follow this link that has more information https://education.microsoft.com/en-us/course/ac59d6bc/overview

Summary & kudos

These are just a few suggestions that I decided to add to the ever-growing amount of solutions that people are putting together after reading inspiring posts by Dale Munday and Kyungmee Lee (see below).

https://twitter.com/Dale_Munday/status/1237403844109389824

https://twitter.com/hi_klee/status/1237081081507196929

Selected ideas, guidance and readings for designing for learning online & communities of practice

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Getting students to use (new) apps

I've decided to quickly write up some thoughts on getting students to use new apps for learning and teaching as a reflection on what I've observed over the last few years and more recently.

It's safe to say that I approach this post from the point of view that there are many opportunities for digital education to enhance the learning and teaching experience.

More specifically, I'm writing this short article in relation to #MicrosoftTeams and what you need to do to ensure successful uptake by students and staff. What I write here applies to any other new systems - even ones such as Moodle.

Social media all around

It's fair to say that a lot of students and even staff in higher education use a variety of social media for various purposes. Students and staff still may use Facebook to connect with their friends and family, and classmates and course mates. Statista has a wealth of data on users of Facebook and Twitter, if you're interested.

Both of these groups may, if they're interested, use Instagram to create, collate and share images and/or video - photography and multimedia generally. A good number of students use Snapchat and in the UK high numbers of users aged 18-24 are likely to use Snapchat. Some university staff even use Snapchat to engage students in the classroom - with success!

Students aren't digital natives

A lot of my colleagues in higher education might understandably believe that because students regularly use apps like Snapchat, Instagram, WeChat, Facebook and others that this ability translates into a being able to effectively use digital tools and being tech savvy - being digital natives. - well beyond what my colleagues may have grown up with.

A lot of us use technology to 'passively soak up information' which could be scrolling through a Facebook or Instagram feed and reacting to posts. Yes, perhaps we share the odd image, video or article and add a bit of commentary - commentary - but are these acts critical or rather habitual?

I'd say these are habitual acts that form part of a series of daily routines in which users might fill time - gaps between spurts of attention to other things - and/or while navigating and exploring the vast ocean of information that's out there. From funny memes to noteworthy articles or click-bait news - it's all information, and it doesn't take much effort to open our favorite app to access that information! And this leads me to my main point...

New and unfamiliar systems

In a university setting, students will often use platforms such as Moodle, Blackboard, Google Apps for Education or similar. Microsoft has an answer, too, #MicrosoftTeams. All of these platforms offer a range of activities, structures and systems that can greatly help to manage the design, flow and presentation of information for users.

One thing we should not forget is that the aforementioned systems are created for the purposes of education, business and collaboration generally which go beyond the basic functions of Facebook, Snapchat and Instagram which are primarily for 1:1 or small group chats/discussions that are often centered around the sharing of media.

However, what unites all of these systems, platforms and apps for education is that generally these are unfamiliar to students unless there is a chance that they'd previously used one of them in school. Even then, if, for example, students have used Moodle in school, the look and feel of the system may not represent what they end up seeing in a university setting. Indeed, where modules on Moodle are still often used as repositories rather than engaging learning and teaching hubs, this can be daunting for users of 21st Century systems such as Google Search or Bing that offer information at your fingertips with few hurdles if you understand how to do key word searches. This leads me to a question:

How often do you explicitly train your students in using your university system or an app for a module?

I suspect not a lot of programmes take the time to explicitly provide training to students. That said, think of all the time we spend when we start a new post to receive training on the following:

  • health & safety
  • diversity
  • data protection & GDPR

So why don't we spend a bit of time investing in the training of digital abilities and skills rather than assuming that the use of a smart phone = being digital and tech savvy? Taking a selfie does not make you a tech expert!

New systems require explicit training

#MicrosoftTeams is taking off as the latest app for learning, teaching and collaboration generally within higher education in the UK. Indeed, I'm using it on a module that I lead on and it's confirmed a few things that I learned a few years ago.

Between about 2014-2016, I was working with pre-sessional student who would come to the UK during the summertime period to study English as a foreign language for the purposes of improving their academic English language abilities. Students generally had an English language knowledge of about B1 to B2 and they had digital skills that ranged significantly. Nearly all had a smart phone and could use the main apps of the day.

We used Moodle as our online platform with our students to set readings, have online discussions and set assignments that students would write up, upload and submit. Moodle was a system most students hadn't used and would only use in their university studies. In order to ensure the students' success in using the online platform as an enabler rather than a distraction, I convinced colleagues to allow all students to receive 1 hour of explicit instruction on hows and whys of using Moodle.

During the summer, we had around 700 students over 3 cohorts that we needed to train up. So, we booked computer labs and trained students in groups of 30-50 each in the space of about 1 hour; there were frequently 3 staff (including myself) on hand to help out and ensure that everyone was on the same page.

Effective training = tangible benefits

Although with the sheer numbers of students to train some days were long, the result was that we were able to ensure that over 90-95% of the students understood what Moodle was and what it was for, why we were using it and how they could access it. This number was able to ensure that we had created a relatively strong community of learning in which students could support each other in understanding and practicing how to navigate an unfamiliar and new system, which in this case was Moodle.

As a side benefit, also important, for students whose first language wasn't English, they were able to understand that they were going to get a lot of writing practice in English, which would boost their confidence in writing more fluently (albeit not always accurately) in a relatively authentic, meaningful way that they could then transfer back into their own writing for essays and assignments.

Key takeaways

The key takeaway here is this: If we throw the apps at students, they don't always get it. They generally get Instagram, Facebook, Snapchat... because those are fun apps for fun, social stuff. They won't necessarily get apps for education, business and collaboration though; these aren't natural apps - they aren't always fun (or associated with fun!), so we should prepare our students first before letting the apps loose!

With nominal training (1 hour) students will:

  • develop a critical awareness of the reasons for using the system;
  • gain effective practice in using the basic, required elements of the new system;
  • develop transferable digital skills that can be used for approaching and understanding new systems.

So, if you're going to teach on a module that involves Moodle, Microsoft Teams and/or similar, and/or if you have a student induction coming up, take the time to build in 1 hour of training.

The results will pay off and speak for themselves!

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Thoughts on 'Why Believing in Your Students Matters' by Katie Martin

teaching beliefs cycleToday I came across this succinct article by Katie Martin on why believing in our students matters, as this can have a significant impact upon a teacher's practices and students' uptake of learning regardless of where learning and teaching that takes place - whether face-to-face or online.While I have known about the need to wait for responses from students for some time, and I value this approach, sometimes one can wait a bit too long. UK universities have had high and growing numbers of students from China for a while now. Some universities throw their doors open to International students since they pay higher fees.One such university near London where one Master's program of 150+ students has well over 95% of its students from China. At the same time, some of these same universities that seek to recruit large numbers of International students, often heavily reliant upon specific markets such as China, can, at times, lower the bar in terms of language requirements. From my own experience of observing and delivering teaching, this situation, which isn't unique to the aforementioned university, creates a number of issues since the students often:

  1. come with different levels of language readiness for an intensive postgraduate level of study;
  2. are not or may not be used to interacting and socializing with those from other countries;
  3. are unlikely to work outside their 'peer' group of compatriots due to shyness, peer pressure or do so begrudgingly; and/or
  4. lack confidence in their own abilities and are perhaps not provided with enough motivation from teaching staff to instill a positive, 'can-do' attitude to learning.

The result of any or a combination of these is that lecturers, academic tutors, learning developers and tutors of English for academic purposes are frequently put into tricky situations: the content has to be delivered, but if students are struggling to understand, what is to be done? Too often I have heard over the years, from staff at various institutions, similar negative remarks that Katie mentions in her article. I've always found these types of comments particularly demotivating and, silently, I ask myself upon hearing sustained negative comments "Well, why the hell are you in teaching?!" It is as if those making such comments were perfect students who always worked hard.On the flip side, the best colleagues I've had have always been positive, supportive and empathetic to the student journey. This empathy seems to set apart the negativity of the moaners from the teachers/lecturers whose lessons that we would always look forward to when we were once students. I think part of this empathy that some educators have is at least partially informed by the works of the Brazilian educationalist Paolo Freire, among others.Going back to Katie's article, I think one solution is creating a positive, welcoming environment that seeks to recognize the students as intelligent participants who are able to interact at Master's level successfully with regular, positive support that seeks to push the students' boundaries and to modify our teaching practices to engage the students in such a way that might tease out from them meaningful participation.One way, I believe, is to have a meaningful, welcoming induction to a program that gets students involved in getting to know their peers and teaching staff beyond the polite formalities of titles and names (think: basic teambuilding activities that get students to solve real problems related to their studies and/or life within their new educational setting). Oftentimes, I've seen inductions that were so superficially boring, stereotypical and/or dry that it immediately set the wrong (superficial) tone for the program of study in question.Another solution is to embed positive thinking throughout a program. As Katie says in her post:

... when we believe we can learn and improve through hard work and effort we can create the conditions and experiences that lead to increased achievement and improved outcomes.

In terms of learning and teaching, this is particularly powerful for our students. If they feel the above, they can and will improve in their learning journey. We, as educators, have a responsibility to instill these ideas into our students, especially International students who might genuinely need extra support, encouragement and motivation in order for them to become independent learners. Part of ensuring the success of our learners is to change our thinking - to think more positively, and to believe in our students.This also means we might need to change our approach to learning and teaching. So, for example, imagine you have a session of 15-50 students and they don't volunteer answers without being called on and prefer to stare at their phone or laptops (or both!). If our students are quiet and reticent to raise their hands to volunteer an answer, then there are some easily-doable solutions.

Apart from those small solutions, I believe that part of ensuring the success of our learners is also to change our thinking - to think more positively, and to believe in our students. So, for example, rather than immediately assuming that most, if not all, International students are likely to plagiarize essays, we can set the stage from the start by building a positive, supportive environment that seeks to educate rather than pontificate. Another quote from Katie's article below underscores my message:

“When we expect certain behaviors of others, we are likely to act in ways that make the expected behavior more likely to occur. (Rosenthal and Babad, 1985)”

Let's take plagiarism. I've often heard from colleagues both genuine concerns and negative comments/expectations of students in terms of plagiarism. This, in turn, leads to plagiarism being approached in an almost compelling manner within course materials: plagiarism is bad, and therefore if you plagiarize you are bad and so if you plagiarize, you will fail, etc.Using the above example, one relatively simple way to embed a positive approach to learning and teaching is to change the negative, hellfire-and-damnation discourse on plagiarism often present within course materials to one that offers an open, frank discussion on attribution and giving credit. One such way I have done this is by getting students to look up and understand attribution through discussion, and then following this up by reading an in-depth report on a politician who plagiarized a paper for a Master's degree. From what I have observed, these combined approaches give students a chance to explore the issue of plagiarism through a more empowering lens while exercising their academic literacies (digital and information among others).From what I have observed, these combined approaches give students a chance to explore the issue of plagiarism through a more empowering lens while exercising their academic literacies (digital and information among others). It gets them thinking and talking amongst each other rather than being spoken [down] to in terms of the issues of plagiarism. Along with the teacher creating an empathic, positive atmosphere, this also makes students feel part of the discussion and (more) part of the academic community as they seek to understand expectations that may be new and/or alien to previous educational experiences.Ultimately, the choice lies with the teacher in question to change their practices or not. There is always an element of risk to transforming teaching practices. However, without taking risks (even small ones) to innovate, one will simply never know how effective the changes to might be. Mulling ideas over is a good way to get started, but as with anything, mulling ideas over for an extremely long amount of time can kill ideas and innovation. Staff who have ideas should be allowed to experiment, and line managers should be proactive in supporting staff who are enthustic about learning and teaching.If things don't entirely work as planned or expected, well, at least learning has occurred on the part of both the learners and the teacher(s) in question. The light bulb and radio weren't perfected within a day's time, so why should a new teaching approach be perfected before trying it out?! Just do it!Just do it!

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