Critical AI Engagement Framework, Version 1.0

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.

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Not for citation without permission
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
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Education, Pedagogy, Decolonizing education, CPD Dustin Hosseini Education, Pedagogy, Decolonizing education, CPD Dustin Hosseini

Listening, Speaking, Learning: On Verbal Feedback and (Re)Humanizing Assessment

Sunset over Queens Park pond, Glasgow, UK

Recently, I listened to a podcast from 2022, Educatalks: Reflective Practice featuring Professor Melaine Coward, a professor of medical education reflect on her career and her commitment to reflective practice. Medical Educatalks is a podcast created by the Developing Medical Educators Group (DMEG) at the Academy of Medical Educators. Toward the end of the conversation, she described her decision to give students verbal feedback on their assessments. The interviewer sounded genuinely surprised, he hadn’t encountered that approach before.

I paused.

Not because it felt novel, but because it felt familiar.

In 2015, while teaching at a University of London institution, I experimented with providing verbal feedback on written assignments. At the time, our digital marking platform enabled tutors to attach audio recordings directly to students’ scripts, so feedback could be posted alongside the written work itself. Students could either listen asynchronously or book a short follow-up slot to discuss it further. I would have their script in front of me as I recorded or spoke with them, talking through strengths, misunderstandings, and next steps. It was dialogic, immediate, and relational, but it was not universally welcomed.

The pushback was swift and couched in procedural language:

  • How could this be standardized?

  • How could it be moderated?

  • Where was the audit trail?

Ironically, the digital system did generate an artefact. The audio file was stored alongside the script. There was a record. And yet the discomfort persisted. What seemed to trouble colleagues was not the absence of documentation, but the presence of voice, with its tone, inflection, and spontaneity. Feedback had become less easily reduced to a static text block. The underlying concern was not simply technical. It was cultural. Feedback, in this framing, was not primarily a pedagogical encounter, it was a compliance mechanism.

Listening to Coward years later, I realized something I could not fully articulate back then: verbal feedback is not merely a technique. It is an epistemological stance. It is a small but meaningful act of (re)humanising assessment.

But what I found was, so they weren’t reading the comments that I’d spent ages putting on marking there, because I do spend time, it matters that I give good feedback. When I did recorded feedback, I found I had a lot more follow-up from students, because they had had to listen to my feedback, and it was a very clear message of, I really enjoyed this, something for you to think about. I would be quite structured in how I recorded it, so I had notes, so it was formulaic in that sense, but not rehearsed.
— Professor Melaine Coward

Feedback as encounter, not transmission

Higher education assessment cultures are deeply shaped by what Paulo Freire famously critiqued as the “banking model” of education in Pedagogy of the Oppressed. In that model, knowledge is deposited; feedback becomes a written correction of deficits; learning is framed as remediation.

Written feedback, of course, can be thoughtful and transformative. But it often operates within systems that prioritize defensibility over dialogue. Comments are calibrated for external examiners. Language becomes cautious. Tone becomes formal and neutralized. The student becomes a case.

Audio feedback, even when delivered asynchronously through a digital platform, subtly shifts that dynamic. Students hear emphasis. They hear encouragement. They hear uncertainty where appropriate. Meaning is shaped not only by what is said, but how it is said.

And when audio is paired with optional follow-up conversation, feedback becomes dialogic in a deeper sense. Students can respond, query, reinterpret.

This resonates with Freire’s insistence on dialogue as the foundation of emancipatory education. It also aligns with bell hooks’ vision of engaged pedagogy in Teaching to Transgress, where teaching and learning are relational acts rather than one-way transmissions.

When we speak with students rather than at them, feedback becomes less about surveillance and more about growth. Voice, literal voice, reintroduces presence into assessment by (re)humanizing it.

The standardization question

The resistance I encountered in 2015 revolved around standardisation. Written comments were seen as stable, recordable, and therefore fair. Audio feedback, even though stored and retrievable, was viewed as potentially variable.

But here is the uncomfortable truth: standardization is not synonymous with justice.

Critical and decolonial scholars have long questioned whose norms assessment criteria encode. Ngũgĩ wa Thiong'o, in Decolonising the Mind, reminds us that language and evaluation are never neutral; they are embedded within colonial power structures. Similarly, scholars of antiracist pedagogy argue that assessment practices often privilege dominant linguistic and epistemic norms and performances.

Audio feedback can surface some of this hidden curriculum. It allows educators to unpack what we mean by “criticality” or “coherence” in accessible, responsive ways. It can soften deficit framings by conveying nuance and care. It can make tacit expectations explicit.

For neurodivergent students, multilingual students, or those unfamiliar with disciplinary conventions, hearing feedback, with tone and pacing, can support comprehension in ways that dense written comments may not.

Uniform delivery formats may be easier to audit. But equity sometimes requires responsiveness.

Reflective practice and professional identity

Coward’s framing of verbal feedback emerged from reflective practice, a concept often associated with Donald Schön and his work The Reflective Practitioner. Reflection is not merely about improving technique; it is about interrogating the assumptions that underpin our actions.

Looking back, I can see that my 2015 experience exposed a tension between two logics and a clash of paradigms:

  • Assessment as pedagogical relationship / Was feedback a compliance mechanism or a pedagogical relationship?

  • Assessment as quality assurance infrastructure / Was my role to produce defensible documentation or to cultivate understanding?

The digital tool itself was neutral. It could host text or voice. The debate was about what counted as legitimate academic labor and legitimate evidence of fairness.

Reflective practice asks us to interrogate not only how we teach, but why certain practices are normalized while others are treated as suspect.

(Re)humanizing assessment in digital spaces

In my current work, including conversations around decolonizing curricula and rethinking assessment, I often return to a simple question:

What would assessment look like if we centered humanity rather than auditability?

This is not an argument to abandon rigor or documentation. Rather, it is a call to re-balance priorities.

(Re)humanizing assessment might include:

  • Dialogic feedback conversations alongside written summaries

  • Audio or video feedback that conveys tone and relational presence

  • Opportunities for students to respond to feedback

  • Co-constructed criteria discussions

  • Assessment designs that value multiple ways of knowing

These moves resonate with broader critical pedagogical commitments: resisting neoliberal metrics, challenging deficit framings, and recognizing students as co-participants in knowledge production. These moves further resonate with critical pedagogy’s insistence on dialogue, with antiracist commitments to challenging hidden norms, and with decolonial calls to unsettle inherited hierarchies of knowledge.

They also align with emerging scholarship on compassionate pedagogy and relational assessment cultures within higher education.

Hearing someone talk about what you’ve done, the tone and voice to highlight praise, concern, and you can add in a more questioning tone ... They loved it. They loved it because they could hear from my touch.
— Professor Melaine Coward

An epiphany, years later: are our systems human enough?

Listening to Coward describe her practice, I felt both affirmed and reflective. The surprise expressed by the podcast interviewer revealed how deeply entrenched written, standardized feedback remains. Yet the fact that such practices continue to surface across disciplines, from medical education to the humanities, suggests a quiet shift. I also felt less concerned with whether verbal feedback is innovative and more interested in what it reveals. What I once framed defensively as “innovative feedback” now feels more clearly like a small act of resistance against depersonalized academic systems.

Even when captured and archived in a digital platform, voice unsettles the fantasy that assessment can be entirely standardised and neutral. It reintroduces tone, care, and relational accountability.

Perhaps the question is not whether audio feedback can be moderated.

Perhaps the more urgent question is whether our assessment cultures allow space for humanity, for dialogue, for nuance, for recognition.

If critical, antiracist, and decolonial pedagogies ask us to re-centre people rather than processes, then even something as simple as attaching a recorded voice note to a script can become a quietly radical act.

Suggested further reading

  • Pedagogy of the Oppressed – Paulo Freire

  • Teaching to Transgress – bell hooks

  • Decolonising the Mind – Ngũgĩ wa Thiong'o

  • A Handbook of Reflective and Experiential Learning – Jennifer A. Moon

  • The Reflective Practitioner – Donald Schön

And, of course, I would recommend listening to Educatalks: Reflective Practice featuring Melaine Coward, not because verbal feedback is revolutionary, but because reflective conversations about practice remind us that teaching is, at its heart, relational work.

In a sector increasingly governed by metrics, that reminder feels quietly radical.

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