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
Grounded in Mohamed et al. (2020), Benjamin (2019), Noble (2018), Zembylas (2023), Crenshaw (1991)
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
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
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
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
