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A decolonial approach to AI in higher ed T&L

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A decolonial approach to AI in higher ed T&L

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and slides: https://share.google/m4UG4TGWCIEELAVPw

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A decolonial approach to AI in higher education teaching and learning

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GenAI is one of the most discussed topics in (higher) education at the moment. Its framing often focuses on creating policies and practices for responsible use based on GenAI literacy scales for example. Yet, these discourses are not neutral and come with underlying assumptions.

The main argument I would like to put forth through this resource is that GenAI in education is not merely a technological advancement that brings in innovation (as positive) and academic integrity challenges (as negative), but GenAI, when uncritically examined, can perpetuate a colonial gesture. To be critical about GenAI and even to be critical about critical GenAI, we need to dive into some of the less discussed topics to be sufficiently informed when deciding to adopt (or not) GenAI in (higher) education.

This resource situates itself within the domain of decolonial and critical literacies which does not just critique GenAI at the surface but aims to deeply interrogate its onto-epistemological grammar and underlying assumptions and processes in its 'content' production.

It attempts to support educators and students in critically examining the use of GenAI in education to be well-informed (including recognizing complicity in harm), be more response-able, and adopt a pedagogy of refusal to resist the uncritical adoption of GenAI (McGranahan, 2016), to maintain democratic freedom and the place of education as a public good (Delahunty, 2024).

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To the parties forming the government in the Senate and House of Representatives, as well as the outgoing administration,

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1. Coloniality, Eurocentrism, and the whiteness of (Gen)AI

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1. Coloniality, Eurocentrism, and the whiteness of (Gen)AI

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Cave and Dihal (2020) explore how artificial intelligence is overwhelmingly portrayed as White (in both physical appearance and cultural representation) reflecting and reinforcing racial hierarchies rooted in Western ideology.

As Alterator et al., (2025, p. 14) posits: “GenAI perpetuates colonial hegemony in the form of essentialised presentation of culture, misrepresentation of authorship or entirely fabricated authorship.”

Strait et al (2018) demonstrate that people more frequently objectify and stereotype robots racialized as Asian and Black, than they do of robots racialized as White, highlighting racism and sexism.

Far from being neutral tools, GenAI systems perpetuate colonial logics by reproducing whiteness, universalism, and a monoculture of the mind, effectively erasing diverse epistemologies and ways of knowing.

Mbah and Jarvis (2026) argue that GenAI even risks to create an epistemicide and call for a Decolonial AI Sovereignty (DAIS) model.

Zembylas (2021) explores the intersection of decolonial theory and artificial intelligence (AI) within the context of higher education. He argues that AI technologies, as currently developed and deployed, often reproduce colonial structures of power, privileging Eurocentric epistemologies while marginalizing other ways of knowing and being. This process of algorithmic coloniality poses ethical and pedagogical challenges for educators, researchers, and students.

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2. Complicity in exploitative practices 

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2. Complicity in exploitative practices 

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(Gen)AI also contributes to exploitative practices, including the extraction of data and labour from the Global South, reinforcing global inequalities.

Tacheva and Ramasubramanian (2023) examine the AI Empire as heteropatriarchy, racial capitalism, white supremacy, and coloniality. They posit that AI technologies like ChatGPT and DALL·E are not neutral tools but are embedded in and perpetuate harmful practices such as extractivism, automation, surveillance, and containment.

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3. Biases, whiteness, racialisation, ableism

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3. Biases, whiteness, racialisation, ableism

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Biases are often mentioned in guides about (Gen)AI but gesture briefly as considerations but not central problems, demonstrating that the status quo remains the one of White cis-hetero-able-bodied men.

For example Zou and Schiebinger (2018) exemplify this with various examples such as:

- Google Translate defaulting to male pronouns,

- facial recognition misinterpreting Asian faces,

- word embeddings associating European American names with positivity, and African American names with negativity.

De Meulder (2025) investigates the ableist assumptions and the systemic oppression of deaf people's language through their exclusion in (Gen)AI design.

Park (2024) examine AI-generated images and their harmful biases that reproduce and perpetuate Whiteness, and provides reflective questions to engage with within the context of Art Education (p. 44):

- "What do you see?

- What patterns do you identify?

- Who/what is present or dominant?

- Who/what is missing or misrepresented?

- How are these patterns tied to existing social norms or stereotypes in contemporary media culture?"

Furthermore, Bolukbasi et al. (2016) expose how word embeddings (vector representations of language used in machine learning) can reflect and even amplify societal gender biases. They demonstrate that embeddings trained on sources like Google News strongly associate certain professions and roles with specific genders (e.g., "receptionist" with "female").

Hoffman et al. (2024) argue that both representational harms (e.g., stereotyping people) and allocational harms (e.g., unequal distribution of resources and opportunities) are already underway and risk intensifying if covert bias isn’t urgently addressed in how we evaluate and train AI systems.

GenAI literacy and its 'literacy' are topics often positioned in relation to "a sense of urgency" -which is typical of the White supremacist culture (Okun, 2025)- promoting speed and blind adoption over reflection and purpose.

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4. Loss of relationality and subjectness

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4. Loss of relationality and subjectness

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Biesta (2016) argues that true teaching is a relational encounter that calls the student into subjectness through being addressed by the other (such as a [human] teacher), something that GenAI cannot support.

As such, GenAI is a tool that promotes self-objectification

and is deprived of relationality and human judgement (Corbin, Tai, and Flenady, 2025) that are at the core of educational encounters. Therefore, its (ab)use risks the disappearance of people's (teachers and students) ‘subjectness’.

Flenady and Sparrow (2025) argues that framing GenAI as a “study buddy” or “tutor” obscures its role as a mechanism of surveillance and conformity rather than liberation or support.

Furthermore, the authors posit that requiring students to take full responsibility for AI-generated content reflects a neoliberal, consumer-based model of education and undermines the communal, dialogic nature of scholarly learning.

The affordances of GenAi (e.g., speed, automation, adaptive) hail it as an efficient tool to provide feedback to students, yet AI-generated feedback is missing the social and emotional aspects of feedback by stressing only on scaling personalized comments and timeliness (Nazaretsky et al., 2024).

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In their article, Nyaaba, Wright and Choi (2024) argue that (Gen)AI in education risks reinforcing global inequalities by privileging Western languages, knowledge systems, and perspectives, thereby marginalizing indigenous practices and deepening divides between wealthy and poorer regions. It highlights how such dynamics can reproduce cultural imperialism and digital neocolonialism, while calling for reforms that prioritize cultural diversity, equity, and local participation in shaping AI systems.

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Ardana and Christiana (2026) argue that AI detectors act as ‘affective engine of control' through 'algorithmic surveillance' which alienates students and exacerbates 'linguistic injustice' while enabling a ‘pedagogical abdication’ by teachers.

Trust and mistrust at the core of discussion of GenAI in higher education with the assumptions that students will cheat, be irresponsible and therefore needs to be taught to use GenAI responsibly (while teachers do not have to think about this) and controlled/monitored…

AI detectors, based on suspicion and mistrust, turns classrooms into surveillance spaces, and teach students to cheat better or even to write worse to avoid being flagged (Giray, Roe and Diesta Espiritu, 2026).

Luo (2025) reveals how power asymmetries and one-way transparency erode student trust. Through concept mapping and interviews, the study finds that students often feel surveilled, fearful of being wrongly accused of cheating, and hesitant to explore AI tools due to unclear policies and punitive communication from teachers. While students are required to be transparent about their AI use, teachers rarely reciprocate by explaining how AI-influenced work is assessed, reinforcing top-down control. The study highlights the need for two-way transparency, improved teacher AI literacy, supportive policy communication, and collaborative policy design to foster trust and empower students in this evolving educational landscape. Luo advocates a shift from functional to relational approaches in AI policy and pedagogy, emphasizing trust as essential for navigating the uncertainties of AI-mediated learning.

Furthermore, Jafry and Vorstermans (2024) highlight that some students do not want to use AI and do not want to develop a dependency towards it. This perspective is often overlooked since (Gen)AI is largely positioned as a 'hype tool' that is inevitable to engage with in education.

The blind use of and uncritical engagement with GenAI in education reflect aspects of an Orwelian society by positioning students as objects of surveillance and control, not as autonomous subjects, with little real capacity to consent or opt out. Even when institutions claim transparency, the structural coercion of educational settings makes refusal nearly impossible, therefore manufacturing consent.

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The following themes and questions support Higher Education administrators and teachers in engaging critically with critical GenAI concepts.

Epistemology & Knowledge

What kinds of knowledge, histories, and ways of knowing are being excluded in our use or teaching of GenAI?

How does our institution reinforce or challenge the myth of GenAI's neutrality?

Neo-Coloniality & Imperialism in GenAI

Based on Nyaaba, Wright and Choi (2024):

Whose knowledge is being centered, and whose is being marginalized by the data that train GenAI tools used in education?

How might these tools reproduce Eurocentric epistemologies and silence Indigenous, local, or non-Western ways of knowing?

In what ways can educators actively resist and disrupt the neocolonial power dynamics embedded within GenAI systems?

How might teachers design pedagogical practices that use GenAI critically—not just as a technological aid, but as a site of analysis and contestation?

To what extent does reliance on GenAI in educational research risk reinforcing colonial hierarchies of knowledge production?

How might scholars employ liberatory and human-centric design methods to reclaim autonomy over the narratives and data that shape educational futures?

How can students be positioned not merely as users of GenAI, but as critical co-creators who interrogate and reshape its biases?

What pedagogical strategies enable learners—particularly from marginalized communities—to use GenAI as a lens for critiquing power, culture, and representation in digital knowledge systems?

Based on Jafry and Vorstermans (2024)

How do AI tools store and use information?

How are AI tools managed?

Who beneﬁts or is disadvantaged by AI tools?

What are the challenges and opportunities in AI-based research and decision-making?

How can AI be beneﬁcial, particularly for students?

Who is leading the discourse and development of AI, and which voices are missing or underrepresented?

(Un)critical AI (Il)literacy

The concept of AI lietracy is not neutral and often tends to focus on the equipment logics (technical skills such as prompting). While often claiming to also develop 'critical thinking', AI literacy paradoxically does not address criticality deeply and tends to maintain the status quo.

How can students and educators recognize when GenAI produces false or misleading information, and what social or cultural assumptions shape what is presented as “truth”?
How does this challenge our understanding of authority and knowledge in digital learning spaces?

If GenAI outputs are never neutral, whose perspectives and values are being normalized through its design, and whose are being erased or assimilated?
How might this process reinforce a monoculture of the mind where diverse epistemologies are suppressed?

Who benefits economically and politically from the widespread adoption of GenAI tools in education?
In what ways do market-driven interests influence what knowledge is prioritized, standardized, or commodified as “AI literacy”?

What cultural narratives, histories, or identities are centered in GenAI-generated content, and what or who is only briefly featured, distorted, or omitted altogether?
How can educators and students use these absences as a starting point for critical inquiry?

To what extent does the illusion of GenAI “objectivity” mask its role in reproducing colonial or capitalist knowledge hierarchies?
How can critical GenAI literacy empower learners to see through hallucinations not only as technical errors, but as reflections of systemic bias and epistemic exclusion?

Does a lack of prompting skill or a refusal to use GenAI automatically render students or educators “AI illiterate”?
What assumptions about technological participation, progress, and value are embedded in labeling certain groups as “illiterate,” and how might this reproduce deficit views of resistance or alternative epistemologies?

Agency, Power, Decision-Making

How is consent obtained and maintained in learning environments that rely on data?

Are students and educators empowered to opt-out or question GenAI systems?

Who is shaping GenAI (and GenAI literacy) curricula and policies? Do they reflect diverse lived experiences and epistemologies?

How can we maintain students’ and teachers’ place as free subjects and not objects of other’s control?

Data, Metrics, and Surveillance

What is being measured, and why? Who benefits from these metrics?

How might we resist or redesign data systems to reflect care, context, and justice?

How is the GenAI data collected, processed and used? And are users (students, teachers) sufficiently informed about these aspects as well as the location of the data centres?

How do we ensure relational trust? How do we avoid the slippery slope of punitive tones, policing students (e.g. random checks) and surveilling students’ (mis)use?

Sustainability & Justice

Are we critically engaging with the environmental and labor costs of GenAI?

Are students and educators truly informed and able to critically examine the environment and social issues in depth surrounding AI use? (not just the obvious ones)

What policies exist to ensure our GenAI choices do not exacerbate global inequalities?

How are GenAI tools assessed in terms of their ethical conduct and contribution to harmful or violent practices?

How can we ensure that we are adequately informed about the environmental and social costs of our use of GenAI in education to make informed decisions of its use?

Hidden curriculum, Inclusion & Representation

What underlying values, norms, and worldviews are reinforced through the implementation of GenAI and the ways AI literacy is taught?
How might these practices reproduce dominant ideologies such as Whiteness, Eurocentrism, heteronormativity, or able-bodiedness under the guise of neutrality or innovation?

Which ways of being, knowing, and expressing knowledge are marginalized or rendered invisible within GenAI-driven educational spaces?
In what ways do claims of “objectivity” or “universal knowledge” obscure plural, embodied, Indigenous, or storied epistemologies that challenge Western ontological assumptions?

Assessment

Are we only focusing on the production of learning outcomes and measurable outputs by students (following a performative logic) or are we engaging students in problem-posing tasks that connect to themselves and their (future) impact in the world?

How can we ensure that we do not return to pen and paper of controlling and homogeneous/standardizing ways of measuring learning that are exclusionary?

How might we rethink the ‘busy’ assessment tasks ‘given’ to students and move towards co-created assessments?

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Signing the open letter: stop the uncritical adoption of AI technologies in academia

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Signing the open letter: stop the uncritical adoption of AI technologies in academia

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Stop the uncritical adoption of AI technologies in academia [link]

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The AI Manyfesto is a collaborative declaration envisioning a decolonial future for artificial intelligence. It challenges the dominance of Western, male-centric narratives in AI development and calls for inclusive, pluralistic approaches that honor diverse cultural perspectives.

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This manyfesto is a provocation, a question, an opening, a dance about a future of AI technologies that is decolonial. We call it manyfesto, since it reflects some visions among many, and we hope to invite exchange, conversation, and the development of statements from people affected by AI technology.

We begin with the challenge posed by the language we use to talk about AI: language that has emerged, as much of the technology has, dominated by Western male voices, whiteness, and wealth. We seek to uncover, to question, upend, and reinvent the assumptions underlying this language, even as we use it.

"Artificial" and "intelligence" are loaded terms, their definitions subject to cultural biases. AI is a technology, a science, a business, a knowledge system, a set of narratives, of relationships, an imaginary. Across each facet, our effort is to undo the colonial erasure of non-Western ways of being and knowing. The word "decoloniality," too, resonates differently in different communities, including with Indigenous peoples and those for whom colonialism is not a history but a present reality. Some reject the term decolonial in this context. We acknowledge both its use and its rejection.

We do not seek consensus: we value human difference. We reject the idea that any one framework could rule globally. We reject the Western-normative language of "ethical" AI and suggestions of "inclusivity" that do not destabilize current patterns of domination and address power asymmetries. We reject as half-measures any principles meant to tweak, reinforce, and whitewash the status quo, merely blunting its devastation. They fail to acknowledge how the social and the technical are interwoven, and technologies have immaterial as well as material impacts over specific gendered, racialized bodies and territories. Decoloniality rejects the divorcing of the material and immaterial, of feeling from being, knowing, doing or living.

Notions of decolonial governances will emerge from community and situated contexts, questioning what currently constitutes hegemonic narratives. Decolonialilty is not merely diversity and inclusion; removing the echoes of coloniality in AI will require reparations for present and past material and epistemic injustice and dispossession. These reinventions of AI governance will acknowledge the expertise that comes from lived experience, and create new pathways to make it possible for those who have historically been marginalized to have the opportunity to decide and build their own dignified socio-technical futures. Decolonial governance will recognize, in a way that Western-centric governance structures historically have not, how our destinies are intertwined. We owe each other our mutual futures.

Our humanity is relational, defined by how we are tied to one another. Technology has an important role in those relationships. Creation, art, stories and sensitive experience are some of the paths that we must explore in order to foster the decolonial imagination. We seek to center the engineering, design, knowledge-production, and dispute-resolution practices of diverse cultures, which are embedded with their own value systems.

Our urgency arises from humans' capacity to use AI as a knowledge system to create irrefutable "algorithmic truths" to reinforce domination. In doing so, other systems of knowledge production and other visions are denied and erased, as are other peoples' agency, autonomy, and contestation. In this way AI coloniality extends beyond data colonialism: AI implies material extractivism, and the use of AI has the capacity to shape reality. Designed in an unequal society, these systems can be employed to reproduce those inequalities. Built with an emphasis on efficiency rather than dignity, they can do irreparable harm. In insisting on a decolonial AI, we stand for the right of each historically marginalized community to reshape reality on their terms.

Our methods will evolve, sensitive to needs and opportunities, but our aim is to create and hold a resonant forum for learning and exchange from and between voices silenced by colonialist structures and the coloniality in force through socio-technical systems.

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This manyfesto is a provocation, a question, an opening, a dance about a future of AI technologies that is decolonial. We call it manyfesto, since it reflects some visions among many, and we hope to invite exchange, conversation, and the development of statements from people affected by AI technology.

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Adopting relational practices as forms of resistance

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Adopting relational practices as forms of resistance

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Gourlay (2022) challenges the perceived inevitability of surveillance and datafication in academia. She calls for resistance to these reductive practices and advocates preserving the rich, diverse, and often invisible aspects of academic life that metrics and algorithms tend to erase.

Zembylas (2021) calls for resisting digital neocolonialism and reshaping AI toward a more equitable future.

Adopt a pedagogy of refusal -as elaborated by Eggert, Hagen, and Turner (2025) and McGranahan (2016)- to resist simply following the crowd or the 'hype' and truly maintaining people's place a free subjects and not as objects of control.

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As Zembylas (2025) describes, Mignolo defines 'pluriversality' as diverse ways of knowing as necessary for countering Eurocentrism and dismantling (neo)colonial structures of knowledge production.

Instead of homogeneity and 'one-size-fits-all' policy design that are rarely co-created with the people subject to it (i.e. students) and often accompanied with punitive tone, how might we 'pluriversalise' our policies?

Can we be more attentive to the underlying assumptions we make in these texts, and the power asymmetries that are created or maintained. For example, Luo (2025) demonstrates that in GenAI policies, students are the only ones who need to disclose the ways they use AI, but teachers do not have to be transparent, which erodes trust.

It is possible, as noted by Shahjahan (2016) to reimagine more equitable and pluralistic approaches to knowledge and policy-making in higher education.

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Debiasing and decolonizing the AI Empire

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Debiasing and decolonizing the AI Empire

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Zou and Schiebinger (2018) argue that achieving fairness in AI requires more than technical fixes. It demands interdisciplinary collaboration among computer scientists, ethicists, social scientists, and others. They emphasize the importance of questioning what fairness should mean in AI contexts and stress that education must include both technical and social dimensions of AI. Ultimately, the article calls for proactive efforts to ensure AI does not perpetuate or deepen existing social inequalities.

Bolukbasi et al. (2016) advocate for the widespread use of debiased embeddings to prevent the reinforcement of stereotypes and call for hard and soft debiasing, distinguishing hard and soft debiasing:

Hard debiasing removes gender associations from gender-neutral words while preserving necessary gender distinctions (e.g., maintaining the relation “she:grandmother :: he:grandfather”).

Soft debiasing balances bias reduction with the preservation of original relationships for use in more nuanced applications.

Tacheva and Ramasubramanian (2023) reject the notion that AI can be reformed from within these oppressive systems, warning that superficial fixes only reinforce existing harm. Instead, they call for a radical, bottom-up transformation led by those most affected, highlighting recent collective resistance efforts, such as the unionization of African content moderators. Their conclusion insists that true justice and technological liberation can only be achieved through intersectional, transnational organizing that dismantles the foundational power structures of the AI Empire.

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Repurpose, Rebuild, Refuse (Beetham, 2025)

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Repurpose, Rebuild, Refuse (Beetham, 2025)

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A blog by Beetham (2025) proposes three R (Repurpose, Rebuild, Refuse) with some concrete steps such as:

Repurpose:

Deliver learning activities with/without AI that reveal its fragilities, fabrications and breaking points

Teach and actively support counter-hegemonic data projects: feminist, decolonial, minority-cultural, public and sustainable projects, projects of data sovereignty and data justice

Encourage slow scholarship and slow learning with AI, refusing speed and productivity to focus on quality and interpretability;

Rebuild:

Create and protect spaces for learning that do not involve data systems, such as hands-on making and investigating, live writing (not necessarily assessed), active experimentation, sensory feedback, discussing and debating, live (unrecorded) performance;

Secure education policy and the training of teachers from the influence of AI lobbyists and AI-funded researchers;

Redesign assessments away from instrumental outcomes and standardised rubrics; negotiate assessment tasks with students;

Refuse:

Refuse to use systems or architectures that may be harmful, biased, unsustainable or unjust;

Refuse to provide or collect data that may be used to build harmful data architectures or to make harmful discriminations concerning learners and educators;

Refuse to adapt educational workflows and conditions of work to the use of AI, particularly where this will render education workers overworked, more precarious or less skilled;

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Open letter: stop the uncritical adoption of AI technologies in academia

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Open letter: stop the uncritical adoption of AI technologies in academia

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Open Letter: Stop the Uncritical Adoption of AI Technologies in Academia

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Dear Universities of The Netherlands, Dutch Universities of Applied Sciences, and Respective Executive Boards,

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Metaphors of AI in Education: Discourses, Histories and Practices | Journal of Interactive Media in Education

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(watch) recording: assurance of learning in the age of GenAI: Can assessment for inclusion and security co-exist?

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(watch) recording: assurance of learning in the age of GenAI: Can assessment for inclusion and security co-exist?

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Assurance of learning in the age of Gen AI: Can assessment for inclusion and security co-exist?

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James Muldoon: 'Artificial Intelligence in the Colonial Matrix of Power'

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Video: https://www.youtube.com/watch?v=jsrz_JCeBnE&t=59s&ab_channel=DecolonialFuturesCollective

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AI & Systems Awareness Seminar: Vanessa de Oliveira Andreotti

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"I argue for the concept of “techno-transference”, by which I mean the transfer of relational and affective expectations on to generative systems. Without the humanities, we risk training a generation to inhabit a Wild West in which the algorithm is the only law and no one is left to interpret the fallout."

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Government renews drive to attract international academics with more recruits coming to UK under Global Talent Fund

More people sceptical that science makes life better compared with pre-pandemic, and public also doubting contribution to economic growth

Large numbers of editors publishing in their own special issue attacked as 'academic narcissism' and 'scientific misconduct' in first-of-its-kind study

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Humanities cuts leave us defenceless in the age of AI

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It is vital to examine what chatbots' behaviour reveals about their underlying structures - and human responses to them, says Agnieszka Piotrowska

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To Read - Decolonising AI: A Critical Approach to Education and

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To Read - Decolonising AI: A Critical Approach to Education and

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Social Justice

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How To Argue With Me About AI, If You Must

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If you insist we have a conversation, please come prepared.

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View of Assessment after Artificial Intelligence: The Research We Should Be Doing

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Return to Article Details Assessment after Artificial Intelligence: The Research We Should Be Doing

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Developing Stamina for Decolonizing Higher Education [Workbook]

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Developing Stamina for Decolonizing Higher Education [Workbook]

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A Little Book About AI That Is Not Really About AI

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New study finds that ChatGPT amplifies global inequalities |

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New research from the Oxford Internet Institute at the University of Oxford, and the University of Kentucky, finds that ChatGPT systematically favours wealthier, Western regions in response to

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The "AI" Rosary. Each and every one of its beans is leaving you behind. The "AI" Rosary is the one that is here to stay, contrary to what broligarchs, techno-illusionists, and "AI" enthusiasts want… | Dagmar Monett

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The “AI” Rosary. Each and every one of its beans is leaving you behind. The “AI” Rosary is the one that is here to stay, contrary to what broligarchs, techno-illusionists, and “AI” enthusiasts want you to believe. I used that slide in one talk this week, and just updated and included it in the slide deck for the next talk I’ll be giving next week. The “AI” Rosary might not be enough for some, though, especially for those of the sort “But, but, but <insert whatever you wish about wishful mnemonics, or techno-solutionism, or techno-determinism, or AI hype here>!” 🤷‍♀️ (Even the vibe coder-in-chief is paddling backwards now, I have heard? That must be real progress!) ____
Guest, O., Suarez, M., Müller, B., van Meerkerk, E., Oude Groote Beverborg, A., de Haan, R., Reyes Elizondo, A., Blokpoel, M., Scharfenberg, N., Kleinherenbrink, A., Camerino, I., Woensdregt, M., Monett, D., Brown, J., Avraamidou, L., Alenda-Demoutiez, J., Hermans, F., & van Rooij, I. (2025). Against the Uncritical Adoption of ‘AI’ Technologies in Academia. Zenodo. https://lnkd.in/d9mPkpaQ Monett, D.,& Grigorescu, B. (2024). Deconstructing the AI Myth: Fallacies and Harms of Algorithmification. In Proceedings of the 23rd European Conference on e-Learning, ECEL 2024, 23(1), 242-248. https://lnkd.in/dJEjZhaU Monett, D., & Paquet, G. (2025). Against the Commodification of Education — if harms then not AI. Journal of Open, Distance, and Digital Education, 2(1), 1-24. https://lnkd.in/dXai2rUa

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'Resisting AI' with Dan McQuillan | Critical AI Talks

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Critical conversation with Christine Moser

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Critical conversation with Christine Moser

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AI and the Future of Work: A conversation with Christine Moser (VU Amsterdam)

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Technocolonialism: When Technology for Good is Harmful

Mirca Madianou

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Based on this article (2025), clinicians report that prolonged AI interaction can induced psychosis with episodes such as grandiose delusions, paranoia, disassociation, and compulsive engagement.

Centre for Countering Digital Hate (2025) conducted a large study that found that 53% of ChatGPT responses were harmful and 47% of these contained follow-up suggestions, some encouraging further engagement on harmful topics (self-harm and suicide, substance abuse, and eating disorders).

Because GenAI is so white, it creates a sociogeny (socially constructed phenomenon with biological effects) of racism that touch everyone and reproduces Fanon's (2008) 'white mask'. For example, it reinforces stereotypes, erases non dominant ways of knowing, and it creates anxieties, social isolation and dependence. It’s problematic for everyone to create homogeneous ‘knowledge’ based on an hegemonic worldview (White and Western) but particularly harmful for students of color or other minoritized groups, whose identities are not recognized, are devalued, and are erased.

A recent article at MIT by Chandra et al. (2026 - still needs to be peer-reviewed at the time I am writing this) showcases that sycophantic models like Chatbots cause delusional spiraling. It's not anecdotal, it's coded within the algorithm as and thus harmful for users, and particularly concerning for vulnerable groups like children.

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Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians

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Max Kleiman-Weiner University of Washington, Seattle Jonathan Ragan-Kelley MIT CSAIL Joshua B. Tenenbaum MIT Department of Brain & Cognitive Sciences

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Predatory aspects of ChatGPT (teens and mental health)

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Predatory aspects of ChatGPT (teens and mental health)

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AI-Induced Psychosis: Understanding Risks of Chatbot Overuse

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Explore the mental health risks of prolonged AI chatbot use, including psychosis-like symptoms. Learn how to set boundaries and seek professional support.

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How did we end up threatening our kids’ lives with AI? - Anil Dash

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A blog about making culture. Since 1999.

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Manifesto on feedback in the age of GenAI

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Manifesto on feedback in the age of GenAI

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Manifesto here

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CRADLE Seminar Series 2026 #1: Manifesto for feedback in the age of AI

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perspectives on sustainability

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¹ Department of Cognitive Science and Artificial Intelligence, Radboud University

² Group of Communication Science, Behavioural Science Institute, Radboud University

"With a devastating ecological footprint and the exploitation of hidden labour, hyped-AI serves to amplify discrimination and other social, economic and environmental injustices." - van Rooij & Guest (2025)

Radboud University envisions a greater role for 'AI' (short for Artificial Intelligence) in its new mission. This is cause for reflection, to say the least, since Radboud University is committed to contributing to a sustainable and equitable world through cultivating critical thinking. Currently popular, widespread, and hyped technologies are in great tension with these stated values, and thus a focus on AI seems to imply giving up on our values. In this short blogpost, we illustrate just how deep these problems go.

The AI hype promoted by tech corporations resulted in the global expansion of hyper-scale data centers. These facilities' extensive computational infrastructure requires intensive use of resources such as electricity, water, labor, and land to develop their generative image models, which produce synthetic images, and their large language models (LLMs), which do the same for text. Since tech companies are not reporting their energy and water, experts now warn of the increasing environmental harms caused by AI, with a critical study estimating that AI industries could consume as much energy as the Netherlands by 2027. Meanwhile, and alarmingly, companies are reportedly investing in reopening nuclear power facilities to cover the great amount of energy that AI technologies will require in the coming years. Some companies are even using methane gas illegally.

Sadly, energy is not the only public good whose sustainability is compromised due to AI hype: water is indispensable for data centers, too. Millions of liters of water are needed to cool the extensive computational infrastructures on which LLMs are developed and used. One study notes that writing a 100-word email with AI technologies consumes half a liter of water and enough energy to fully charge seven mobile telephones. Another study estimates that by 2027, the AI industry will use half the amount of water as does the United Kingdom annually. However, there are further hidden environmental costs of water, energy, and other minerals and metals in the AI supply chain, not to mention the electronic garbage that ends up in countries of the Global South.

These social and environmental harms of AI technologies are related to the very material consequences in the territories that sustain the data centers supporting systems like LLMs. Across the globe, from Mexico to Taiwan, communities living next to the data centers report electricity outages, light pollution, and water scarcity. This is particularly worrying in the Global South which is rich in resources and so attracts predation from AI companies. These corporations are intensifying the extractivist business model, which runs on extant colonial lines, to secure AI computational power. This is to say that natural and human resources are stolen from local territories, leading to scarcity, displacement, and violence.

In spite of its enormous resource cost, AI technologies are being pushed on society without critical reflection: in education, government, banking, science, journalism, police and military forces, across a wide range of applications, such as browsers, video calls, and other software. Supposedly these applications promise to help us in some way by making us work more efficiently and effectively. However, AI technologies reinforce social inequalities and harms. These technologies are increasingly involved, for example, in allegations of racism, sexism, plagiarism, violations of copyright, and professional integrity, just to mention a few.

Although claimed otherwise, AI technologies are never gratis: they come at a high cost for our planet and society, and even more so for communities that have been historically oppressed by colonial power structures. Despite gradually increasing critique of issues surrounding sustainability of AI in the media, much public confusion and misinformation persists. This is caused in part by the AI industry, on the one hand, aggressively lobbying against regulation and, on the other hand, self-describing as "green" and as working towards sustainability - a maneuver known as greenwashing. Using this contradiction, these companies obscure their energy and water consumption in data centers and their societal and political harms. AI technologies are thus presented as indispensable to society, suggesting that only with the help of AI technologies we are able to fight, for example, the climate crisis - also known as technosolutionism. This even goes as far as suggestions that we all need to sacrifice our planet for unrealistic promises of the AI hype. Obviously, there is no planet B, and the Earth is more important than AI technologies with high environmental and social costs.

Radboud University can no longer avoid critically reflecting on AI. We need to raise awareness of how these technologies harm people and ruin our planet. When we critique AI, we should do so with intellectual honesty and in a principled way. Utmost care is needed to avoid ethics washing, greenwashing, and generally - what we dub - critical washing. Critical reflection cannot be reduced to a mere 'check box' before unleashing AI technologies in our daily life after all (as has been done in other areas such as vaping and combustion engines). Researching and reflecting on the harms of AI is not itself harm reduction. It may even contribute to rationalizing, normalizing, and enabling harm. Critical reflection without appropriate action is thus quintessentially critical washing.

Universities are not spokespersons for the AI industry. On the contrary, we need to resist being coopted and corrupted by the industries' agendas. This requires deep commitment to the mission of universities, our academic values, our scientific integrity, and our critical scholarship - whether one is a working academic or administrative and support staff, we all have a part to play to protect and uphold these values and standards. Universities have the responsibility not to blindly adopt AI in research, education for staff and students, and for administration under the guise of helping society to move forward and prosper, and of preparing students and staff for an imagined future. We do not have to limit ourselves to the AI industry's or the UN's and EU's legal frameworks for a shared vision on our ethical, ecological, and social responsibilities: these have failed us before. Instead, universities need to take on the mantle of a critical voice and of knowledge producers. We need to increase Critical AI Literacy in students, in scientists, and in other professionals. This is the role that universities are duty bound to play in times of planetary crises.

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Critical AI Literacy: Beyond hegemonic perspectives on sustainability

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How can universities resist being coopted and corrupted by the AI industries' agendas?

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Manifesto on Feedback in the age of GenAI

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Manifesto on Feedback in the age of GenAI

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CRADLE Seminar Series 2026 #1: Manifesto for feedback in the age of AI

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Lund University School of Economics and Management

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NEXUS AI Symposium: If harms then not AI else caution

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Creepy propaganda by cogniti (Panopticon vibe...)

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Creepy propaganda by cogniti (Panopticon vibe...)

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What We Must Do About AI In Education – GenAI:N3

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The ethics of AI in education - Education Technology Society

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There is growing talk about ‘AI ethics’ in education. We talk to Michał Wieczorek (University College Dublin) about how to think about tech ethics in a philosophically-grounded manner, and how much of the current push for AI in education is e…

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'Resisting AI' with Dan McQuillan | Critical AI Talks

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8. Threat to academic integrity and authoring (work in progress)

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8. Threat to academic integrity and authoring (work in progress)

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Ardana and Christiana (2026) ...

GenAI detectors punish multilingual students the most (Giray, Roe and Diesta Espiritu, 2026).

While some researchers legitimize the use of AI for research, such as data analysis, a highly cited paper by Jowsey et al. (2025) completely rejects the use of AI in Reflexive Thematic Analysis (a dominant method in qualitative research).

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Hollowed, with Olivia Guest, Iris van Rooij and Andrea Reyes Elizondo | The Data Fix with Dr. Mél Hogan

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a lefty pod about perpetual tech promises

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The role of the University is to resist AI

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This is the text of a seminar given at the Goldsmiths Centre for Philosophy and Critical Thought on June 11th 2025

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Degenerative AI in education [Blog by Williamson, 2023]

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Degenerative AI in education [Blog by Williamson, 2023]

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Guersenzvaig and Monett (2026) describe how offloading responsibility to users (teachers/students) enables ethics washing and propose the term 'Responsible Realism' to explain ...

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Re using degenerative "AI" in qualitative research, if you asked me, I have zero, nada, nichts, nothing possitive to tell.

I'd suggest you to read (no "AI" mediated, but read read) e.g. these works first, instead:

Levitt, H. M. (2026). A consideration of the ethics and methodological integrity of generative artificial intelligence in qualitative research: Guidelines for Qualitative Psychology [Editorial]. Qualitative Psychology, 13(1), 1–5. https://lnkd.in/dzjuf82c

Jowsey, T., Braun, V., Clarke, V., Lupton, D., Fine, M. (2025). We Reject the Use of Generative Artificial Intelligence for Reflexive Qualitative Research. Qualitative Inquiry, 0(0). https://lnkd.in/deANGUj6
Preprint available at SSRN: https://lnkd.in/dt4wK2mZ

Nguyen, D. C., Welch, C. (2025). Engaged and Responsible Scholarship: Why Qualitative Researchers Should Not Embrace GenAI. Business & Society, 0(0). https://lnkd.in/dzNbKW2K

Nguyen, D. C., Welch, C. (2025). Generative Artificial Intelligence in Qualitative Data Analysis: Analyzing—Or Just Chatting? Organizational Research Methods, 0(0). https://lnkd.in/dDKa2wWM

Radu, R., Rocher, L. (2026). Attributing and situating knowledge cannot be left to language models. Nature Machine Intelligence. https://lnkd.in/d8-_sYtB
Preprint: https://lnkd.in/dK3uJU6Q

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Against the uncritical adoption of AI in academia

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Against the uncritical adoption of AI in academia

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Against the Uncritical Adoption of ‘AI’ Technologies in Academia

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Under the banner of progress, products have been uncritically adopted or even imposed on users—in past centuries with tobacco and combustion engines, and in the 21st with social media. Currently, we are similarly entangled with artificial intelligence (AI) technology. For example, software updates…

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Resisting Dehumanization in the Age of 'AI'

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Resisting Dehumanization in the Age of 'AI'

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Resisting Dehumanization in the Age of "AI": The View from the Humanities, Emily M. Bender

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An illustrated guide to resisting "AI is inevitable" in education

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An illustrated guide to resisting "AI is inevitable" in education

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An illustrated guide to resisting “AI is inevitable” in education

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Dynamic tensions: an AI-assisted critical scoping review of university students' qualitative experiences of GenAI

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Dynamic tensions: an AI-assisted critical scoping review of university students' qualitative experiences of GenAI

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Dynamic tensions: an AI-assisted critical scoping review of university students’ qualitative experiences of GenAI

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Purpose. This study presents a GenAI-assisted scoping review of interview-based studies exploring student experiences and perceptions of generative AI (GenAI) in international higher education. It aims to synthesise emerging insights into the implications of GenAI as a research tool in contrast to its use by students.Design/methodology/approach. An AI-assisted scoping review was conducted of 39 qualitative interview studies from 20 countries, all published in 2023/24. Studies were identified through an extensive Boolean search of Web of Science (Arksey and O’Malley, 2003). We conducted in-depth readings, compiled bibliometric data and uploaded findings to Claude Projects for analysis. Thematic analysis (Braun and Clarke, 2006) identified patterns across studies, alongside a review of methodological and theoretical approaches.Findings. The review highlights diverse analytical and theoretical framings of GenAI, ranging from technology acceptance models to student agency and motivation. Three key perceived benefits and three perceived drawbacks were identified. These represent dynamic tensions surrounding its use in higher education, providing insight into the evolving methodological landscape of GenAI-related research.Research limitations/implications. Findings are limited to interview-based studies from a defined time period and by the need to rigorously check GenAI contributions to the analysis process. The ambitious scope of the paper lead to a challenge in bringing together the two research strands while maintaining necessary detail and nuance of the findings. Despite this, the findings have significant potential encouraging international collaboration with students and researchers to develop an ethical methodological approach to GenAI use for literature reviews.Originality/value. This study offers a unique meta-perspective on the place of GenAI in higher education, contrasting student experiences with the process of its use for academic research. It underscores the rapidly evolving nature of GenAI, highlighting the fundamental dynamic tensions of GenAI in higher education and research. It explores whether GenAI is augmenting the human role or eroding it all together.

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Data Workers’ Inquiry - Data Workers’ Inquiry

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Inoculating citizens against sycophancy in large language models

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Inoculating citizens against sycophancy in large language models

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The Horrible Economics of AI Are Starting to Come Crashing Down

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The Horrible Economics of AI Are Starting to Come Crashing Down

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The Horrible Economics of AI Are Starting to Come Crashing Down

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As costs continue to ramp up, the AI industry’s enterprise customers could soon be left holding the bag due to higher prices.

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AI is a Morbid Symptom of the Interregnum

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AI is a Morbid Symptom of the Interregnum

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Dan McQuillan - AI is a Morbid Symptom of the Interregnum - 2025/2027 Rogers Chair Series

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By Dagmar Monett (LinkedIn post, April 2026)

Coining "eduslop" here, 🙋‍♀️! eduslop (noun) The amount of content (including, but not limited to, teaching materials, lesson plans, essays, homework, theses, exercises, guidelines, syllabi, feedback, assessments, etc.) that is "created" by using degenerative "AI" in educational contexts. Examples: - Robert used eduslop in one third of his course slides. - Anna shared her eduslop with other peers in class. eduslop (verb) The act of "creating" slop by using degenerative "AI" in educational contexts. Examples: - The teacher eduslopped his syllabus successfully. - John eduslops the essay minutes before the deadline is due, and doesn't need to invest time writing. Hallelujah! ___ Any other examples you want to add?!

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Bosses say AI boosts productivity – workers say they’re drowning in ‘workslop’ | Dagmar Monett | 18 comments

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Coining “eduslop” here, 🙋‍♀️! eduslop (noun)
The amount of content (including, but not limited to, teaching materials, lesson plans, essays, homework, theses, exercises, guidelines, syllabi, feedback, assessments, etc.) that is “created” by using degenerative “AI” in educational contexts.
Examples:
- Robert used eduslop in one third of his course slides.
- Anna shared her eduslop with other peers in class. eduslop (verb)
The act of “creating” slop by using degenerative “AI” in educational contexts.
Examples:
- The teacher eduslopped his syllabus successfully.
- John eduslops the essay minutes before the deadline is due, and doesn’t need to invest time writing. Hallelujah! ___
Any other examples you want to add?! | 18 comments on LinkedIn

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Bosses say AI boosts productivity – workers say they’re drowning in ‘workslop’

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Workslop refers to AI-generated work that seems polished but is flawed and in need of heavy corrections

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Why I object to and Reject to GenAI [Blog by Deborah Lupton]

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Why I object to and Reject to GenAI [Blog by Deborah Lupton]

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Amnesty: Global: Enormous data pipelines powering major generative AI systems are rooted in mass invasions of privacy by design

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Amnesty: Global: Enormous data pipelines powering major generative AI systems are rooted in mass invasions of privacy by design

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Global: Enormous data pipelines powering major generative AI systems are rooted in mass invasions of privacy by design

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Companies are extracting vast troves of online data through unlawful web scraping to build their generative artificial intelligence (AI) products in a way that is enabling a mass invasion of privacy, making these systems unlawful by design, Amnesty International said in a new briefing today. Unlawful by Design: Exposing the Human Rights Costs of Generative AI documents serious risks […]

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TPACK in the Age of Context and Artificial Intelligence: Punya Mishra on the “Wicked Problem” of AI and Why Algorithms Can’t Replace Context

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TPACK in the Age of Context and Artificial Intelligence: Punya Mishra on the “Wicked Problem” of AI and Why Algorithms Can’t Replace Context

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AI is like a "“smart drunk intern,” it’s both incredibly capable and deeply unreliable"

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Introduction

Teaching is widely recognized as a complex, ill-structured discipline that requires educators to navigate dynamic classroom environments and apply specialized structures of knowledge across varying contexts (Koehler & Mishra, 2009). For decades, the integration of technology into this mix was often hampered by a "technocentric" approach; one that viewed technology as a set of isolated skills to be mastered independent of subject matter or instructional method (Bozkurt, 2020). This reductionist view failed to account for the protean, unstable, and opaque nature of modern digital technologies, which, unlike the transparent stability of a pencil or chalkboard, present fundamental challenges to educators (Koehler & Mishra, 2009; Mishra & Koehler, 2006).

To address this disconnect, the Technological Pedagogical Content Knowledge (TPACK) framework was introduced as a conceptual lens to capture the essential qualities of teacher knowledge required for thoughtful technology integration. Building upon Shulman's (1986) formulation of Pedagogical Content Knowledge (PCK), TPACK argues that effective teaching with technology is not merely about adding technical skills to existing practice (Koehler & Mishra, 2005). Rather, it requires a nuanced understanding of the complex relationships between technology, content, and pedagogy (Mishra & Koehler, 2006).

At its core, the framework identifies three primary forms of knowledge: Content Knowledge (CK), the subject matter to be learned; Pedagogical Knowledge (PK), the methods and processes of teaching; and Technological Knowledge (TK), the understanding of standard and advanced technologies. However, the framework's true power lies in the intersections of these domains. It highlights that true expertise emerges from the transactional relationships between them, such as Technological Pedagogical Knowledge (TPK), knowing how teaching changes when specific technologies are used, and Technological Content Knowledge (TCK), understanding how technology creates new representations of content (Koehler et al., 2013).

From Digital Transformation to Contextual Knowledge (XK)

As the educational landscape has shifted from basic digitization to profound digital transformation, the TPACK framework has faced scrutiny regarding its treatment of context. Early iterations of the model acknowledged context with a dotted line surrounding the core knowledge domains, symbolizing the external factors influencing teaching (Mishra & Koehler, 2006). However, critics and evolving research have argued that this representation was insufficient for capturing the situated nature of teacher expertise (Petko et al., 2024; Petko et al., 2025).

In a significant theoretical upgrade, recent scholarship has reconceptualized this outer boundary not just as a passive container, but as a distinct domain of Contextual Knowledge (XK). This shift moves beyond viewing context as merely external influences to recognizing it as an explicit form of teacher knowledge, ranging from the micro-level awareness of student needs to the macro-level understanding of sociopolitical constraints (Mishra, 2019).

This dual nature of context, existing both as the environment in which knowledge is enacted and as a cognitive domain the teacher must possess, is critical for understanding why technology integration succeeds in one classroom but fails in another. It highlights that the "successful integration of technology relies not only on high levels of knowledge in each of these domains but also on combined considerations... that guide the pedagogical design" (Petko et al., 2025, p. 1).

The Generative AI Disruption

Today, the framework faces its most provocative challenge yet: the rise of Generative Artificial Intelligence (AI). Unlike previous waves of educational technology which required teachers to curate and present information, GenAI agents are capable of creating content, simulating pedagogical dialogue, and personalizing instruction autonomously (Artsin & Bozkurt, 2026). This accretion of capability forces a re-examination of the core TPACK components (Islam & Mishra, 2024; Mishra et al., 2024).

The influence of AI on Pedagogical Knowledge (PK) is particularly profound, as AI-powered tools begin to reshape the dynamic nature of teacher knowledge transformation. Furthermore, the introduction of AI necessitates a critical look at Technological Content Knowledge (TCK). If an AI can generate a historical essay or a mathematical proof, the teacher's role shifts from a primary source of content to a critical evaluator of "curriculum-shaped objects" generated by algorithms (Mishra et al., 2024).

As we stand at the precipice of this new era, the TPACK framework remains a vital, albeit evolving, map. It challenges us to move beyond technocratic rationality, the belief that technology alone can save education, and instead focus on developing teachers who possess the cognitive flexibility to weave together content, pedagogy, technology, and context into a coherent whole. The future of educational technology will not be defined by the sophistication of our tools, but by the "deep, flexible, pragmatic, and nuanced understanding" teachers bring to their use (Koehler et al., 2013, p. 16). To illuminate these urgent issues and explore the resilience of teacher knowledge in an age of algorithmic disruption, we present the following scholarly dialogue with Professor Punya Mishra.

A Scholarly Dialogue with Punya Mishra on TPACK and Digital Transformation and AI

Professor Mishra, thank you for joining us for this dialogue. The TPACK framework has long served as the 'North Star' for integrating technology into education, emphasizing that teaching is a complex, situated activity requiring specialized knowledge. However, we now face a technological wave, GenAI, that doesn't just support instruction but can actively generate it. This shifts the ground beneath our feet. In this conversation, we want to explore the resilience of the TPACK model in the age of AI agents, the critical role of 'Contextual Knowledge' when algorithms drive learning, and your vision for the future of human creativity and teacher agency in a digital world.

The Evolving Role and Knowledge of the Teacher

Aras Bozkurt: The rise of GenAI is profoundly reshaping the role of the teacher, shifting it from an information provider to something more akin to a learning architect or a 'cyborg shepherd', guiding both students and AI. From your perspective, how does this shift impact the fundamental knowledge domains a teacher must possess? What new pedagogical skills (PK) and content-related understandings (CK) are becoming essential for educators who must now teach students with and about AI in a critical and ethical manner?

Punya Mishra: Let me start by clarifying what the TPACK framework fundamentally is: a framework of teacher knowledge. It builds on Shulman's Pedagogical Content Knowledge framework (1986), which argues that the knowledge teachers have is different from that of subject matter experts or general pedagogues. It's about understanding the deep relationship between how students understand the world, the misconceptions they might have, and the pedagogical strategies that work best for particular kinds of content. What Matt Koehler and I did (in a series of articles, most prominently Mishra & Koehler, 2006; and Koehler & Mishra, 2008) was argue that given the advent of new technologies, knowledge of technology becomes an integral component of this relationship.

With GenAI, something surprising has emerged: content knowledge has become even more important than before. This may seem counterintuitive, but the stochastic, hallucinatory nature of these technologies means they can go off track in very subtle and nuanced ways. Unless you have deep knowledge of your field, you won't be able to catch these errors. I experience this constantly. When GenAI helps me draft something, the output might be right, but it's not quite right. It gets nuances wrong in ways that only someone who has thought deeply about these topics can detect. Novice teachers (and learners) are at a significant handicap here because they don't have the judgment to evaluate GenAI outputs critically.

This technology is what I call a "smart drunk intern," it's both incredibly capable and deeply unreliable. It can do things we couldn't do before: translation across languages, generating multiple analogies, creating simulations. But it's an untrustworthy partner that can lead you astray. As a dialogic partner for brainstorming, it's powerful. But getting content knowledge wrong; that's mission critical, because that's your goal as a teacher. You want students to learn science, math, music, or whatever your discipline may be.

In terms of pedagogical skills, teachers now face something unprecedented: a "social other" in the classroom that spouts things with extreme confidence but can occasionally be profoundly misguided. This is a strange beast in a classroom context, we've never had an almost psychologically real being that has the potential to undermine the educator's authority. Teachers need to develop new skills for managing this presence, including helping students become critical evaluators of GenAI outputs.

I need to push back on one aspect of your question, though, the word "must" when you say teachers must teach with and about AI. I would caveat that significantly. If I'm teaching algebra or balancing chemical equations, I don't necessarily need to be teaching about GenAI in that context. The goal is for students to understand the deeper underlying content. However, I do think that in certain fields, particularly writing and, most definitely, technology education, addressing GenAI becomes almost essential. The question of voice, of caring, of pride in one's work becomes more foregrounded.

That word "pride" is crucial here. In how few contexts in school do we ask students to do things they would feel proud of? Pride has become a lens for me in thinking about GenAI adoption. If you take pride in your work, you will automatically be skeptical and critical of GenAI outputs, not rejecting them outright, but carefully evaluating and refining them. This applies to teachers as much as students. And in some ways is one possible answer to the issue of plagiarism that has bedeviled all conversations about GenAI in education, ever since ChatGPT erupted into our social and cultural lives.

There's another dimension to teaching that GenAI cannot replicate: the performative and design aspects of crafting learning experiences. When a teacher designs an activity, they work with broad contours-imagining how it will unfold, what students will struggle with, how to sequence the parts. They test ideas, reject what won't work, refine based on intuition built from years of classroom experience. This is the artistry of teaching: understanding not just what content to cover or which pedagogy to use, but how to orchestrate an experience that works for these particular students in this particular moment. GenAI can suggest components, but it cannot replicate the design sensibility and situational judgment that make teaching a craft.

Finally, regarding contextual knowledge, teachers need to understand that the sociology of the classroom has changed. From a distributed expertise perspective, GenAI becomes another piece of that puzzle, a smart drunk intern with all the strengths and weaknesses that come with that. This falls under contextual knowledge: understanding the new context within which learning happens. The addition of contextual knowledge (XK) to the TPACK framework came from recognizing that the original Venn diagram representation was semantically incomplete-spaces should define knowledge domains, not dotted lines around them labeled "contexts." By reframing context as contextual knowledge, we can now think productively about what teachers need to know about their specific situations: whether students have tablets versus limited computer lab time, whether cell phones are banned, what state standards apply, what school policies constrain or enable certain practices. These are all elements of contextual knowledge that fundamentally shape how technology, pedagogy, and content can come together in practice.

The TPACK Framework in the Age of Generative AI

Aras Bozkurt: The TPACK framework brilliantly illustrates the interplay between technology, pedagogy, and content. GenAI-a technology that can create content (C), generate lesson plans (P), and act as the technology (T) itself-seems to blur these distinct domains. How does the emergence of AI challenge or extend the TPACK framework? For instance, when an AI can generate a Socratic dialogue (TPK) or explain a scientific concept using a novel analogy it creates (TCK), do we need a redefinition of the intersections, or perhaps even a new component in the model to account for this agentic technology?"

Punya Mishra: This is a critical question, and I want to point you to a paragraph in our original (Mishra & Koehler, 2006) paper that I'm particularly proud of. We were explicit that the distinctions we make between technological knowledge, pedagogical knowledge, and content knowledge are somewhat artificial, in other words, they serve an analytic function, and the reality is definitely fuzzier. If you're a musician, what's the line between knowing music and knowing the tools you create music with? If you're an astronomer, where's the boundary between your knowledge of astronomy and your ability to interpret data from the Hubble Space Telescope? Tools and knowledge have always been deeply intertwined.

Thus, the TPACK framework is an analytic framework that helps us see the phenomenon of teaching through these three interlocking circles (and the outer circle of contextual knowledge). We were always aware that this is a move that helps us analytically for research and teacher professional development, while keeping in mind that these distinctions are much more fuzzy in practice. What GenAI has done, as it does in many domains, is push that fuzziness in our face. It's no longer something we can ignore.

When GenAI creates what I call "curriculum-shaped objects" (something with all the right headings and words that look like curriculum but may miss crucial nuances), it reveals the limitations of surface similarity. These outputs lack the lived expertise, the practical wisdom that educators have who work in actual classrooms with actual people. This makes the teacher's role shift toward being more of a curator, teaching at a meta-level about how to evaluate content rather than just generating content. You can ask GenAI to write an essay about history, but how do you know if it's a good essay?

I spoke at a journalism conference recently, and when asked about the biggest change in their careers, I said: "You're going to spend 95% of your time verifying." There will be so much slop out there that authenticity becomes critically important. Teachers have a vital role in helping students understand authenticity: which sources to trust, what are productive, critical ways of thinking about this issue of trust. GenAI has no connection to truth; it will say whatever emerges from its statistical patterns, and it is our responsibility to learn how to critically deal with its outputs.

Now, regarding whether we need new components in the framework, I need to address something that frustrates me considerably. Many people have added circles to the TPACK framework (ethics, equity, you name it). The problem isn't that these elements aren't important; it's that people don't understand what Venn diagrams are. Venn diagrams with circles can only meaningfully represent three overlapping sets-that's just geometry. A fourth set is possible, but it requires ellipses rather than circles to capture all the intersections properly, which sacrifices the threefold symmetry that makes the three-circle Venn so visually intuitive and communicatively powerful. Beyond four, the visual complexity becomes unmanageable. If you want to bring in additional elements, you might need a different visual representation altogether, perhaps a tetrahedron where content, pedagogy, and technology sit at three corners and your new element (say, ethics) sits at the fourth.

This is actually the same semantic issue that led us to shift from "contexts" (represented as a dotted line) to "contextual knowledge" (represented as another circle). The original representation suggested, without saying so explicitly, that context was somehow different from the other knowledge domains, sitting outside them. But teachers need knowledge of their contexts just as much as they need knowledge of content, pedagogy, and technology. Making the outer circle represent contextual knowledge made the framework semantically consistent since all the spaces now represent knowledge domains. However, this also highlights the limitation of adding further elements. We need to be thoughtful about how we represent things visually. I say this not because I think the TPACK framework is sacrosanct, but more to urge people to be intentional in the design choices we make.

Finally, I don't think the framework fundamentally changes with AI. What changes is what goes into those circles-the kind of knowledge required gets redefined. From a distributed expertise perspective, GenAI becomes another piece of distributed expertise in the classroom. To understand what this means: there have been discussions about distributed versions of TPACK in multi-teacher classrooms or team-teaching contexts-spaces where one educator might excel in language arts while another's strength is mathematics, creating complementary expertise across the team. In this framework, GenAI becomes another element of that distributed expertise, contributing its particular strengths (rapid generation, translation, pattern matching) while lacking others (judgment, contextual wisdom, relational understanding). Understanding GenAI as distributed expertise, rather than as a replacement for teacher knowledge, helps us see more clearly both what it offers and what it cannot do. That is a form of technological knowledge.

So, in that manner, the framework itself remains useful for what it needs to do. The advent of GenAI complicates each of the elements, certainly, but the basic structure holds. It's important to remember that TPACK was never meant to capture everything about teaching. Teaching and learning are too complex to capture in a venn-diagram. But that's okay-it's one lens among many. It is a relatively abstract representation of a complex domain, which, I believe, is its strength. But like every representation it simplifies in order to communicate. The point is not that TPACK is the answer to every educational conundrum. It would be naive to make such a claim. But it is a framework that lets us interrogate teacher knowledge in productive ways, it helps us think of teacher education and teacher professional development in intentional ways.

Fostering Creativity with GenAI through a TPACK Lens

Aras Bozkurt: Beyond its core components, your work has often emphasized creativity and design thinking. GenAI tools offer immense creative potential but also risk homogenizing student work through predictable outputs. How can educators use the principles of TPACK to design learning experiences that leverage AI to foster genuine creativity and critical aesthetic judgment, rather than stifle it? What does creative 'Technological Pedagogical Content Knowledge' look like when your co-creator is an algorithm?"

Punya Mishra: I need to clarify something fundamental. The TPACK framework is completely neutral with respect to the goals of education. If your goal is for students to do well on tests about rote memorization, TPACK will suggest flashcards and spaced repetition. If your goal is for students to become better writers, flashcards are a terrible idea, but TPACK won't tell you that one goal is better than another. That's a broader question we need to answer as a society, through standards and decisions that individual teachers make about what they care about. What I am trying to say is that TPACK, at its heart, isn't about telling educators what should matter; it's about helping them see what kinds of knowledge are at play when they teach. The values that guide those choices (ethical, creative, or civic) come from us, not from the framework itself.

Creativity is a good example to make this point. The TPACK framework doesn't tell you to prioritize creativity. That has to be a value that you bring to it. But once you've decided it matters, TPACK helps you think through how to pursue it.

I should also add that there are two ways creativity can play out in educational contexts, and it's important to distinguish between them. The first is creativity on the part of the educator, how they approach their work, design experiences, and bring their whole selves into teaching. The second is when creativity becomes a goal for students, helping them become more creative thinkers and makers. These can align, but they are fundamentally different things and making this distinction matters.

Let me address teacher creativity first. Personally, creativity is huge for me as a teacher, though I recognize this is separate from the framework itself. My overarching goal when working with teachers is that I want them to be agentic in their practice, to own that space. Owning that space often means being creative, bringing their whole self into it, not just following prescribed approaches. This comes from two perspectives. First, many of our pedagogical approaches have become stale. By giving students new ways of approaching content and ideas, we make them more agentic and engaged. Second, there's evidence that creative approaches help develop teachers who are more engaged and avoid rote mechanical approaches.

The TPACK framework can support teacher creativity by asking: Let's hold two elements constant, what can we change in the third? We used to run "TPACK games" where you'd pick two things at random-teach 5th grade math using paper airplanes, for example, and figure out the pedagogical approach that would make it work. This forces us to think differently, creatively. Similarly, if you start from the technology itself rather than from subject matter or pedagogy, interesting things can happen. If you say, "I have this thing called a wiki where everybody can co-edit," you start thinking of new pedagogical approaches and new ways of thinking about content. With GenAI, asking "How does it change how I think about content and pedagogy?" becomes a generative question.

Regarding student creativity (the second dimension), this has to be a goal you bring to the framework beforehand. You have to decide that you want your students to be creative. The framework itself doesn't mandate creativity, or any other pedagogical goal for that matter. It is quite goal-neutral. But once you've decided creativity is your goal, TPACK helps you think through how to align your knowledge of technology, pedagogy, and content to support that aim.

Finally, regarding your question about co-creating with "an algorithm," I would push back a bit on that terminology. GenAI isn't algorithm-driven in the traditional sense; algorithms produce the same result with the same input. These LLMs are stochastic systems, and there is some inherent variability in their outputs. And this can be both good and bad. As long as you know you're working with a smart drunk intern that's biased, sycophantic, and unreliable, it can be powerful. You can ask for five analogies using popular media to explain entropy, and it will generate interesting options, but it will be your filter, your judgment, that determines the value and impact.

My main concern is the underlying assumption that GenAI technologies are all-knowing and always correct. I hear stories about people treating GenAI as an infallible buddy, and that's dangerous. But it is an amazing technology and can be used in highly creative ways. And not all creativity has to be big-C creativity. Many of the uses of GenAI (or other technologies) are examples of small-c creativity. Most uses of technology are relatively mundane, not attempts at high art. Similarly, most GenAI uses will be relatively mundane but fun and creative nonetheless.

These technologies are complicated. When working with GenAI as a creative partner, think of it like collaborating with a human colleague: academic integrity rules apply. Just as you wouldn't claim a colleague's ideas as entirely your own, you can't claim GenAI output as your own. You can collaborate with it, brainstorm with it, use it to generate possibilities, but at the end of the day, you are responsible for what you produce, and the final responsibility is always yours. You cannot say the GenAI came up with a bad lesson plan. You chose to use it. That's on you. If you take pride in your work, you will automatically be skeptical and critical of GenAI outputs, not rejecting them outright but carefully refining them.

Future Vision for Teacher Expertise and 'Future-PACK'

Aras Bozkurt: Looking at the future of education, what is your vision for the evolution of teacher expertise? As AI potentially handles more routine tasks of instruction and content delivery, what becomes the core, irreplaceable value and knowledge of the human teacher? If the TPACK framework describes what educators need to know today, what might a 'Future-PACK' framework look like for a teacher in the next decade, and what would you argue is its most important component?

Punya Mishra: I need to be honest... My future vision is actually quite pessimistic for several reasons. The constraints that education faces globally, combined with the tremendous hype around this technology, and the language around "we're going to GenAI all our schools" and "this is the greatest tutor ever"-this inevitability rhetoric is so strong that it will actually undermine teacher education. Anywhere we have teacher shortages, this will make things worse, forcing us to lean more heavily into GenAI as a supposed solution.

There are massive equity issues here. People with resources will have humans plus AI, while people without resources will just have GenAI with vague human supervision. We will abdicate our judgment to these systems. Broader social issues around GenAI slop, authenticity, truth, and trust-which are critical to democratic functioning-will worsen. Classrooms are petri dishes where students learn to work with each other, rub shoulders, disagree, and have their rough edges intellectually sanded out. These are crucial crucibles for democracy.

I worry deeply about this as we lean into one-on-one models of teaching and learning. Most studies of personalized learning systems show that 95% of students don't use them. The 5% who do use them perform better, sure-but that's not a solution, it's a failure. I see the devaluation of teacher expertise coming because we have a harder time measuring practical wisdom. How do you measure lived experience? We don't know how to do it, so it gets devalued.

All of this exists within a broader social environment that's already highly polarized. Look at the politics around what we can teach, what history should be taught. There's a wonderful book called Prejudice and Pride by an Indian academic who examines history textbooks in India and Pakistan. We were one geographical region until 75 years ago, yet the way we represent our histories is radically different, tracing lineages back a thousand years to make essentialist claims about national identity. These technologies will only make these problems worse.

That said, here's what I think teachers need for the future, drawing from my personal journey: I started with my dissertation, creating software, focused on representations of knowledge and one-on-one interactions. Later work led to the TPACK framework, viewing teachers as orchestrators within classroom spaces. Then Melissa Warr and I co-developed the Five Spaces of Design (Warr et al., 2020; Warr et al., 2023), looking at broader ecosystems of schools and classrooms. Now, I see that we must think about the even bigger context within which education exists.

If teachers are to be useful going forward, they need that bigger lens. They need to understand the society their students are entering-a society that is transforming rapidly. This requires almost a quasi-political stance. However, curricula for teacher education are determined by standards that are, by their very nature, conservative. There's a tension here I don't know how to resolve.

Teachers need to develop judgment about when and how to use these tools, understanding both their power and their profound limitations. They need to cultivate in themselves and their students a sense of pride in work-creating things worth being proud of, not just completing assignments. They need to be curators and evaluators, teaching at a meta-level about how to assess the authenticity and quality of content in a world increasingly filled with convincing but potentially misleading outputs.

Most critically, teachers need to maintain their core expertise in their disciplines while developing sophisticated understanding of how GenAI intersects with their practice. Content knowledge hasn't become less important-paradoxically, it's become more important because GenAI makes it easier to be confidently wrong at scale. The irreplaceable value of human teachers lies in their judgment, their practical wisdom, their understanding of individual students in specific contexts, and their ability to model critical thinking and authentic engagement with ideas.

A "Future-PACK" framework, if we must call it that, would need to account for this broader contextual awareness-not just the immediate classroom context, but the societal, political, and technological contexts within which education happens. But we need to be thoughtful about how we represent such complexity. Perhaps we need to move beyond frameworks altogether and toward more fluid, adaptive models of teacher knowledge that acknowledge the irreducible complexity of teaching in transforming societies.

Conclusion: The Human Element in the Loop

This dialogue with Punya Mishra highlights a profound paradox of the AI age: as machines become smarter and more generative, the specific, nuanced, and contextual knowledge of the human teacher becomes more, not less, critical. Mishra dismantles the notion that AI can seamlessly replace the complex orchestration of teaching, framing GenAI instead as a "smart drunk intern," a tool of distributed expertise that is incredibly capable yet fundamentally unreliable.

The conversation highlights that the TPACK framework remains robust precisely because it helps identify what AI cannot do. While AI can generate "curriculum-shaped objects" and simulate pedagogical moves, it lacks the deep Content Knowledge (CK) required to verify the nuances of truth in a landscape of hallucinations. Furthermore, AI lacks the Contextual Knowledge (XK) of the specific students in the room, the "sociology of the classroom", which Mishra argues is essential for effective instruction.

Crucially, Mishra introduces the concept of pride as a pedagogical lens. In an era where plagiarism is easy and content is abundant, the role of the teacher shifts to fostering a sense of ownership and agency in students. Whether using AI for creativity or analysis, the human must remain the final filter, responsible for the output.

Ultimately, Mishra's vision serves as a sober, critical warning against technological solutionism. He paints a potentially pessimistic future where the "devaluation of teacher expertise" accelerates, and equity gaps widen between those who have access to human mentorship and those relegated to automated tutoring. The "Future-PACK" for educators, he argues, must therefore go beyond technical skills to include a "quasi-political" understanding of the societal systems they inhabit. The future of education depends not on the sophistication of our algorithms, but on the preservation of teacher agency, the cultivation of critical judgment, and a commitment to protecting the human connections that constitute the heart of learning.

Funding Statement

This paper is funded by Anadolu University with grant number YTS-2024-2559 and YTS-2025-2776.

Competing Interests

The authors have no competing interests to declare.

Ethics and Consent

This study was conducted collaboratively by both researchers, who are also listed as the sole authors of the work. As the research did not involve external participants and was based entirely on voluntary collaboration between the authors, no formal ethical approval was required. Participation in the study was based on mutual consent and voluntary involvement of the researchers.

Acknowledgements

Based on Academic Integrity and Transparency in AI-assisted Research and Specification Framework (Bozkurt, 2024), the authors of this paper acknowledge that the paper was proofread and edited with the assistance of Google's Gemini (Versions as of July 2025) and Anthropic's Claude (Versions as of January 2026), complementing the human editorial process. The human authors critically assessed and validated the content to maintain academic rigor. The authors also assessed and addressed potential biases inherent in the AI-generated content. The final version of the paper is the sole responsibility of the human authors.

Author Contributions (CRediT)

Punya Mishra: Conceptualization, writing-original draft preparation, writing-review and editing. Aras Bozkurt: Conceptualization, supervision, project administration, funding acquisition, writing-original draft preparation, writing-review and editing. All authors have read and agreed to the published version of the manuscript.

References

Islam, R., & Mishra, P. (2024, March). The design of a TPACK survey for Gen AI: A preliminary study. In Society for Information Technology & Teacher Education International Conference (pp. 2415-2420). Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/primary/p/224315/

Koehler, M. J., & Mishra, P. (2008). Introducing TPCK. In AACTE Committee on Innovation and Technology (Ed.), The handbook of technological pedagogical content knowledge (TPCK) for educators (pp. 3-29).

Mishra, P., Phillips, M., Baran, E., Koehler, M., Harris, J., & Williams, M. (2024, March). Part 1: The past, present, and most importantly the future of TPACK. In Society for Information Technology & Teacher Education International Conference (pp. 2443-2448). Association for the Advancement of Computing in Education (AACE).

Warr, M., Mishra, P., & Scragg, B. (2020). Designing Theory. Educational Technology Research and Development. https://doi.org/10.1007/s11423-020-09746-9

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