Generative artificial intelligence (GenAI) is rapidly reshaping engineering education, yet prevailing debates frame its impact narrowly as either pedagogical innovation or epistemic threat. In this article, we argue that such framings misdiagnose the problem by treating GenAI as a tool rather than as a sociotechnical reorganization of epistemic authority, agency, and professional formation. Drawing on sociological theory, philosophy of science, labor economics, and recent empirical studies, we show that GenAI lowers epistemic floors by expanding access to competent performance while leaving epistemic ceilings, those associated with judgment, authority, and problem framing, largely intact. This asymmetry produces new forms of stratification, redistributes responsibility without redistributing control, and stabilizes plausibility‐based forms of knowledge in place of warrant‐based engineering reasoning. We advance three central contributions. First, we conceptualize a shift from warrant to plausibility in engineering knowledge, in which fluent, algorithmically generated outputs increasingly circulate as legitimate despite bypassing core practices of verification, critique, and accountability. Second, we extend the concept of the AI wall beyond productivity to describe a ceiling of epistemic development, where further learning and judgment are not institutionally rewarded without deeper expertise, authority, or complementary organizational resources. Third, we theorize how engineering education participates in stabilizing these dynamics through what we term the social campus, the institutional space in which curricular norms, assessment regimes, professional signaling, and peer comparison converge to normalize AI‐mediated epistemic authority. Together, these dynamics position engineering education at a critical juncture. We argue for a reorientation toward epistemic stewardship, in which engineering programs deliberately cultivate the capacities needed to interrogate, govern, and responsibly deploy AI systems rather than merely operate within them. The question we want to highlight is not whether engineering education will adapt to AI, but whether it will do so as a steward of epistemic responsibility or as an accelerator of epistemic stratification.
Generative artificial intelligence (GenAI) has been primarily framed as an impartial educational tool. However, this framing overlooks an even larger shift: the reassignment of epistemological authority from teachers to students to machines. This paper presents a conceptual evaluation of the extent to which GenAI redistributes students'and teachers'ability to act in classrooms to produce knowledge, validate each other's claims, and create evidence of student learning while collaborating with and competing against humans. This evaluation draws on various theoretical paradigms, including Distributed Agency, Self-Determination Theory, Society 5.0, and Technology Integration Paradigms, including TPACK and SAMR. While all of the theoretical paradigms evaluated are relevant to the role of agency within education mediated by AI, none of them address the ongoing disparity regarding equitable distribution of power, ownership of the data used to mediate interaction, and accountability in relation to human-mediated interactions. As such, this paper introduces the Ecological Co-Agency Framework, which defines agency in terms of relational, regulatory, and pedagogical processes, conditioned by a defined commitment to human accountability for epistemological claims.
As generative artificial intelligence becomes one of the most significant systems of knowledge production in our society today, questions relating to who can access and shape that production grow increasingly important in our discourse. This paper argues that the existing frameworks for AI literacy, which are dominated by technical competency and responsible-use principles, are insufficient because they enforce a"consumer"orientation toward AI rather than fostering genuine epistemic agency. Based upon Foucault's concept of power-knowledge, Freire's pedagogy of critical consciousness, and scholarship of digital literacy, this paper proposes a reconceptualization of AI literacy as a critical practice that equips individuals not just to use AI systems, but to critically evaluate them, resist their structuring assumptions, and participate in their governance. The paper further argues that unequal access to AI tools in society recapitulates longstanding epistemic injustices, and that a literacy framework oriented toward empowerment must account for these structural inequities. A three-part framework of AI literacy based on the notions of contextual use, critical interrogation, and participatory governance frames this literacy as a cultivation of epistemic"agents"rather than the training of competent consumers of AI-generated information.
In higher education, academic writing has long been a central practice through which knowledge is developed, negotiated, and communicated. Epistemic agency, understood as responsibility for judgement and justification, has thereby functioned as a largely implicit foundation of writing, with authorship and text forming a stable unit from which epistemic responsibility could be inferred. The emergence of generative AI (genAI) disrupts this configuration. While genAI can participate in the formulation, structuring, and evaluation of knowledge claims, it does not bear responsibility for them. At the same time, it becomes internal to writing processes, making epistemic agency less visible and no longer reliably inferable from textual products alone. This article argues for a reorientation of academic writing pedagogy toward the explicit cultivation of epistemic agency under conditions of AI mediation. It conceptualises epistemic agency along four interrelated principles: decision-making, positioning, evaluation, and justification. From a media-pedagogical perspective, these are understood as mediated practices in which responsibility, critique, and authorship must be actively developed. The analysis thus situates academic writing within broader transformations of knowledge practices and points to fundamental challenges for learning and assessment in higher education in the age of generative AI.
The rapid diffusion of generative artificial intelligence (GenAI) tools has opened unimagined avenues for disrupting higher education, enabling professionals, especially researchers, to produce expert-seeming outputs and claim “expert-level status” without formal training in artificial intelligence. Yet, despite its popularity, concerns about AI’s effects on labor and expertise have largely overlooked a deeper categorical crisis: the collapse of the distinction between AI tool proficiency and genuine AI expertise and the consequences this has for professional identity and institutional decision-making. This positional paper interrogates the boundary between AI use and AI expertise, arguing that access to a tool that simulates expert output creates conditions under which the distinction between literacy and expertise becomes nearly impossible to perceive. Drawing on AI literacy frameworks, expertise theory, and professional identity. The paper maps this crisis by introducing simulated contributory expertise as a construct to name this condition at both individual and collective levels. The paper raises pressing implications for how higher education certifies expertise, designs AI literacy curricula, and governs institutional decision-making in an era where the professional identity, AI expertise, and its substance have become difficult to pinpoint.
Lucy Michael Nyagoga· Global Journal of Human-Soci...· 0 citations
By reframing GenAI adoption as a sociotechnical design-and-governance problem rather than tool uptake, RAiLE offers a pathway for building plural, accountable, and agency-preserving futures for language education.
Ali Khodi, Samantha M. Curle· Frontiers in Education· 1 citation