What Does the Credential Still Certify? Cognitive Stewardship for AI-Mediated Education

AI-generated plausibility and epistemic responsibility in higher education: implications for knowledge, learning, and society

Evaluation in the Age of AI: Output as Evidence of Learning

This paper argues that the core problem extends beyond academic dishonesty to a deeper misalignment between assessment practices and the learning outcomes they are intended to measure, and highlights the need for alternative assessment models that emphasize process over product.

Designing AI-resilient assessment in higher education: a four-pillar conceptual framework

A framework for AI-resilient assessment that shifts evaluation from product quality to demonstrable reasoning, decision-making, and ownership of learning is proposed, Illustrated primarily through health sciences education, with wider relevance to professional and practice-oriented disciplines.

Does Expertise Still Matter? Generative AI and the Crisis of Professional Identity

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.

When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution

As AI systems become capable of producing the essays, code, reports, summaries, plans, and decisions through which institutions usually recognize competence, a familiar question becomes harder to answer: what is learning for? Existing AI ethics rightly emphasizes present failures--bias, opacity, hallucination, labor extraction, privacy risk, and weak accountability. But if the case for learning rests only on those failures, then each technical improvement appears to weaken it. This article develops a different answer. Using the idealization of AI that executes specified tasks flawlessly while lacking authority over purposes, legitimacy, and responsibility, we argue for post-instrumental learning: learning that preserves the capacities people and institutions need when many useful outputs can be delegated. We analyze five such capacities--end-setting, reason-giving, contestability, refusal/revision, and participation--and name their erosion capacity dissolution. The central case is assessment under generative AI. When a polished artifact no longer reliably evidences understanding, institutions must assess the learner's accountable relation to AI-mediated work rather than the artifact alone. The takeaway is practical: AI governance should evaluate not only whether systems perform well, but also whether their deployment leaves people able to understand, challenge, revise, and share responsibility for the practices those systems mediate.

Considerations for Effective Online Assessment in the Age of Artificial Intelligence

Online assessment offers flexibility, scalability and rapid feedback, yet generative artificial intelligence (GenAI) has weakened the assumption that a submitted product reliably represents a student’s independent learning. This critical essay argues that higher education should shift from product-centred assessment to a process-plus-performance model that makes reasoning, evidence selection, revision, feedback use, ethical judgement and intellectual ownership visible. Drawing on process-writing theory, constructivism, constructive alignment, assessment for learning, self-regulated learning, the Community of Inquiry, authentic assessment and Universal Design for Learning, it examines the limitations of take-home essays, remote multiple-choice tests, AI detection and surveillance. It proposes staged assignments, critical evaluation and transparent disclosure of AI use, authentic and localised tasks, reasoned multiple-choice questions, brief oral verification and targeted secure assessment at key progression points. Because process records can also be fabricated, trustworthy judgement should triangulate written development with live explanation or practical performance. This layered approach supports academic integrity without treating surveillance as the default, while strengthening critical thinking, AI literacy, inclusion and professional accountability.