Tag: higher-education
10 items with this tag.
Wicked problems
Problems that resist resolution because they have no definitive formulation, no stopping rule, and solutions that are good or bad rather than true or false.
AI assessment scales are taxonomies of containment
AI assessment scales and similar policies are taxonomies of containment that ask how to protect existing assessment practices from AI, not whether those practices remain fit for purpose. This post argues that they're asking the wrong question, and examines what higher education might be asking instead, with particular implications for health professions education.
Developing evaluative judgement for a time of generative artificial intelligence
Source details Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024).
The wicked problem of AI and assessment
Source details Corbin, T., Bearman, M., Boud, D., & Dawson, P. (2025). The wicked problem of AI and assessment.
Higher Education Reference Model (HERM)
A shared vocabulary for universities, set out in business, data, application, and technology reference models, and a practical starting point for making institutional knowledge readable by software and AI agents.
Arms race dynamics in higher education
How arms race dynamics in higher education create adversarial relationships between institutions and students, and what drives these cycles
Hidden prompts in assignments: the security theatre of AI detection
Some lecturers now hide prompts in assignment materials so that any AI system reading them adds tell-tale keywords to its output. This post looks at what those tripwires assume about assessment, and why what they detect is carelessness with AI, not the absence of learning. The technique also invites students to turn it on AI marking, and starts an escalation neither side can win. The alternative is to ask what assessment is for when producing the artefact takes seconds.
A bitter lesson for higher education
Rich Sutton's "bitter lesson" from AI research is that general methods using computation beat decades of carefully encoded human expertise. Higher education faces a parallel lesson about AI and assessment validity. Essays, reflective accounts, and other artefacts worked as evidence of learning mainly because they were hard to produce, and detailed briefs and rubrics now read as ready-made prompts for a language model. Handwritten exams, proctoring, and AI use scales all try to make artefacts hard to produce again, when the question worth asking is how we recognise learning at all.
The learning alignment problem: AI and the loss of control in higher education
Higher education's focus on prompt engineering — teaching technical skills for crafting AI queries — represents a misunderstanding of learning. This essay argues that prompts emerge from personal meaning-making frameworks, not technical mechanics, and that the institutional impulse to control AI interaction reveals a 'learning alignment problem': systems optimising for measurable proxies like grades rather than authentic curiosity. Drawing parallels to AI safety's value alignment problem, it shows how AI exposes that many assignments were already completable without genuine intellectual work. Universities must shift from control to cultivation paradigms, recognising that learning is personal and resistant to external specification, ensuring AI becomes a partner in human flourishing rather than a tool for strategic performance.
From teaching to learning: Rethinking education for a world of information abundance
Traditional education systems are structured around teaching, assuming it inevitably produces learning. This essay argues that this unidirectional model — knowledge flowing from expert to novice — no longer suits a world of information abundance and AI disruption. A networked, learner-centred approach offers an alternative, reconceptualising learning as a complex process where educators shift from knowledge authority to learning facilitator, enabling diverse participants to contribute to collective understanding. This transformation is urgent as AI develops capabilities once exclusive to human experts, and requires reimagining institutional structures around learning networks rather than teaching hierarchies to prepare graduates for complexity and uncertainty.