Assessment
10 items with this tag.
The verification trap
Institutions everywhere now insist on the same rule — use AI, but always verify its output. Yet verifying AI output is hard, slow, and often inconclusive, even in domains where you're competent to judge it. This post pushes that advice to its limit to see what breaks, and finds that as models improve, the verification mandate quietly trains the opposite of the scepticism it intends.
AI and the learning alignment problem
An invited keynote webinar for the Global Research Nursing Education Network, whose members work across the US, Canada, and Africa. The talk names a learning alignment problem in education that is structurally identical to the value alignment problem in AI safety: systems optimise the measurable proxy rather than the thing the proxy stands for, and AI has made the gap between the two impossible to ignore. It works through what nursing formation actually requires, why more accurate detection cannot help, and what shifts when educators design for the conditions of cognitive struggle rather than for the artefact.
What is the work?
A keynote for the Royal College of Nursing's Education conference. Examines how generative AI has severed the inferential chain between assessment artifacts and the learning they were meant to evidence, and what nursing education needs to do structurally in response. The argument moves from the current AI landscape, through the premises of nursing formation, to why discursive responses (policies, declarations) cannot address a structural problem.
The PhD is a process of becoming: Reimagining AI and PhD assessment
AI has disrupted doctoral education in two ways: the immediate question of AI-assisted writing, and the deeper question of what the PhD means when AI can conduct research from scratch. This post argues that the thesis was always a proxy for the person; evidence of an identity shift, not the thing being assessed in its own right.
Problem-based learning and the structural conditions for productive AI integration
Higher education's response to AI has focused on the artefact: detecting it, restricting it, and restoring confidence in what students produce. This essay argues that the structural features of problem-based learning — problem-driven inquiry, collaborative knowledge construction, facilitation over instruction, and metacognitive reflection — are the same conditions under which AI integration becomes educationally productive rather than substitutive. The alignment is structural, not retrospective: PBL was designed around these conditions before AI existed. The argument extends further: AI shifts what category of problem PBL can engage with, expanding access to wicked problems previously beyond students' reach. Investing in PBL's structural conditions is simultaneously investing in AI readiness.
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.
Designing AI out of assessment should be an academic offence
Academic offences committees are investigating the wrong party. When AI is integral to authentic professional practice, assessment that excludes it does not protect rigour — it tests performance in a professional context that no longer exists. Valid assessment measures what graduates will actually need to do; for most health professions graduates in 2025, that includes thinking well with AI. The accountability for assessment design lies with educators, not students.
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
AI detection in assessment: The security theatre of prompt injection
When educators embed hidden instructions in assessment materials to detect AI use, they import adversarial security thinking into educational relationships. This post examines what AI tripwires reveal about institutional assumptions (i.e. that assessment is about artifact authentication rather than learning measurement) and argues that this approach creates escalating countermeasure dynamics while only detecting carelessness, not genuine disengagement. The alternative requires rethinking what assessment is actually for in an era when artifact production has become trivially automatable.
A bitter lesson for higher education
Rich Sutton's 'Bitter Lesson' from AI research—that general methods leveraging computation outperform human-crafted knowledge—has a direct parallel in education. When AI can produce the kinds of artefacts that assessments have traditionally relied on, it exposes a fundamental problem we have long ignored: we were never really measuring learning, we were measuring the difficulty of producing certain artefacts. This post explores what the Bitter Lesson means for assessment design in health professions education, and why AI makes it impossible to continue pretending otherwise.