Course design maps to stated learning outcomes and the relevant qualification framework level, so alignment evidence is produced as a by-product of teaching rather than assembled before a review.
The assessment problem is not going away
Take-home essays, coursework and a good deal of project work no longer reliably evidence the student. A mark awarded for a written submission now certifies what a model produced, filtered through a student’s judgement about what to submit.
Detection is not the answer. False positive rates are high enough to make detection tools unsafe as the basis of an academic misconduct finding, and the reputational cost of a wrong accusation is severe. The durable response is changing what is assessed.
Three studios
1. HigherED Pedagogy with AI — design
AI drafts lecture plans, slide decks and materials mapped to stated learning outcomes, Bloom’s levels and the relevant framework level. The lecturer edits, owns and signs off.
- Course and session design across 15+ research-driven pedagogies.
- Editable, lecture-ready decks.
- Constructive alignment by construction rather than retrofitted before review.
- Workable for large cohorts.
2. Spot Observation — develop
Short, evidence-anchored teaching observations scored against a transparent rubric. Every rating requires evidence from the room, hedged language is flagged, and the record carries the lecturer’s right of reply.
That last point is not a courtesy. Teaching observation in higher education touches promotion, probation and departmental politics; a record a lecturer cannot answer will not be trusted, and an untrusted observation system produces performances rather than teaching.
3. Innovation Challenge Studio — assess
Students take on authentic, team-based challenges demanding original contribution and external validation, producing portfolio-grade evidence of capability.
| Conventional assessment | Innovation Challenge |
|---|---|
| Recall examinations rewarding memorisation | Authentic briefs requiring original contribution |
| Coursework a model can write in seconds | External validation the student defends in person |
| A single high-stakes mark, no visible process | Process evidenced across the full experiential cycle |
| Certifies what was produced | Evidences what the student can do |
Every challenge ships with a four-band rubric, scaffolded milestones and CV bullet specifications, so assessment measures what graduates can do and employers can trust the record.
Why this matters for UK institutions
- Graduate outcomes are measured and published, and feed institutional reputation and regulatory scrutiny. A portfolio of defended, externally validated work is a stronger employability signal than a degree classification.
- Academic integrity policies written before generative AI are being rewritten across the sector. Assessment redesign is the durable half of that work; detection is the fragile half.
- Teaching quality evidence is required for internal review and external scrutiny, and most institutions assemble it periodically rather than continuously.
Who it is for
- Universities reviewing assessment strategy in response to generative AI.
- Further education and sixth form colleges, including those delivering T Levels and technical routes.
- Centres for academic practice and teaching enhancement units.
- Professional and licensure-track programmes — nursing, engineering, law, accounting — where employer and regulator confidence in the credential is direct.
- Initial teacher education providers, who moved to the new Ofsted inspection model from January 2026 and whose graduates are themselves being observed.
Getting started
Most institutions begin with one department rather than an institution-wide rollout, running the Innovation Challenge Studio on a single module for one cohort with the observation engine alongside. That produces a comparison against the previous cohort’s conventional assessment within one academic year, which is the evidence a committee will actually act on.
Related: Pedagogy with AI for the underlying model, and AI in education for the wider policy context.