The studios address a single problem — that recall examinations and take-home essays no longer evidence what a graduate can do, because generative AI can produce both. The Innovation Challenge Studio replaces them with authentic, team-based challenges requiring original contribution, external validation and visible process.
The assessment problem
Recall examinations and take-home essays were always an imperfect proxy for graduate capability. They survived because the proxy was cheap and roughly correlated with the thing it stood in for. Generative AI has broken the correlation. A grade awarded for a take-home essay now certifies what a model produced, filtered through a student’s judgement about which output to submit.
The instinctive response — detection software and a return to invigilated recall — fails on both sides. Detection is unreliable enough to be unsafe as evidence in a misconduct case. Invigilated recall assesses a narrower band of capability than employers were already complaining about.
The alternative is assessment that is difficult to fake because the process is part of the evidence.
Three studios
1. HigherED Pedagogy with AI — design
AI drafts lesson plans, slide decks and teaching content for university modules, mapped to intended learning outcomes, Bloom’s taxonomy and constructive alignment. The lecturer edits, owns and signs off.
- Course and session design across 15+ research-driven pedagogies.
- Editable, lecture-ready presentation decks.
- Outcomes and assessment aligned by construction rather than retrofitted.
2. Spot Observation — develop
Short, evidence-anchored teaching observations scored against a transparent rubric. Every rating requires proof from the room, and hedged language is stripped out, so feedback develops practice rather than policing it.
- Rubric-scored ratings with mandatory evidence.
- Job-embedded coaching and reflection prompts.
- Defensible records including the lecturer’s right of reply.
- Multiple framework support, including Ofsted, Danielson and TRCN professional standards.
3. Innovation Challenge Studio — assess
Students take on authentic, team-based challenges that demand original contribution and external validation, producing portfolio-grade evidence of capability.
| Conventional assessment | Innovation Challenge |
|---|---|
| Timed recall examinations rewarding memorisation | Authentic real-world briefs requiring original contribution |
| Take-home essays a model can write in seconds | External validation the student must defend in person |
| A single high-stakes grade with no visible process | Process evidenced across the full experiential learning 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 the three connect
Course design, observation and assessment are usually owned by different parts of a university and evidenced in different systems. That fragmentation is why teaching quality reviews are laborious and why assessment reform stalls: nobody can see the whole picture at once. Running the three on one record means an intended learning outcome can be traced from the module design, through the teaching that delivered it, to the evidence a student produced against it.
Who it is for
- Universities and colleges reviewing assessment strategy in response to generative AI.
- Teaching and learning units responsible for academic development.
- Professional and vocational programmes where employer confidence in the qualification matters directly — nursing, engineering, education, business.
- Institutions preparing for accreditation or quality review that requires evidence of teaching quality.
Getting started
Most institutions begin with a single programme rather than an institution-wide rollout, running the Innovation Challenge Studio on one module for one cohort while the observation engine runs alongside it. That produces a comparison against the previous cohort’s conventional assessment within one academic year.
Related: Pedagogy with AI for the underlying instructional model, assessment for the K–12 implementation, and AI in education for the wider context.