They address one problem — that recall examinations and take-home papers no longer evidence what a graduate can do, because generative AI produces 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 papers were always an imperfect proxy for graduate capability. They survived because the proxy was cheap and correlated roughly with the thing it stood for. Generative AI has broken the correlation. A grade awarded for a take-home paper now certifies what a model produced, filtered through a student’s judgment about which output to submit.
The instinctive response — detection software plus a return to proctored recall — fails on both sides. Detection is unreliable enough to be unsafe as evidence in an academic integrity hearing, and institutions have already reversed findings that rested on it. Proctored recall assesses a narrower band of capability than employers were complaining about before any of this started.
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 session plans, slide decks and course materials mapped to stated learning outcomes, Bloom’s taxonomy and constructive alignment. The faculty member edits, owns and signs off.
- Course and session design across 15+ research-driven pedagogies.
- Editable, lecture-ready decks.
- Outcomes and assessment aligned by construction rather than retrofitted before an accreditation visit.
2. Spot Observation — develop
Short, evidence-anchored teaching observations scored against a transparent rubric. Every rating requires proof from the room, hedged language is stripped out, and the record includes the instructor’s right of reply. This is the postsecondary implementation of Teaching Observation with AI.
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 |
|---|---|
| Timed recall exams rewarding memorization | Authentic real-world briefs requiring original contribution |
| Take-home papers a model can write in seconds | External validation the student defends 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 resume bullet specifications — so assessment measures what graduates can do and employers can trust the record.
Why the three connect
Course design, teaching observation and assessment are usually owned by different offices and evidenced in different systems, which is why teaching quality reviews are laborious and assessment reform stalls: nobody can see the whole picture at once. Running the three on one record means a stated learning outcome traces from course design, through the teaching that delivered it, to the evidence a student produced against it.
That trace is also what a regional accreditor and a program-level accreditor are asking for. Most institutions can produce it; few can produce it without a semester of preparation.
Who it is for
- Institutions reviewing assessment strategy in response to generative AI.
- Centers for teaching and learning responsible for faculty development.
- Professional and licensure-track programs where employer and board confidence in the credential is direct — nursing, engineering, education, accounting, business.
- Community colleges with workforce and transfer accountability pressures.
- Institutions preparing for accreditation review or program-level assessment reporting.
Getting started
Most institutions begin with a single program rather than a campus-wide rollout, running the Innovation Challenge Studio on one course for one cohort with the observation engine alongside it. That produces a comparison against the previous cohort’s conventional assessment inside one academic year, which is the evidence a curriculum committee will actually act on.
Related: Pedagogy with AI for the underlying instructional model, assessment for the K–12 implementation, and AI in education for the wider context.