Why Your AI Committee Keeps Stalling
Source: Mike Kentz Substack Author: Mike Kentz Original source: https://mikekentz.substack.com/p/why-your-ai-committee-keeps-stalling Published: 2026-08-06 Source type: essay
Summary
Mike Kentz argues that campus AI committees stall because they keep trying to solve two different problems at once: AI literacy and assessment integrity. Once institutions put both goals in one committee, discussion collapses into a loop of syllabus language, detection software, and academic-integrity panic instead of real progress.
His central move is architectural: split the work into two charters. One room focuses on assessment redesign, where the real question is what counts as trusted evidence of learning. The other room focuses on AI literacy as a multi-tiered curriculum problem, including machine literacy, cultural impact, and applied LLM fluency.
Pull quotes
One committee, two agendas
“Most institutions are running one committee charged with solving two mutually exclusive agendas at the exact same time.”
The middle becomes a sinkhole
“When an institution mashes these two domains into a single task force, the overlap area (AI Cheating) becomes a sinkhole.”
Split the work
“When you split these two conversations into separate rooms with separate charters, things get really interesting.”
AI literacy is broader than policy
“Most campus AI literacy efforts currently begin and end with syllabus disclaimers.”
Big ideas
- District AI work is a long-term redesign project
- AI literacy has to be taught inside real subjects
- Learning still needs some struggle, even when AI can make things easier
Claims
- AI literacy takes system capacity, not just tool access
- In an AI world, assessment should focus on watching students think
- Schools should separate AI literacy work from assessment integrity work
Key evidence and examples
- Kentz describes the recurring committee loop in which literacy advocates and assessment defenders keep talking past each other.
- He says the overlap area is usually AI cheating, which absorbs energy that should go to redesign work.
- In the assessment room, he recommends secured tasks, open tasks, oral defenses, process artifacts, live performance, and comparative analysis.
- In the literacy room, he divides AI literacy into machine literacy, AI cultural studies, and LLM literacy.
- His proposed “Five-and-Five Split” tells institutions to form two independent working groups with separate deliverables for the semester.
Education relevance
Very high relevance for higher-ed governance, campus AI committees, assessment redesign, faculty working groups, and institutional strategy.
Durability note
The specific committee examples and named institutional frameworks may age, but the durable insight is likely to recur: institutions make more progress when they stop treating AI literacy and assessment integrity as one agenda.
My notes
- Big one for Clay’s work because it reframes AI governance as a structure problem, not a policy wording problem.