Separate AI Literacy and Assessment Integrity

Source: Mike Kentz Substack
Author: Mike Kentz
Original source: https://mikekentz.substack.com/p/separate-ai-literacy-and-assessment

Summary

Mike Kentz argues that educators keep stalling because they treat AI literacy and assessment integrity as one problem. AI literacy asks how students can use AI critically, metacognitively, and in ways that build their own thinking. Assessment integrity asks how schools can know what students understand when polished digital artifacts are easy to outsource. Kentz says the two concerns overlap, but they need separate working groups, experiments, and success metrics: AI literacy work should focus on mature AI use, metacognition, and societal context, while assessment-integrity work should focus on portfolios, project-based learning, process evidence, and other ways to evaluate student thinking that may not require AI at all.

Big ideas

• District AI work is a long-term redesign project
• Learning still needs some struggle, even when AI can make things easier
• AI literacy needs different kinds of practice, not one generic skill

Claims

• Schools should separate AI literacy work from assessment integrity work
• AI literacy takes system capacity, not just tool access
• In an AI world, assessment should focus on watching students think
• AI-assisted homework requires redesign, not just policing
• AI literacy should teach students what to do with AI, not just what to think about it

Key evidence and examples

• Kentz cites faculty surveys showing widespread concern about AI-written work, over-reliance, and increased cheating.
• He distinguishes assessment integrity from AI literacy: one is about whether student work remains reliable evidence of thinking, while the other is about helping students use AI critically and metacognitively.
• He says combined approaches, such as comparative transcript analysis, can work but place a heavy cognitive burden on students and educators when scaled.
• He recommends separate AI Literacy and Assessment Integrity working groups, with separate experiments and success metrics.
• The AI literacy track should focus on mature AI use, metacognition, skills in the chat, and social context.
• The assessment-integrity track should focus on portfolios, project-based learning, process-based evaluation, and conversation-as-artifact approaches.

Education relevance

Very high relevance for school AI strategy, faculty and teacher professional learning, assessment redesign, academic integrity, K-12 and higher education AI governance, and Clay’s likely conversations with educators who may conflate AI literacy with cheating prevention.

My notes

• Clay noted that this distinction will matter because conversations with teachers about AI literacy and conversations about assessment will constantly get in each other’s way unless the two tracks are named separately.