Legislator Briefing Prep Pack

Source-grounded context summary

The paper addresses a public higher-education concern: generative AI is making it harder to know whether online assessment reflects student learning. It proposes random learning audits as one possible response that may avoid some concerns associated with surveillance-heavy tools.

60-second briefing script

Generative AI has made online assessment more complicated for colleges and universities. This working paper looks at the issue through incentive design. Instead of trying to monitor every student all the time, it asks whether random, brief learning-verification conversations could make cheating too risky to be attractive. The proposal is not ready-made policy. It would need careful pilots, accessibility safeguards, due process, and alignment with institutional academic integrity rules.

State / district relevance

• Online and hybrid learning are part of the higher-education landscape.
• Institutions need integrity systems that protect honest students without defaulting to invasive surveillance.
• Pilot-based approaches can help campuses evaluate what actually works before broad adoption.

Likely skeptical questions

• Would this increase faculty workload?
• Could students be unfairly accused?
• Does this discourage responsible AI use?

Policy / lobbying boundaries

Keep the conversation educational and nonpartisan. Do not ask for legislation or funding unless a separate approved institutional agenda exists.