{"id":4073,"date":"2026-02-10T19:51:56","date_gmt":"2026-02-10T19:51:56","guid":{"rendered":"https:\/\/www.mtsu.edu\/online\/?page_id=4073"},"modified":"2026-08-05T15:47:18","modified_gmt":"2026-08-05T15:47:18","slug":"assessments","status":"publish","type":"page","link":"https:\/\/www.mtsu.edu\/online\/assessments\/","title":{"rendered":"Designing Assessments and Learning Activities That Discourage AI Cheating"},"content":{"rendered":"\n
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\"Teaching<\/figure>\n\n\n\n

If institutions have reached one conclusion quickly, it\u2019s this:<\/p>\n\n\n\n

The most effective response to AI-related cheating is not surveillance\u2014it\u2019s design.<\/p>\n\n\n\n

A more productive question than \u201cHow do I stop students from using AI?\u201d is:<\/p>\n\n\n\n

\u201cHow do I design learning activities where outsourcing the thinking simply doesn\u2019t work?\u201d<\/p>\n\n\n\n

This reframing shifts energy away from enforcement and toward instructional control. Faculty cannot fully control technology. But they can control assignment structure, visibility of learning, and clarity of expectations.<\/p>\n\n\n\n

When assessments are meaningful, contextual, and process-oriented, AI becomes either irrelevant\u2014or obviously insufficient.<\/p>\n\n\n\n

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