PnB-T01 - Alternative Grading as a Way to Disincentivize Unauthorized AI Use 

Information

This poster will share examples of how two statistics faculty use alternative grading in our courses and how these methods align with research on ways to reduce student incentive to cheat. With generative AI usage on the rise, many instructors are seeking solutions to grapple with AI-enabled cheating. Though academic integrity cases did not drive our change to alternative grading policies, both instructors believe the policies have decreased the incentives to cheat, including with AI. We have developed approaches that share important similarities while also differing according to our own teaching styles and preferences. We will include student feedback on the grading approaches, our own perceptions of how student AI use has decreased, as well as pros and cons of these approaches. Attendees will leave with some examples of ways they could begin incorporating similar strategies, with or without a full grading overhaul. We teach at a mid-sized private liberal arts university with class sizes typically 20-30 students. Our approaches have been used in introductory courses for non-majors as well as advanced undergraduate classes largely taken by statistics majors and minors. Our conclusions on the effect of the alternative grading policies are drawn from our experience teaching this range of courses and students. We will share student quotes from IRB-approved student surveys addressing their learning and experiences with these grading methods. We will also share how these methods align with other research about cheating and AI use.