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atgadgetgirl.bsky.social
@atgadgetgirl.bsky.social
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drjustincaouette.bsky.social
Can AI detection tools intended to uphold academic integrity do more harm than good? This paper aims to show how false positives unfairly target non-native English speakers and unique writing styles (e.g., researchers with autism), sparking anxiety and distrust.

doi.org/10.1080/0361...
Screen shot of the abstract to the linked article.
December 17, 2024 at 11:07 PM
Reposted
swanpsych.com
Brian Stone @Boise State finds that Ss at his school show that this most recent sem, more than half of students used #AI in their schoolwork.

Abt ~10% reported false accusations.

We’ve got a long road ahead…

#academicsky #psychsky #highered

www.brianwstone.com/wp-content/u...
Screenshot of research article with the following text: The Science of Teaching and Learning Corner Generative Al in Higher Education: Uncertain Students, Ambiguous Use Cases, and Mercenary Perspectives SOCIETY& THE TEACHING OF PSYCHOLOGY Teaching of Psychology I-10 © The Author(s) 2024 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/00986283241305398 journals.sagepub.com/home/top § Sage Brian W. Stone ID Abstract Background: Students in higher education are using generative artificial intelligence (Al) despite mixed messages and contradictory policies. Objective: This study helps answer outstanding questions about many aspects of Al in higher education: familiarity, usage, perceptions of peers, ethical/social views, and Al grading. Method: I surveyed 733 undergraduates. Results: Students reported mixed levels of experience with Al and tended toward nervousness over excitement. Most reported professors addressing Al but not integrating it. While 41% of students had used Al in ways explicitly banned, many more students (59%) reported ambiguous use cases. Students overestimated peer cheating, and this predicted their own cheating, as did general experience with and excitement about Al. Meanwhile, 11% of students reported false accusations, with first-generation students possibly at a higher rate. Pragmatic views about career and inequality may be affecting behaviors. Men consistently reported more involvement with Al than women. Conclusion: Future research should focus on the hybrid collaboration of humans and Al and how Al might be leveraged to support and scaffold genuine learning. Teaching Implications: Al will be relevant to many future careers, and students increasingly want it to be part of their education. Academic integrity will be a continuing challenge, and students need transparency. Keywords technology artificial intelligence, generative Al, academic integrity, false accusations, detection, bias, ethics, inequality, workforce, educ…
January 3, 2025 at 11:46 PM