Discussion of “Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies”✩
Volume 24, Issue 1 (2026): Special Issue: Statistical aspects of Trustworthy Machine Learning, pp. 274–275
Pub. online: 11 February 2026
Type: Education In Data Science
Open Access
Open Access
✩
Main article: https://doi.org/10.6339/25-JDS1208.
Published
11 February 2026
11 February 2026
References
Bertram Gallant T, Rettinger DA (2025). The Opposite of Cheating: Teaching for Integrity in the Age of AI. Teaching, Engaging, Thriving, Higher Ed. University of Oklahoma Press. https://www.oupress.com/9780806194967/the-opposite-of-cheating/
Mollick E (2024). Co-Intelligence: Living and Working with AI. Penguin Random House. https://www.penguinrandomhouse.com/books/741805/co-intelligence-by-ethan-mollick/
Sun M, Han R, Jiang B, Qi H, Sun D, Yuan Y, Huang J (2025). A survey on large language model-based agents for statistics and data science. The American Statistician, 0(0): 1–14. https://doi.org/10.1080/00031305.2025.2561140
Wang S, Xu L, Liu L, Zhai L (2026). Addressing the challenges of AI-generated assignment submissions in education: Insights and strategies. Journal of Data Science, 24(1): 1–7. https://doi.org/10.6339/25-JDS1208
Yeager D (2024). 10 to 25: The science of motivating young people: A groundbreaking approach to leading the next generation—And making your own life easier. Simon and Schuster. https://www.simonandschuster.com/books/10-to-25/David-Yeager/9781668023884
