Travis M. Loux
2026.1.1Journal of Data Science
Abstract
concepts seemed too distant from their interests and too much coding created barriers. In more advanced courses students certainly should be developing coding skills and under- standing of fundamental concepts and best practices. In this setting, we need to treat AI as a new tool to help with the mundane tasks, not to replace human engagement (Mollick, 2024). This will require giving some amount of grace to students in terms of AI and academic integrity, as we are all learning these new tools and their capabilities together. We cannot boil such a so- phisticated tool down to a list of dos and don’ts in a syllabus statement, nevertheless one simple enough we expect students to fully understand and remember (Bertram Gallant and Rettinger, 2025). Our expectations may even change throughout the semester. Academic integrity policies which are too rigid with regards to AI might scare students from seeking honest feedback about their workflow and stifle their long-term growth (Bertram Gallant and Rettinger, 2025). I am experimenting with the language of allowing “judicious use” of artificial intelligence in my aca- demic integrity policies. This includes a plan to have one-on-one and small group discussions as needed to focus on why students should build their skills independent of AI while also learning the capabilities and limitations of AI as a partner or assistant. This obviously creates some grey area and students refusing to accept this guidance still need to be held to a high standard of academic integrity. However, a more collaborative approach has the potential to balance the needs to learn foundational concepts and become a modern professional (Yeager, 2024). Laying out assessment options is a great starting point. As instructors we should also be taking the next step to reconsider what and how we teach on a more holistic level. AI has created something of a “John Henry” moment, raising the floor for what can be considered acceptable work or meaningful learning and knowledge. This is true of both student work as well as post- graduation professional expectations. It is our job as instructors and experts in our fields to make sure our students don’t drown in self-doubt, existential dread, or complacency when facing this new reality. The stakes for our students’ careers have been raised. Students need us to step up and help them reach these new expectations.
Citation format
LOUX, Travis M. Discussion of “addressing the challenges of AI-Generated assignment submissions in education: Insights and strategies”. Journal of Data Science, 2026.