AI-CED IT! Student Training and Student Research on Engagement
Presented by:
Summer Roberts, University of South Carolina Beaufort
Students’ voices are missing from the conversation surrounding AI. Explore how to center training on students and allow students to drive AI research.
Keywords:
Student Research, AI Training, Academic Integrity
Abstract:
As AI usage expands across academic institutions, students remain largely the focus of concern. However, students are often not part of academic integrity policymaking, and AI trainings are geared toward faculty. This session describes efforts to center students in the AI conversation, by pilot-testing training to help students navigate best practices for employing AI tools, within the limits of class policies, and through student-led research to examine perceptions and use of AI within the university. The session aims to encourage more active involvement of students’ voices in how AI tools can be employed while maintaining academic integrity.
Outcomes:
1. Identify outcomes for cross-disciplinary student AI training at other universities.
2. Compare student-driven research on AI usage to faculty research goals.
3. Assess how AI tools can be integrated in ethical academic work.
Hear it from the author:
Transcript:
Hi. My name is Summer Roberts, and I'm looking forward to sharing our work involving students in the conversation surrounding AI and academic integrity. This work began as the result of a grant from the Aspire-AI program the University of South Carolina that Dr Carmen Brown Farrell and I received to develop a training program for students, helping them to consider practical and ethical uses of AI in their lives while critically evaluating its outputs.
Emerging from this work, I implemented a focus in my research methods and research seminar sequence for the 2025-2026 academic year, asking students to develop their own studies on academic integrity and AI. Among the interesting directions their research has taken, one honors student asked to assist me in development of a university-level academic integrity module, being designed with a focus on AI-literacy.
We hope you’ll find this poster useful for considering how to positively center students in our approaches to AI. Thank you.
References:
Baidoo-Anu, D., Asamoah, D., Amoako, I., & Mahama, I. (2024). Exploring student perspectives on generative artificial intelligence in higher education learning. Discover Education, 3(1), Article 98. https://doi.org/10.1007/s44217-024-00173-z
Baek, C., Tate, T., & Warschauer, M. (2024). “ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI. Computers and Education: Artificial Intelligence, 7, Article 100294. https://doi.org/10.1016/j.caeai.2024.100294
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 1–18. https://doi.org/10.1186/s41239-023-00411-8
Dabis, A., & Csáki, C. (2024). AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI. Humanities and Social Sciences Communications, 11, Article 1006. https://doi.org/10.1057/s41599-024-03526-z
Fošner, A. (2024). University students’ attitudes and perceptions towards AI tools: Implications for sustainable educational practices. Sustainability, 16(19), Article 8668. https://doi.org/10.3390/su16198668
Kelly, A., Sullivan, M., & Strampel, K. (2023). Generative artificial intelligence: University student awareness, experience, and confidence in use across disciplines. Journal of University Teaching & Learning Practice, 20(6), Article 12. https://doi.org/10.53761/1.20.6.12