Recent research has investigated how students perceive generative AI tools (Amani et al., 2023; Chan & Hu, 2023; Johnston et al., 2024), little is known about how AI use reshapes their underlying information assumptions and behaviors. This study examines how the information behaviors of first-year students have evolved in an information landscape that includes AI generated text and images. Using a survey adapted from Cole, Napier, and Marcum and a complementary interview protocol, we will share our analysis of new 2025 data on the potential impact of generative AI through a longitudinal study with data starting from 2017. Our results probe critical topics such as students’ evaluation of image authenticity, their use of generative AI in searches, and how they assess the reliability of AI-generated textual information. This research provides a unique perspective on first-year students’ understanding of authenticity, evaluation practices, and generative AI’s limitations.
Participants will engage with results to gain practical strategies for integrating these insights into library instruction and basic AI education, such as considering a Wikipedia-first approach to pre-research to avoid AI hallucinations. The session will include reflective questions, giving space for anecdotal experiences to be compared to our findings; collaborative tools like Padlet will encourage active participation and anonymous sharing of implementation ideas for practices at their own institutions.
At the end of this presentation, participants should be able to:
• Identify how Gen AI influences students’ information evaluation behavior.
• Understand some of the changes in information beliefs based on AI
• Apply our findings to their own IL teaching practices
Participants are encouraged to bring their electronic devices. Presenters will need to be able to project a PowerPoint presentation on a screen and have microphones for accessibility. Target audience: Any librarian who works with students, particularly at a college or university.
References
Amani, S., White, L., Balart, T., Arora, L., Shryock, K. J., Brumbelow, K., & Watson, K. L. (2023). Generative AI Perceptions: A Survey to Measure the Perceptions of Faculty, Staff, and Students on Generative AI Tools in Academia. [arXiv preprint]. Retrieved 17 August, 2025 from https://arxiv.org/abs/2304.14415
Chan, C. K., & 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). https://doi.org/10.1186/s41239-023-00411-8
Cole, A., Napier, T., & Marcum, B. (2015). Generation Z: Facts and fictions. In H. Jagman, & T. Swanson, Not Just Where to Click: Teaching Students How to Think About Information. Chicago: American Library Association.
Johnston, H., Wells, R. F., Shanks, E. M., Boey, T., & Parsons, B. N. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educaitonal Integrity, 20(1). https://doi.org/10.1007/s40979-024-00149-4
