Extended Abstract
Reports of generative AI’s (GenAI’s) alarming influence on users’ social, psychological, and affective states proliferate. GenAI attunes to specific contexts and individual users’ emotional states, desires, and vulnerabilities (Nash, 2024), yet much extant research on GenAI in education treats it as a relationally neutral tool. As learning engineers, our aim is to design human-centered solutions to the challenges posed to education in an AI-saturated society.
To better understand these challenges, we explored the following questions in one micro-cycle nested within the “Challenge” phase of our larger project (Craig et al., 2025):
How do university students use GenAI?
What range of affective experiences do students report in their interactions with GenAI, and how do these experiences impact their academic engagement, social interactions, and personal well-being?
Preliminary results from thematic analysis of an initial student interview include (a) anxiety related to uncertain or ignored academic expectations for AI use and (b) creative uses of AI for personal purposes. We present an agenda for continuing research on GenAI that centers the social and emotional well-being of students. By better understanding these increasingly urgent issues we can work toward uses of GenAI that prioritize human flourishing.
Funding for this research was provided in part by MLFTC: Division of Educational Leadership and Innovation at ASU.
Craig, S. D., Avancha, K., Malhotra, P., Gorman, J. C., Verma, V., Likamwa, R., ... & Goldberg, B. (2025). Using a Nested Learning Engineering Methodology to Develop a Team Dynamic Measurement Framework for a Virtual Training Environment. In International Consortium for Innovation and Collaboration in Learning Engineering (ICICLE) 2024 Conference Proceedings: Solving for Complexity at Scale.
Nash, B. L. (2024). Love and Learning in the Age of Algorithms: How Intimate Relationships with Artificial Intelligence May Shape Epistemology, Sociality, and Linguistic Justice. Reading Research Quarterly, 59(4), 624–631. Academic Search Ultimate. https://doi.org/10.1002/rrq.549
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