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Motivation, Mindsets, and AI: Understanding Learner Attributes in AI-Enhanced Education
Thu, April 9, 4:15 to 5:45pm PDT (4:15 to 5:45pm PDT), JW Marriott Los Angeles L.A. LIVE, Floor: Ground Floor, Gold 4Session Type: Roundtable Session
Abstract
These studies examine how personal attributes shape learners’ engagement with Generative AI (GenAI) tools across educational contexts. Structural models reveal that motivation mediates the effects of self-efficacy and attitude on strategy and resource use in AI-integrated language learning. Students’ GenAI help-seeking is influenced by trust, norms, and perceived control, though not directly linked to help-seeking intentions. A brief video intervention improved STEM students’ attitudes toward learning-centered AI use. High school students with low engagement and belonging were more likely to use AI for academic shortcuts. Finally, GenAI acceptance in EFL learning predicted achievement through performance and effort expectancy. Together, these findings highlight the importance of motivational and contextual factors in shaping responsible and effective AI use in education.
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Papers
CTRL + THINK: Changing STEM Students’ Attitudes about Using AI in Learning-Centered Ways - Carlton J. Fong, Texas State University; Pedram Zarei, Texas State University; Zachary Baquet, University of Houston; Stephen Oluwaseyi Maku, Texas State University; IMANEH SOLEIMANI, Texas State University; Zohreh Fathi, Texas State University; Pegah Peimani, Texas State University
From acceptance to achievement: Exploring learners’ perceptions of GenAI tools in EFL learning - Zhenlei Huang, Shanghai International Studies University; Hui Jin, Shanghai International Studies University
Investigating the Drivers of Generative AI Help-Seeking: Structural Equation Model of Attitudes, Norms, and Control - Andrea Macias, University of Southern California; Stephen J. Aguilar, University of Southern California
Motivation, Belonging, and Perceptions of School Work Behind High School Students’ AI Usage Patterns - Ruishi Chen, Stanford University; Sarah Miles, Challenge Success; Annie Camey Kuo, Stanford University; Denise C. Pope, Stanford University; Victor R. Lee, Stanford University
Pathways to Proficiency: Mapping the Structural Relationships of Personal Attributes in AI-Integrated Self-Directed Language Learning - Belle Li, Purdue University; Zhuo Zhang, Towson University; Victoria Lynn Lowell, Purdue University