“Help-seeking 2.0”: exploring how expectancy-value-cost profiles relate to GenAI help-seeking tendencies among STEM undergraduates

Abstract Discussions on Generative AI (GenAI) and student learning have shifted from whether students should be permitted to use GenAI to how educators and institutions may support its use to enhance learning. Specifically, understanding the factors associated with students’ expedient and instrumental use of GenAI for help-seeking is essential. However, less is known about the motivational factors that shape these distinct patterns of AI use. Using an expectancy-value-cost framework, we explored which motivational factors were associated with expedient and instrumental GenAI help-seeking among 389 college students studying in STEM disciplines. We used a latent profile approach to create various profiles of STEM-specific motivational beliefs. Profile membership predicted expedient and instrumental AI help-seeking in several interesting ways. Overall, profiles with higher cost perceptions towards STEM coursework were less inclined to use AI in more mastery-focused ways (lower AI instrumental help-seeking) and more inclined to use AI as a shortcut for completing assignments (higher AI expedient help-seeking). Implications for theory, practice, and future research are discussed.