“Help-Seeking 2.0”: Exploring GenAI Help-Seeking Among STEM Undergraduates Using an Expectancy-Value-Cost Framework

• In Event: AERA Roundtable Session Saturday 1:45 pm - Gold 4
  In Roundtable Session: Framing Effort, Building Efficacy: Motivational Insights Across Contexts

Abstract

Discussions on Generative AI (GenAI) and student learning are shifting from not whether students are allowed to use GenAI to how educators and institutions may support students’ use of GenAI to promote learning. Specifically, it is important to understand the factors linked to students’ seeking help from GenAI in both expedient versus instrumental ways. Using an expectancy-value-cost framework, we explored which motivational factors were associated with expedient and instrumental GenAI help-seeking among college students in STEM. The more students had low expectancy and intrinsic value but higher cost perceptions, the more they sought expedient help from GenAI. However, if students had high utility value, they tended to use GenAI more instrumentally. Implications for theory and practice are discussed.

Authors

• Zohreh Fathi, Texas State University

• Pedram Zarei, Texas State University

• Pegah Peimani, Texas State University

• IMANEH SOLEIMANI, Texas State University

• Zachary Baquet, University of Houston

• Stephen Oluwaseyi Maku, Texas State University

• Carlton J. Fong, Texas State University