Harnessing Emotions in Generative AI-Enabled Learning: A Multigroup Analysis of Chinese Undergraduates Using an Appraisal-Based Model (Poster 5)

• In Event: AERA Poster Session Wednesday 4:20 p.m.
  In Poster Session: Technology, Instruction, Cognition, and Learning (TICL) Poster Session

Abstract

This study explores factors influencing undergraduates’ generative AI-enabled learning productivity using the appraised-based model. It examines how primary appraisal (social influence, hedonic motivation, anthropomorphism) and secondary appraisal (cognitive appraisals, emotions) of generative AI affect learning productivity, and whether these relationships differ across gender, grade, major, and family location. The investigation of 2,026 Chinese undergraduates revealed that social influence and hedonic motivation positively related to challenge appraisal, while anthropomorphism showed the opposite. Challenge appraisal positively correlated with achievement and challenge emotions, while hindrance appraisal positively related to loss and deterrence emotions. Achievement emotions positively correlated with AI-enabled learning productivity, while loss emotions negatively correlated. The influential mechanism varied across undergraduates’ diverse backgrounds, offering insights for tailoring educational strategies in higher education.

Authors

• Chan Wang, The Chinese University of Hong Kong

• Hongbiao Yin, The Chinese University of Hong Kong