The Impact of Generative AI on Undergraduate Learning: Moderating Role of Faculty Encouragement

Abstract

The study aims to examine how academic workload and time pressure compel graduate students to use Generative Artificial Intelligence and to engage in different learning outcomes, such as cognitive, psychomotor, affective, and technical. Besides, how faculty influences create a pivotal role in the use of GenAI and learning. The study analyses data from 612 students from three different universities as the sub-sample. The study examines the direct, indirect, and moderating effects using the structural equation model and the Hayes process macro in an emerging nation. The results show that academic workload and time pressure encourage the use of GenAI, and GenAI also supports cognitive, psychomotor, affective, and technical learning. Further, academic workload and time pressure also enforce cognitive, psychomotor, affective, and technical learning. Moreover, the use of GenAI mediates academic workload and positively and significantly affects four types of learning. The study also finds that faculty member encouragement moderates positively and significantly between academic workload and technical learning, and between academic workload and affective learning. The study advances graduate students’ understanding of the impact of GenAI on workload and time pressure, and the role of GenAI in promotes cognitive, psychomotor, affective, and technical learning. The self-reported data were used in current study to do the analysis, in future it may use longitudinal and/or different data sources.

Ähnliche Arbeiten