Exploring the Effects of Generative Artificial Intelligence on Learning Effectiveness Through Motivations

Generative artificial intelligence (GenAI) is an important learning tool in academia and practice for the development of contemporary science and technology. However, few studies have investigated the effect of GenAI on perceived learning effectiveness (LE) through intrinsic psychological states and extrinsic technological features concerning motivation. This study developed and validated an integrated framework based on flow theory (Flow), acting as an intrinsic motivation with its antecedents, interactivity (INT), and challenges (CHA), and the technology acceptance model (TAM), acting as an extrinsic motivation. Data collected from 112 respondents in Macau were tested against the research model using the partial least squares approach. The results indicate that INT and CHA positively influence flow (Flow), which, in turn, promotes LE and attitudes (ATT) toward GenAI. Furthermore, perceived ease of use (PEU) plays a critical role in determining perceived usefulness, which affects subsequent ATT and, ultimately, LE, although PEU does not have a significant effect on ATT. The findings reveal that joint intrinsic and extrinsic motivation could significantly improve learners’ LE in a GenAI-using environment.