Exploring large language models as an integrated tool for learning, teaching, and research through the Fogg Behavior Model: a comprehensive mixed-methods analysis

Abstract

Large language models (LLMs) are a recent advancement in artificial intelligence that has the potential to revolutionize learning, teaching, and research. Still, there is room for improvement regarding how effectively LLMs could be incorporated into these environments. This study investigated the role of LLMs, specifically ChatGPT, in learning, teaching, and research contexts. To understand how motivation, ability, and triggers influence the behavior of undergraduate students, teachers, and research scholars toward LLMs, the Fogg Behavior Model (FBM) is adopted. The study revealed that the behavior of students and researchers to apply LLMs in their respective domains was greatly influenced by their motivation and ability. However, teachers exhibited little interest in incorporating any LLMs into their pedagogical strategies. In addition to these results, participants identified limitations of ChatGPT in learning, teaching, and research fields. These insights contribute valuable perspectives on the practical implementation and effectiveness of LLMs in diverse academic and research contexts. The study sheds light on the potential benefits and challenges of integrating LLMs into educational and research environments. The findings emphasize the importance of accounting for motivational factors and individual abilities when applying such models. The study’s findings offer invaluable insights for educators and researchers to harness the potential of LLMs in educational and research environments while mitigating their limitations.

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