Online Learning Motivation and ChatGPT: A Self-Determination Theory Perspective

Abstract

Growing use of generative AI technologies like ChatGPT has changed online learning and increased student motivation. This study explores online learning motivation and ChatGPT using Self-Determination Theory (SDT) to examine competence, autonomy, and relatedness in online learners. 189 academics from various fields participated in a quantitative survey. A five-point Likert scale-based 52-item questionnaire was derived from Ryan and Deci (2000), Fowler (2018), and Youssef et al. (2024). Competence, autonomy, and relatedness were not gender-specific across academic groupings. In the descriptive study, students rated the AI system's function in critical thinking, academic accomplishment, engagement, and learning motivation positively. The greatest competency item was students' practice of cross-checking ChatGPT knowledge with independent study (M = 4.06), whereas the most autonomous item was achieving good grades (M = 4.59). Relatedness was strong in social engagement and teacher support. They liked class discussions (M = 4.00) and found course materials meaningful (M = 4.28). Positive correlations were found between competence, autonomy (r =.550, p <.001), and competence and relatedness (r =.551, p <.001). The results support the Self-Determination Theory as a valid framework for online learning motivation and show that ChatGPT can promote learners' competence, autonomy, and relatedness if responsibly integrated into online learning settings. The work has major theoretical, pedagogical, and practical consequences for higher education AI-assisted learning.