AI Chatbots as Academic Assistants: Do Frequency and Perceived Usefulness Drive Learning Motivation? - Rut Penina Somisu (Journal of Innovative and Creativity, 2025)
The study purpose was to examine whether the frequency of using AI chatbots and students’ perceived usefulness of those chatbots are associated with learning motivation among economics undergraduates at Cenderawasih University. Materials and methods. A quantitative explanatory, cross-sectional survey was administered to 89 economics students selected proportionally across semesters. Learning motivation was measured via indicators of interest in study, enthusiasm for assignments, satisfaction with learning, and consistency in study habits. After pilot testing and instrument refinement, data were analyzed using simple linear regression (overall utilization) and multiple regression (frequency and perceived usefulness), following normality, multicollinearity, and heteroscedasticity checks. Results show a statistically significant model (F = 54.442, p < .001) explaining a substantial share of variance in learning motivation (R² = 0.559; Adjusted R² = 0.548). Perceived usefulness exerted a larger effect (β = 0.550) than frequency (β = 0.343), with both predictors highly significant (p < .001). The joint model of frequency and perceived usefulness predicted motivation robustly (p < .001), supporting all three hypotheses.