Associations between future orientation and motivated learning: the roles of AI self-efficacy and intrinsic motivation among Chinese university students.

Introduction: As AI-related technologies and products become increasingly accessible in higher education, it is important to understand how students' AI-related competence beliefs are associated with motivated learning. Guided by the motivation-cognition-behavior perspective and self-determination theory, this study examined whether future orientation was associated with university students' motivated learning through AI self-efficacy and intrinsic motivation. Methods: A cross-sectional, face-to-face survey was conducted with 431 university students at a university in Hubei Province, China, using classroom-based group recruitment. Hayes' PROCESS macro Model 6 with 5,000 bias-corrected bootstrap samples was used to test the theoretically specified indirect pathways. Results: The results showed that future orientation was positively associated with motivated learning, with a standardized total effect of β = .549. The total indirect effect through AI self-efficacy and intrinsic motivation was also significant, effect = .196, 95% CI [.129, .274]. Bootstrap analyses further indicated that AI self-efficacy and intrinsic motivation statistically accounted for indirect associations between future orientation and motivated learning, both separately and in a theoretically specified serial pathway. The indirect association through intrinsic motivation was numerically larger than that through AI self-efficacy, although this difference should be interpreted cautiously given the cross-sectional design. Discussion: The findings provide preliminary evidence that future orientation, AI-related competence beliefs, and intrinsic motivation are relevant to students' motivated learning toward professional skill acquisition in university contexts where AI-related tools are increasingly accessible. Theoretical and practical implications are discussed.