Associations between future orientation and motivated learning: the roles of AI self-efficacy and intrinsic motivation among Chinese university students - Report - MDSpire
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Associations between future orientation and motivated learning: the roles of AI self-efficacy and intrinsic motivation among Chinese university students
Links Between Future Orientation and Motivated Learning in Chinese Students
Overview
This study investigates the relationship between future orientation, AI self-efficacy, and intrinsic motivation in university students, revealing a significant positive association with motivated learning.
Background
Understanding the factors that influence motivated learning is crucial in higher education, especially as AI technologies become more prevalent. Future orientation may play a role in academic engagement.
Data Highlights
Measure
Effect Size
Future Orientation to Motivated Learning
β = .549
Total Indirect Effect through AI Self-Efficacy and Intrinsic Motivation
Effect = .196, 95% CI [.129, .274]
Key Findings
Future orientation is positively associated with motivated learning (b2 = .549).
The total indirect effect through AI self-efficacy and intrinsic motivation is significant (effect = .196, 95% CI [.129, .274]).
AI self-efficacy and intrinsic motivation account for indirect associations between future orientation and motivated learning.
The indirect association through intrinsic motivation is numerically larger than through AI self-efficacy.
Clinical Implications
The study emphasizes the need for educational strategies that enhance students' future orientation and intrinsic motivation, particularly in AI-integrated learning environments. Understanding these psychological factors can inform the development of effective teaching methods in higher education.
Conclusion
The findings highlight the association of future orientation, AI self-efficacy, and intrinsic motivation with motivated learning among university students.
