AI Learning Strategies Scale: Development and Validation for Individual Differences in Self-Regulated Learning with AI

As AI-powered tools become embedded in learning environments, understanding how learners regulate their interactions with these systems is essential for designing learner-sensitive, adaptive AI-supported instruction. Yet existing self-regulated learning measures do not capture the regulatory demands of learning with generative AI nor the individual differences in how learners navigate them. This research conceptualises AI learning strategies as the strategic ways learners use AI tools to support and regulate their learning, grounded in a self-regulated learning framework spanning forethought, performance, and self-reflection phases. Across five studies (N = 1001), we developed and validated the 12-item AI Learning Strategies Scale and mapped its nomological network. Study 1 generated and content-validated scale items. Study 2 identified a three-factor structure (Forethought, Performance, and Self-Reflection) via exploratory factor analysis. Studies 3 and 4 confirmed this three-factor model, demonstrated strong internal consistency (αs = .94–.95), robust factor loadings, sex invariance, and convergent and discriminant validity. Within the nomological network, greater AI learning strategy use was associated with higher self-efficacy, autonomous motivation, mastery-approach goals, conscientiousness, and extraversion, as well as more positive attitudes toward AI, more frequent and diverse AI use, and lower AI fatigue and neuroticism. Study 5 demonstrated one-week test–retest reliability (ICC(2,1) = .80) and criterion validity, with higher scores predicting richer self-regulated learning content in a writing task. The present work clarifies how learners differ in their strategic use of AI for learning, and provides a validated measure of individual differences to support adaptive, theory-driven, and cross-group research in technology-enhanced learning environments.