The development and validation of the AI-supported self-regulated science and mathematics learning scale (AI-SSRSML) among secondary school students in Saudi Arabia
Abstract
This study aimed to develop and validate the AI-Supported Self-Regulated Science and Mathematics Learning Scale (AI-SSRSML Scale) for technology-enhanced educational settings in Tabuk Province, Saudi Arabia. This study used an exploratory sequential mixed-methods design. In 2025, data were collected from students in Tabuk Province, Saudi Arabia. The scale was developed using a systematic literature review, 18 semi-structured interviews with secondary students, and iterative psychometric testing. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), exploratory graph analysis (EGA), convergent and discriminant validity, measurement invariance by gender, and an exploratory Random Forest regression model were used to analyze data from a pilot study (n = 60) and a large-scale survey (n = 920) to establish the construct validity and predictive utility of the scale. Reliability was investigated using Cronbach’s alpha, McDonald’s omega, composite reliability (CR), and intraclass correlation coefficients (ICC). EFA and CFA. results supported a six-factor solution: Meta-Cognitive Planning, Growth Mindset Endorsement, Self-Monitoring and Reflection, Strategic AI Utilization, Motivational Resilience, and Adaptive Help-Seeking, which accounted for 62.3% of the variance. The final AI-SSRSML Scale included 48 items (eight items per factor). The final model had acceptable fit indices (RMSEA = 0.075, CFI = 0.928, TLI = 0.911), and reliability indices showed excellent…
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