Related Experiment Video
Updated: Jan 10, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
Published on: August 29, 2025
The development and validation of the AI-supported self-regulated science and mathematics learning scale (AI-SSRSML)
Ataallh Aodh Alatoai1, Ali Saleh Alshahri2
1Science Education, Curriculum & Instruction Department, College of Education and Arts, University of Tabuk, Tabuk, Saudi Arabia. aalatoai@ut.edu.sa.
Background:
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.
Methods:
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).
Results:
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 internal consistency and temporal stability (Cronbach's α = 0.914-0.959; McDonald's ω = 0.914-0.959; CR = 0.903-0.959; ICC = 0.758-0.869). The measurement invariance was supported by gender. The Random Forest analysis. results showed that items from the Meta-Cognitive Planning factor were the most important in predicting the overall AI-SSRSML score.
Conclusion:
The AI-SSRSML Scale is a valid, reliable, and gender-invariant instrument for measuring AI-supported self-regulated learning in secondary science and mathematics education. The scale has the potential to provide a psychometrically sound foundation for research, intervention design, and educational policy to support students' cognitive, motivational, and metacognitive engagement in technology-enhanced, growth-oriented educational settings.
More Related Videos
10:26Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
12:55Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
Published on: September 27, 2020
Related Concept Videos
Reliability and Validity
Self-Efficacy
Self-Evaluation Maintenance Model
Metacognition
Self-Regulation