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:

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

SUBMITTER: Alatoai AA 

PROVIDER: S-EPMC12670807 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature