Design and Validation of the AI‐Integrated Metacognitive Self‐Regulation of Future Thinking Scale (MSRFTS) for Female University Students.
Objectives: The purpose of this article is to design and validate the AI‐Integrated Metacognitive Self‐Regulation of Future Thinking Scale (MSRFTS), an instrument developed to assess students' ability to plan, monitor, and adapt their future thinking strategies in AI‐enhanced learning environments....
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 1; pp. 1 - 24 |
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| Formato: | research tables/charts Journal Article |
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Wiley-Blackwell
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191181627&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191181627 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2026 vid: 42 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 191181627 191181627 191181627 10.1002/jcal.70175 191181627 ppf: 1 ppct: 23 formats: tig: atl: Design and Validation of the AI‐Integrated Metacognitive Self‐Regulation of Future Thinking Scale (MSRFTS) for Female University Students. aug: au: AlMuhaysh, Hissah Fahad affil: Department of Curricula And Teaching Methods, King Faisal University, Al‐Ahsa, Saudi Arabia sug: subj: Instrument Construction Instrument Validation Scales Psychometrics Evaluation Artificial Intelligence Cognition Self Regulation Thinking Students, Undergraduate Psychosocial Factors Learning Environment Women Human Saudi Arabia Female Young Adult Validation Studies Exploratory Research Multimethod Studies Conceptual Framework Semi-Structured Interview Purposive Sample Stratified Random Sample Internal Consistency Face Validity Maximum Likelihood Predictive Validity Factor Analysis Criterion-Related Validity Reliability Coefficient alpha Intraclass Correlation Coefficient Test-Retest Reliability Random Forest Data Analysis Software Descriptive Statistics Funding Source Female ab: Objectives: The purpose of this article is to design and validate the AI‐Integrated Metacognitive Self‐Regulation of Future Thinking Scale (MSRFTS), an instrument developed to assess students' ability to plan, monitor, and adapt their future thinking strategies in AI‐enhanced learning environments. The scale is specifically contextualised for female university students in Saudi Arabia and is intended to provide evidence regarding its structural validity, psychometric functioning, and predictive importance. Methods: This exploratory mixed‐methods study was conducted in 2025 in Saudi Arabia. The initial items were developed through a systematic literature review and semi‐structured interviews. Quantitative data were collected from 888 female students across multiple disciplines using stratified random sampling. Psychometric analyses included Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Exploratory Graph Analysis (EGA), and Random Forest Modelling (RFM). Reliability was evaluated using Cronbach's alpha (α) and McDonald's omega (ω), while test–retest reliability was assessed using the Intraclass Correlation Coefficient (ICC) over a 2‐week interval. Measurement invariance was examined across academic semester levels. Results and Conclusions: EFA identified five key factors—Future Goal Clarification, Strategic Future Planning, Adaptive Future Reappraisal, Reflective Anticipation, and Temporal Monitoring of Progress—that together explained 67.10% of the total variance. CFA results demonstrated acceptable fit indices (CFI > 0.91, RMSEA = 0.073). Reliability indicators showed strong internal consistency (α and ω > 0.80), and ICC values (0.89–0.95) indicated high test–retest stability. EGA confirmed the robustness of the five‐factor structure, and Random Forest Modelling revealed that Future Goal Clarification had the highest predictive importance for overall MSRFT levels. Measurement invariance testing confirmed the scale's structural stability across academic subgroups. Collectively, these findings indicate that the MSRFTS is a valid and reliable tool for assessing metacognitive self‐regulation of future thinking in AI‐mediated learning contexts among female university students and for informing research and interventions in AI‐enhanced self‐regulated learning in higher education. Practitioner Notes: What is currently known about this topic? ○Metacognition and future thinking support AI‐based learning success.○No validated tool links AI, metacognition, and future orientation.○Female learners face cultural and gendered barriers in tech use.○Self‐regulated learning is key for digital and career readiness.What does this paper add? ○Introduces MSRFTS, a validated five‐factor measurement scale.○Tailored to female Saudi students in AI‐mediated learning.○Confirms strong validity, reliability, and structural stability.○Reveals 'future goal clarification' as the most predictive factor.Implications for practice and/or policy ○Guides AI‐based, student‐centred teaching and curriculum design.○Enables targeted support for female learners in digital contexts.○Informs policies to enhance women's digital confidence and skills.○Provides a robust tool for quality assurance in higher education. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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