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Design and Validation of the AI‐Integrated Metacognitive Learning Resilience Scale (AIIMLR Scale) for Secondary School Students in Jordan: Insights From the Network Analysis Perspective.

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Bibliographic Details
Title: Design and Validation of the AI‐Integrated Metacognitive Learning Resilience Scale (AIIMLR Scale) for Secondary School Students in Jordan: Insights From the Network Analysis Perspective.
Authors: Ayasrah, Mohammad Nayef, Khasawneh, Mohamad Ahmad Saleem, Almulla, Mazen Omar, Aboutaleb, Amoura Hassan
Source: Journal of Computer Assisted Learning. Oct2025, Vol. 41 Issue 5, p1-23. 23p.
Subjects: Psychological resilience, Scale analysis (Psychology), Emotion regulation, Cognitive testing, Cronbach's alpha, Data analysis, Research funding, High school students, Artificial intelligence, Research methodology evaluation, Interviewing, Research evaluation, Learning, Chi-squared test, Descriptive statistics, Self-control, Problem solving, Experimental design, Research methodology, Psychometrics, Cluster sampling, Intraclass correlation, Statistical reliability, Statistics, Factor analysis, Calibration, Reliability (Personality trait), Discriminant analysis, Adolescence
Geographic Terms: Jordan
Abstract: Background: One area that has been dramatically changed by artificial intelligence (AI) is educational environments. Chatbots, Recommender Systems, Adaptive Learning Systems and Large Language Models have been emerging as practical tools for facilitating learning. However, using such tools appropriately is challenging. In this regard, the construct of metacognitive learning resilience has been receiving growing attention, especially in the face of uncertainties and adversities associated with AI‐supported learning. Objectives: The current research aimed to develop and evaluate the psychometric properties of the AI‐Integrated Metacognitive Learning Resilience Scale (AIIMLR Scale). This scale was developed to assess students' ability to cognitively and emotionally manage learning challenges in AI‐enhanced learning settings. Methods: This study, which had a mixed‐method research design, was performed in Jordan in 2025. A pool of items, developed based on a systematic review of theoretical literature and semi‐structured interviews, was used. Then, content validation and the pilot phase were used to modify items. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), exploratory graph analysis (EGA) and Random Forest Modelling (RFM) were used to assess construct validity of this scale. In addition, Cronbach's alpha (α) and McDonald's omega (ω) were used to assess reliability. Finally, the intraclass correlation coefficient (ICC) was performed in addition to evaluating test–retest reliability. Results and Conclusions: EFA results revealed six factors: Self‐Awareness and Metacognitive Regulation in AI‐Mediated Learning; Cognitive Adaptability in Dynamic AI‐Based Learning Contexts; Emotional Stability During AI‐Integrated Learning Challenges; Strategic Perseverance in AI‐Supported Problem‐Solving; Motivational Resilience Amid AI‐Driven Learning Difficulties; and Reflective Recalibration of Learning through AI Feedback. These six factors collectively explained 66.21% of the total variance. CFA fit indices (CFI = 0.917, RMSEA = 0.079) and reliability indicators, including Cronbach's alpha (0.897–0.948), McDonald's omega (0.892–0.950) and Composite Reliability (CR: 0.888–0.954), were all within acceptable ranges. Moreover, convergent and discriminant validity were confirmed using the Average Variance Extracted (AVE). The measurement invariance test across gender indicated that the scale maintains stable measurement properties for both males and females. Findings suggest that the AIIMLR Scale is a valid and reliable tool for assessing metacognitive learning resilience in AI‐enhanced educational settings. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
Description
Abstract:Background: One area that has been dramatically changed by artificial intelligence (AI) is educational environments. Chatbots, Recommender Systems, Adaptive Learning Systems and Large Language Models have been emerging as practical tools for facilitating learning. However, using such tools appropriately is challenging. In this regard, the construct of metacognitive learning resilience has been receiving growing attention, especially in the face of uncertainties and adversities associated with AI‐supported learning. Objectives: The current research aimed to develop and evaluate the psychometric properties of the AI‐Integrated Metacognitive Learning Resilience Scale (AIIMLR Scale). This scale was developed to assess students' ability to cognitively and emotionally manage learning challenges in AI‐enhanced learning settings. Methods: This study, which had a mixed‐method research design, was performed in Jordan in 2025. A pool of items, developed based on a systematic review of theoretical literature and semi‐structured interviews, was used. Then, content validation and the pilot phase were used to modify items. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), exploratory graph analysis (EGA) and Random Forest Modelling (RFM) were used to assess construct validity of this scale. In addition, Cronbach's alpha (α) and McDonald's omega (ω) were used to assess reliability. Finally, the intraclass correlation coefficient (ICC) was performed in addition to evaluating test–retest reliability. Results and Conclusions: EFA results revealed six factors: Self‐Awareness and Metacognitive Regulation in AI‐Mediated Learning; Cognitive Adaptability in Dynamic AI‐Based Learning Contexts; Emotional Stability During AI‐Integrated Learning Challenges; Strategic Perseverance in AI‐Supported Problem‐Solving; Motivational Resilience Amid AI‐Driven Learning Difficulties; and Reflective Recalibration of Learning through AI Feedback. These six factors collectively explained 66.21% of the total variance. CFA fit indices (CFI = 0.917, RMSEA = 0.079) and reliability indicators, including Cronbach's alpha (0.897–0.948), McDonald's omega (0.892–0.950) and Composite Reliability (CR: 0.888–0.954), were all within acceptable ranges. Moreover, convergent and discriminant validity were confirmed using the Average Variance Extracted (AVE). The measurement invariance test across gender indicated that the scale maintains stable measurement properties for both males and females. Findings suggest that the AIIMLR Scale is a valid and reliable tool for assessing metacognitive learning resilience in AI‐enhanced educational settings. [ABSTRACT FROM AUTHOR]
ISSN:02664909
DOI:10.1111/jcal.70127