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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| 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. |
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| 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] |
| Copyright of Journal of Computer Assisted Learning is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 188234224 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ayasrah%2C+Mohammad+Nayef%22">Ayasrah, Mohammad Nayef</searchLink><br /><searchLink fieldCode="AR" term="%22Khasawneh%2C+Mohamad+Ahmad+Saleem%22">Khasawneh, Mohamad Ahmad Saleem</searchLink><br /><searchLink fieldCode="AR" term="%22Almulla%2C+Mazen+Omar%22">Almulla, Mazen Omar</searchLink><br /><searchLink fieldCode="AR" term="%22Aboutaleb%2C+Amoura+Hassan%22">Aboutaleb, Amoura Hassan</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Computer+Assisted+Learning%22">Journal of Computer Assisted Learning</searchLink>. Oct2025, Vol. 41 Issue 5, p1-23. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Psychological+resilience%22">Psychological resilience</searchLink><br /><searchLink fieldCode="DE" term="%22Scale+analysis+%28Psychology%29%22">Scale analysis (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Emotion+regulation%22">Emotion regulation</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+testing%22">Cognitive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Cronbach's+alpha%22">Cronbach's alpha</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22High+school+students%22">High school students</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology+evaluation%22">Research methodology evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Interviewing%22">Interviewing</searchLink><br /><searchLink fieldCode="DE" term="%22Research+evaluation%22">Research evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Chi-squared+test%22">Chi-squared test</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Self-control%22">Self-control</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Experimental+design%22">Experimental design</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Psychometrics%22">Psychometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+sampling%22">Cluster sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Intraclass+correlation%22">Intraclass correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+reliability%22">Statistical reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Factor+analysis%22">Factor analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink><br /><searchLink fieldCode="DE" term="%22Reliability+%28Personality+trait%29%22">Reliability (Personality trait)</searchLink><br /><searchLink fieldCode="DE" term="%22Discriminant+analysis%22">Discriminant analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescence%22">Adolescence</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Jordan%22">Jordan</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Computer Assisted Learning is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/jcal.70127 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1 Subjects: – SubjectFull: Psychological resilience Type: general – SubjectFull: Scale analysis (Psychology) Type: general – SubjectFull: Emotion regulation Type: general – SubjectFull: Cognitive testing Type: general – SubjectFull: Cronbach's alpha Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Research funding Type: general – SubjectFull: High school students Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Research methodology evaluation Type: general – SubjectFull: Interviewing Type: general – SubjectFull: Research evaluation Type: general – SubjectFull: Learning Type: general – SubjectFull: Chi-squared test Type: general – SubjectFull: Descriptive statistics Type: general – SubjectFull: Self-control Type: general – SubjectFull: Problem solving Type: general – SubjectFull: Experimental design Type: general – SubjectFull: Research methodology Type: general – SubjectFull: Psychometrics Type: general – SubjectFull: Cluster sampling Type: general – SubjectFull: Intraclass correlation Type: general – SubjectFull: Statistical reliability Type: general – SubjectFull: Statistics Type: general – SubjectFull: Factor analysis Type: general – SubjectFull: Calibration Type: general – SubjectFull: Reliability (Personality trait) Type: general – SubjectFull: Discriminant analysis Type: general – SubjectFull: Adolescence Type: general – SubjectFull: Jordan Type: general Titles: – TitleFull: 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. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ayasrah, Mohammad Nayef – PersonEntity: Name: NameFull: Khasawneh, Mohamad Ahmad Saleem – PersonEntity: Name: NameFull: Almulla, Mazen Omar – PersonEntity: Name: NameFull: Aboutaleb, Amoura Hassan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02664909 Numbering: – Type: volume Value: 41 – Type: issue Value: 5 Titles: – TitleFull: Journal of Computer Assisted Learning Type: main |
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