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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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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
Language: English
Authors: Mohammad Nayef Ayasrah (ORCID 0000-0001-5247-2526), Mohamad Ahmad Saleem Khasawneh (ORCID 0000-0002-1390-3765), Mazen Omar Almulla (ORCID 0009-0009-1952-3083), Amoura Hassan Aboutaleb (ORCID 0000-0003-3709-4314)
Source: Journal of Computer Assisted Learning. 2025 41(5).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 23
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Secondary Education
Descriptors: Test Construction, Test Validity, Test Reliability, Rating Scales, Test Items, Content Validity, Factor Analysis, Graphs, Measurement, Network Analysis, Metacognition, Resilience (Psychology), Secondary School Students, Foreign Countries, Learning Processes, Problem Solving, Learning Problems, Gender Differences
Geographic Terms: Jordan
DOI: 10.1111/jcal.70127
ISSN: 0266-4909
1365-2729
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 (a) and McDonald's omega (?) 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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1484242
Database: ERIC
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  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
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  Data: <searchLink fieldCode="AR" term="%22Mohammad+Nayef+Ayasrah%22">Mohammad Nayef Ayasrah</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5247-2526">0000-0001-5247-2526</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mohamad+Ahmad+Saleem+Khasawneh%22">Mohamad Ahmad Saleem Khasawneh</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1390-3765">0000-0002-1390-3765</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mazen+Omar+Almulla%22">Mazen Omar Almulla</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0009-1952-3083">0009-0009-1952-3083</externalLink>)<br /><searchLink fieldCode="AR" term="%22Amoura+Hassan+Aboutaleb%22">Amoura Hassan Aboutaleb</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3709-4314">0000-0003-3709-4314</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. 2025 41(5).
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 23
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Test+Construction%22">Test Construction</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Validity%22">Test Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Reliability%22">Test Reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Rating+Scales%22">Rating Scales</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Items%22">Test Items</searchLink><br /><searchLink fieldCode="DE" term="%22Content+Validity%22">Content Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Factor+Analysis%22">Factor Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Graphs%22">Graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement%22">Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Network+Analysis%22">Network Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Metacognition%22">Metacognition</searchLink><br /><searchLink fieldCode="DE" term="%22Resilience+%28Psychology%29%22">Resilience (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Problems%22">Learning Problems</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink>
– Name: Subject
  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Jordan%22">Jordan</searchLink>
– Name: DOI
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  Data: 10.1111/jcal.70127
– Name: ISSN
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  Data: 0266-4909<br />1365-2729
– Name: Abstract
  Label: Abstract
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  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 (a) and McDonald's omega (?) 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.
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        Value: 10.1111/jcal.70127
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      – Text: English
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        PageCount: 23
    Subjects:
      – SubjectFull: Test Construction
        Type: general
      – SubjectFull: Test Validity
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      – SubjectFull: Test Reliability
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