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

IF 4.6 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2025-09-14 DOI:10.1111/jcal.70127

Mohammad Nayef Ayasrah, Mohamad Ahmad Saleem Khasawneh, Mazen Omar Almulla, Amoura Hassan Aboutaleb

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引用次数: 0

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.

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约旦中学生ai整合元认知学习弹性量表（AIIMLR）的设计与验证：来自网络分析视角的见解

被人工智能（AI）彻底改变的一个领域是教育环境。聊天机器人、推荐系统、自适应学习系统和大型语言模型已经成为促进学习的实用工具。然而，适当地使用这些工具是具有挑战性的。在这方面，元认知学习弹性的构建受到越来越多的关注，特别是在面对人工智能支持学习的不确定性和逆境时。本研究旨在开发和评估人工智能综合元认知学习弹性量表（AIIMLR）的心理测量特性。该量表旨在评估学生在人工智能增强学习环境中认知和情感管理学习挑战的能力。方法本研究采用混合方法设计，于2025年在约旦进行。使用了基于理论文献和半结构化访谈的系统综述而开发的项目池。然后，内容验证和试点阶段用于修改项目。采用探索性因子分析（EFA）、验证性因子分析（CFA）、探索性图分析（EGA）和随机森林模型（RFM）对量表的构建效度进行评估。此外，采用Cronbach’s alpha （α）和McDonald’s omega （ω）来评估信度。最后，除了评估重测信度外，还进行了类内相关系数（ICC）的计算。结果与结论EFA结果揭示了六个因素：人工智能介导学习中的自我意识和元认知调节；动态人工智能学习情境中的认知适应性研究人工智能集成学习挑战中的情绪稳定性研究人工智能支持下问题解决的战略毅力人工智能驱动学习困难的动机弹性研究以及通过人工智能反馈对学习进行反思性重新校准。这六个因素共同解释了总方差的66.21%。CFA拟合指数（CFI = 0.917, RMSEA = 0.079）和信度指标Cronbach's alpha（0.897-0.948）、McDonald's omega（0.892-0.950）、复合信度（CR: 0.888-0.954）均在可接受范围内。此外，使用平均方差提取（AVE）验证了收敛效度和判别效度。跨性别测量不变性检验表明，量表对男性和女性都保持稳定的测量性质。研究结果表明，AIIMLR量表是评估人工智能增强教育环境中元认知学习弹性的有效和可靠的工具。

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

期刊介绍： The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope