Design and Validation of the Metacognitive Diagnostic Disruption in AI Decision‐Making Scale (MDD‐AI Scale) Among Educational Administrators in Jordan: Insights From the Network Analysis Perspective.
Background: In recent decades, emerging technologies—particularly Artificial Intelligence (AI)—have had a profound impact on the structure and decision‐making processes within educational systems. AI‐based tools now play a central role in data analysis, performance prediction, and personalised learn...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 2; pp. 1 - 26 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2026
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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=192476922&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192476922 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Apr2026 vid: 42 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 192476922 192476922 192476922 10.1002/jcal.70216 192476922 ppf: 1 ppct: 25 formats: tig: atl: Design and Validation of the Metacognitive Diagnostic Disruption in AI Decision‐Making Scale (MDD‐AI Scale) Among Educational Administrators in Jordan: Insights From the Network Analysis Perspective. aug: au: Khasawneh, Yusra jadallah abed Khasawneh, Najwa Ahmed Saleem Khasawneh, Mohamad Ahmad Saleem affil: Faculty of Educational Sciences, Department of Educational Administration, Ajloun National University, Ajloun, Jordan sug: subj: Instrument Construction Instrument Validation Artificial Intelligence, Generative Decision Making, Computer Assisted School Administrators Psychosocial Factors Scales Evaluation Cognition Diagnosis, Delayed Funding Source Jordan Human Scales Descriptive Statistics Self Regulation Multimethod Studies Exploratory Research Factor Analysis Chi Square Test Coefficient alpha Self-Efficacy Professional Development Diagnosis, Computer Assisted Leadership User-Computer Interface Intraclass Correlation Coefficient Validation Studies ab: Background: In recent decades, emerging technologies—particularly Artificial Intelligence (AI)—have had a profound impact on the structure and decision‐making processes within educational systems. AI‐based tools now play a central role in data analysis, performance prediction, and personalised learning design. Objectives: This study aimed to design and validate the Metacognitive Diagnostic Disruption in AI Decision‐Making Scale (MDD‐AI Scale) for educational administrators, focusing on metacognitive disruptions—disturbances in self‐monitoring and self‐regulation processes caused by AI involvement in decision‐making. Methods: Using an exploratory mixed‐methods design, the study combined a literature review and interviews with 20 Jordanian educational administrators to develop 52 items from an initial 92. Data from 670 administrators were analysed for validity and reliability, with exploratory graph analysis (EGA) providing a network‐based perspective. Results and Conclusions: Exploratory Factor Analysis (EFA) revealed a six‐factor structure—Metacognitive Self‐Monitoring Disruption, Metacognitive Self‐Evaluation Disruption, Cognitive Flexibility Reduction, Cognitive Overreliance on AI, Conflict Detection Shutdown, and Disruption of Metacognitive Decision Planning—explaining 67.6% of the variance. Confirmatory Factor Analysis (CFA) showed good fit (RMSEA = 0.072, CFI = 0.918), with AVE values > 0.50 confirming convergent validity. Reliability was high across all indices (α = 0.904–0.951; ω = 0.897–0.952; CR > 0.89; ICC = 0.752–0.870). Measurement invariance across gender indicated structural equivalence. Exploratory Graph Analysis (EGA) and Random Forest Modeling (RFM) further supported robustness and predictive utility. Overall, the 42‐item MDD‐AI Scale is a valid, reliable, and innovative tool for assessing metacognitive disruptions in algorithmic decision‐making within educational management. Lay Description: what is already known about this topic ○AI is being rapidly integrated into educational decision‐making, helping school leaders with tasks such as analysing student data, evaluating teachers, and planning resources○Many educational administrators struggle to interpret AI‐generated data, especially in complex situations where human judgement and machine analysis may conflict.○Excessive reliance on AI tools can disrupt metacognitive processes, making it harder for administrators to critically evaluate, monitor, and regulate their own thinking during decision‐making.What this paper adds ○This paper introduces the concept of Metacognitive Diagnostic Disruption, highlighting how AI tools can interfere with administrators' ability to think critically and reflectively○It develops and validates a new measurement tool—the MDD‐AI Scale—specifically designed to assess the cognitive and metacognitive challenges educational administrators face when interacting with AI.○The study provides a theoretical and practical foundation for creating training programs and policies that support more thoughtful, ethical, and independent decision‐making in AI‐enhanced educational settingsImplications for practice and/or policy ○Educational institutions should provide targeted professional development to enhance administrators' metacognitive skills and critical thinking when using AI tools○Policymakers need to ensure that AI integration in schools includes ethical guidelines and support systems that promote human oversight and judgement.○The MDD‐AI Scale can be used by education authorities to identify cognitive vulnerabilities in school leaders, informing the design of more effective AI training and capacity‐building programs. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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