Mohammad Nayef Ayasrah

Background: During adolescence, peer relationships play a central role in identity development and emotional adjustment. Excessive worry and distress about the potential or actual loss of peer support referred to as peer dependency anxiety may increase vulnerability to anxiety and depression. Despite its significance, no validated measure exists for Arabic‐speaking adolescents. This study aimed to develop and evaluate the Peer Dependency Anxiety Scale (PDAS) among Jordanian adolescents. Methods: A mixed‐methods design was employed in Jordan in 2025. In the qualitative phase, items were generated through a systematic literature review and semi‐structured interviews with 20 adolescents experiencing symptoms of anxiety and depression. In the quantitative phase, the finalized scale was administered to 352 adolescents with elevated anxiety and/or depressive symptoms identified through RCADS‐25 symptom‐based screening. The total sample was randomly split into two independent subsamples of 176 participants for exploratory factor analysis (EFA) and 176 participants for confirmatory factor analysis (CFA), respectively. Psychometric evaluation included exploratory factor analysis (EFA), confirmatory factor analysis (CFA), exploratory graph analysis (EGA), convergent validity, and reliability (Cronbach's α, McDonald's ω, intraclass correlation coefficient [ICC]). Results: EFA indicated a four‐factor structure consisting of Separation Anxiety from Peers, Peer Rejection Sensitivity, Emotional Dependence on Peer Support, and Excessive Concern about Peer Approval. CFA supported both the first‐ and second‐order four‐factor models with adequate fit. Reliability indices were good to excellent. Convergent validity was supported, and EGA with bootstrapping supported the stability of the four‐factor structure. Conclusions: The PDAS showed promising initial psychometric properties for research and screening‐oriented assessment among adolescents who screened positive for elevated internalizing symptoms. Its factor structure, reliability, and score interpretation should be re‐evaluated in representative community samples before the scale is used for broader adolescent populations or for normative interpretation, consistent with validity standards that link score interpretation to intended population and use.

1. Over the past decade, rapid advancements in Artificial Intelligence (AI) have profoundly influenced educational systems worldwide. The integration of AI-based technologies into education has cre...

With the rapid expansion of artificial intelligence (AI) in education, valid tools are needed to assess teachers' readiness for anticipatory, adaptive, and reflective engagement with AI-supported instruction. This study developed and evaluated the Forward-Looking Metacognitive-AI Readiness Scale (FLMAIRS) among secondary school teachers in Jordan. An exploratory mixed-methods design was used. Items were generated through a literature review and semi-structured interviews with 22 teachers, followed by expert review, content and face validity assessment, and cognitive interviewing. The field-test version was administered online to 640 teachers and randomly split for exploratory factor analysis (EFA; n = 320) and confirmatory factor analysis (CFA; n = 320). EFA supported a five-factor, 45-item structure: AI Foresight Planning, Adaptive AI-Oriented Instructional Regulation, Algorithmic Anticipatory Reasoning, Human-AI Instructional Synergy, and Long-Term AI-Reflective Practice, explaining 58.29% of the variance. CFA supported acceptable first- and second-order model fit. Reliability was high across dimensions (Cronbach's α = 0.897-0.931; McDonald's ω = 0.900-0.932), and test-retest stability was good (ICC = 0.752-0.851). Convergent and discriminant validity were supported. FLMAIRS provides a context-sensitive instrument for assessing teachers' forward-looking metacognitive readiness for AI-integrated education.

Adaptive learning in digital environments involves learners’ growth-oriented beliefs and their capacity to reflect on and adjust learning strategies

This study developed and psychometrically validated the Cognitive–Spiritual Algorithmic Responsiveness in Teaching Scale (C-SARTS) for AI-supported secondary education in Jordan. Using a sequential mixed-methods design, we generated and refined items through qualitative interviews and literature review, then administered the scale to 552 teachers. Exploratory factor analysis suggested a coherent four-factor solution—integrative multidimensional responsiveness, cognitive responsiveness in live pedagogy, spiritual responsiveness in teaching encounters, and algorithmic responsiveness in instructional design—which was subsequently confirmed by confirmatory factor analysis with acceptable fit indices. Reliability was strong across dimensions (α/ω/CR at or above conventional thresholds). Convergent and discriminant validity were largely satisfactory; one construct showed AVE slightly below 50 but met composite reliability criteria, indicating conservative yet acceptable convergence. Measurement invariance held across gender. Network-based exploratory graph analysis with bootstrap replications supported a stable four-dimensional structure, and a random-forest check highlighted the integrative dimension and items that combine AI-readable design with attention to students’ psychosocial context as most influential. C-SARTS offers a concise, multidimensional measure of how teachers align cognitive, ethical–spiritual, and algorithmic considerations in AI-rich classrooms, supporting applications in educational research, teacher professional development, and ethically grounded technology integration in Jordanian secondary schools.