A multimethod exploration of human–AI-supported shared metacognition for inclusive educational leadership during crisis in Türkiye and Pakistan
Discover Computing, cilt.29, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 29 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s10791-026-10235-5
- Dergi Adı: Discover Computing
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Anahtar Kelimeler: Cross-cultural education, Generative AI, Human–AI collaboration, Inclusive educational leadership, Shared metacognition, Socio-technical innovation
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet
Özet
This study examines how Generative AI–supported self-regulation and social media– supported co-regulation jointly contribute to Shared Metacognition and Inclusive Educational Leadership (IEL) development among pre-service teachers in crisis contexts in Türkiye and Pakistan. Using the Community of Inquiry framework and its shared metacognition extension, informing the design of AI-supported self-regulation and social-media-supported co-regulation. A quasi-experimental design was implemented with unpaired pre- and post-intervention data from 249 participants. In addition to self-reported measures, expert rubric-based evaluations were used to provide an objective assessment of leadership outcomes. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), fuzzy-set Qualitative Comparative Analysis (fsQCA), and Artificial Neural Networks (ANN) to capture linear, configurational, and non-linear associations. Results showed strong associations between social media co-regulation and IEL (β = 0.477, p <.001) and between AI self-regulation and IEL (β = 0.354, p <.001). A mediation effect of AI self-regulation between social media co-regulation and inclusive educational leadership (β = 0.259, p <.001) emerged in the post-intervention phase.The fsQCA indicated that the configuration of high AI self-regulation and high social media co-regulation was sufficient for high IEL. ANN analysis achieved low prediction error (MSE = 0.774; MAE = 0.690). These findings highlight a socio-technical pathway where human–AI collaboration supports inclusive, equitable leadership practices in educational crisis contexts across cultures. Given the use of unpaired cross-sectional data, findings are interpreted as associative rather than causal. This study adopts a human-centered perspective on AI-supported learning by illustrating how inclusive, human-centered leadership can foster sustainable socio-technical innovation in education during crises.