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<p>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, <i>p</i> &lt; .001) and between AI self-regulation and IEL (β = 0.354, <i>p</i> &lt; .001). A mediation effect of AI self-regulation between social media co-regulation and inclusive educational leadership (β = 0.259, <i>p</i> &lt; .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.</p>

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A multimethod exploration of human–AI-supported shared metacognition for inclusive educational leadership during crisis in Türkiye and Pakistan

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Fatih Mutlu Özbilen,
  • Muhammad Zaheer Asghar,
  • Mustafa Bayrakci

摘要

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.