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<p>Recent disruptions have prompted scholarly inquiry into whether research on knowledge building (KB) represents transient disturbances or durable transformation at the disciplinary level. This study offers a phase-specific synthesis of KB research from 2014 to 2025, divided into three periods: a pre-pandemic baseline (2014–2019), a pandemic-induced adaptation phase (2020–2022), and a post-2023 era marked by integration of generative artificial intelligence (GenAI) and learning analytics. Employing Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)-guided retrieval and explicit KB inclusion criteria, 306&#xa0;KB-core journal articles indexed in the Social Sciences Citation Index (SSCI) were analyzed through science-mapping and systematic coding of research designs, contexts, and platforms. Findings reveal structural shifts in publication volume around 2020 and 2023, followed by sustained post-2023 acceleration. Considering publication lag, this period is interpreted as temporally buffered, with trends treated as early signals rather than definitive outcomes. Core KB constructs remain stable across phases, indicating continuity at the level of epistemic mechanisms. Post-2023 developments show selective integration of learning analytics and GenAI into the KB framework, rather than replacement of established constructs, with explicit AI integration showing a regionally concentrated early adoption pattern in East Asia. Collaboration networks expand with increased connectivity and decreased assortativity, suggesting growth with coherence. Collectively, these findings indicate that recent changes do not constitute a paradigmatic shift but reflect constraint-driven reorganization under enduring epistemic commitments. This reorganization entails selective integration of emerging technologies with knowledge-building principles while raising tensions related to transparency, interpretability, and epistemic agency. This study contributes a field-level framework for distinguishing technological perturbations from enduring epistemic structures, highlighting the need for transparency and governance to support cumulative inquiry in AI-mediated computer-supported collaborative learning (CSCL).</p>

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Adaptive reorganization in knowledge building research (2014–2025): Continuity and change across the pandemic and post-AI era

  • 期刊论文

  • 发表日期：

    2026年6月24日

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  • Li-Jen Wang

摘要

Recent disruptions have prompted scholarly inquiry into whether research on knowledge building (KB) represents transient disturbances or durable transformation at the disciplinary level. This study offers a phase-specific synthesis of KB research from 2014 to 2025, divided into three periods: a pre-pandemic baseline (2014–2019), a pandemic-induced adaptation phase (2020–2022), and a post-2023 era marked by integration of generative artificial intelligence (GenAI) and learning analytics. Employing Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)-guided retrieval and explicit KB inclusion criteria, 306 KB-core journal articles indexed in the Social Sciences Citation Index (SSCI) were analyzed through science-mapping and systematic coding of research designs, contexts, and platforms. Findings reveal structural shifts in publication volume around 2020 and 2023, followed by sustained post-2023 acceleration. Considering publication lag, this period is interpreted as temporally buffered, with trends treated as early signals rather than definitive outcomes. Core KB constructs remain stable across phases, indicating continuity at the level of epistemic mechanisms. Post-2023 developments show selective integration of learning analytics and GenAI into the KB framework, rather than replacement of established constructs, with explicit AI integration showing a regionally concentrated early adoption pattern in East Asia. Collaboration networks expand with increased connectivity and decreased assortativity, suggesting growth with coherence. Collectively, these findings indicate that recent changes do not constitute a paradigmatic shift but reflect constraint-driven reorganization under enduring epistemic commitments. This reorganization entails selective integration of emerging technologies with knowledge-building principles while raising tensions related to transparency, interpretability, and epistemic agency. This study contributes a field-level framework for distinguishing technological perturbations from enduring epistemic structures, highlighting the need for transparency and governance to support cumulative inquiry in AI-mediated computer-supported collaborative learning (CSCL).