Clinical Supervision in Teachers’ Professional Development: An Integrative Framework

Clinical supervision has increasingly been positioned as an approach to support teacher professional development; however, previous studies have examined its stages, implementation mechanisms, contextual conditions, and professional outcomes from relatively fragmented perspectives. This study aims to analyze the role of clinical supervision in teacher professional development and to synthesize the relationships among the supervision cycle, developmental mechanisms, implementation conditions, and professional outcomes. This study employed a Systematic Literature Review (SLR) with thematic-narrative synthesis following the PRISMA 2020 framework. Relevant literature published primarily between 2020 and 2026 was identified through Scopus, ERIC, Google Scholar, Garuda, and SINTA using keywords related to clinical supervision and teacher professional development. Selected studies were assessed using the JBI Critical Appraisal Tools and analyzed through thematic coding and cross-study comparison. The findings indicate four interconnected domains: clinical supervision cycle, developmental mechanisms, enabling and constraining conditions, and professional development outcomes. The supervision cycle consists of planning, observation, evidence analysis, feedback, and follow-up, while reflection, professional dialogue, coaching, and collaborative improvement function as mechanisms linking supervision to professional learning. Trust, supervisor competence, teacher engagement, institutional support, time, and workload influence the quality of implementation. Professional outcomes include competence, instructional improvement, self-efficacy, and professional identity. Based on these findings, the study proposes an Integrative Clinical Supervision-Professional Development Framework as a conceptual synthesis of existing evidence. The framework should be understood as an interpretive-conceptual framework requiring further empirical validation rather than as a validated causal model.