Design and Evaluation of an AI-Based Intelligent Knowledge Management Model for Enhancing Elementary School Teachers’ Teaching Performance with an Emphasis on Tacit Knowledge Extraction and Sharing
Keywords:
Knowledge management, artificial intelligence, tacit knowledge, teaching performance, elementary schoolsAbstract
Purpose: This study aimed to design and evaluate an AI-based intelligent knowledge management model for enhancing elementary school teachers’ teaching performance, with particular emphasis on tacit knowledge extraction and sharing.
Methodology: This applied-developmental study employed an exploratory sequential mixed-methods design. In the qualitative phase, 18 experts in knowledge management, educational technology, artificial intelligence, educational management, and primary education, together with experienced primary-school teachers and administrators in Tehran, were selected through purposive sampling until conceptual saturation was reached. Data were collected through semi-structured interviews and analyzed using thematic analysis in MAXQDA. In the quantitative phase, 384 public primary-school teachers in Tehran were selected using multistage cluster sampling. A researcher-developed questionnaire derived from the qualitative findings was used. Measurement quality was assessed through factor loadings, Cronbach’s alpha, rho_A, composite reliability, average variance extracted, and HTMT. The structural model was tested using partial least squares structural equation modeling with 5,000 bootstrap resamples in SmartPLS.
Findings: AI infrastructure and readiness positively predicted tacit knowledge extraction (β = 0.418, p < 0.001), while managerial support and knowledge-sharing culture also significantly predicted tacit knowledge extraction (β = 0.291, p < 0.001). Tacit knowledge extraction significantly influenced intelligent knowledge coding and organization (β = 0.604, p < 0.001), which in turn predicted knowledge storage and retrieval (β = 0.537, p < 0.001). Knowledge sharing significantly predicted professional learning and collaboration (β = 0.566, p < 0.001), and professional learning influenced knowledge application for teaching improvement (β = 0.481, p < 0.001). The strongest path was from knowledge application to teaching performance (β = 0.652, p < 0.001). The model explained 68.4% of the variance in teaching performance and demonstrated satisfactory fit (SRMR = 0.056, NFI = 0.918).
Conclusion: The findings indicate that AI can meaningfully improve elementary teachers’ teaching performance when embedded within an integrated knowledge management system that facilitates the extraction, organization, storage, sharing, and practical application of teachers’ tacit and professional knowledge.
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