Ranking Factors Influencing the Performance of Artificial Intelligence in Improving In-Service Teacher Training Using the Multi-Criteria Decision-Making SECA Method

Background and Objective: This study aimed to rank the factors influencing the performance of artificial intelligence in improving in-service teacher training by utilizing the SECA decision-making method in a qualitative and applied manner. Methodology: The study is considered applied in terms of its objective and descriptive-survey in nature. The statistical population included all faculty members of Nasibeh Campus, Farhangian University in Tehran. The sampling method was purposive and adhered to the principle of theoretical saturation, encompassing 20 experts. Data collection was conducted through interviews with experts. After extracting expert-defined criteria influencing AI performance in in-service teacher training, a decision matrix was developed based on the criteria and alternatives. Finally, the SEC method was employed to determine the ranking of influencing factors, and the LINGO software was used to solve the nonlinear equations. Findings: Educational data analysis, personalized training, and virtual interaction and collaboration were identified as the most significant factors influencing AI performance in improving in-service teacher training. Conclusion: All the criteria identified by the experts were considered positive, indicating that an increase in any criterion would lead to an improvement in the performance of AI in enhancing in-service teacher training.