An intelligent Pythagorean fuzzy MSD–CRITIC–RAWEC framework for assessing psychological states in political education classrooms

Psychological state assessment in political education classrooms involves uncertain, vague, and hesitant assessment information associated with students’ emotional, behavioral, and cognitive conditions. Traditional evaluation approaches are often insufficient for effectively handling such uncertainty in intelligent educational environments. To address this limitation, this study proposes an intelligent Pythagorean fuzzy Mean Square Deviation–Criteria Importance Through Intercriteria Correlation–Ranking of Alternatives by Weighted Evaluation under Criteria (PF-MSD–CRITIC–RAWEC) framework for assessing psychological states in political education classrooms. The proposed framework integrates Pythagorean fuzzy theory with the MSD, CRITIC, and RAWEC methods to manage uncertain psychological assessment information, objectively determine criteria importance, and generate reliable ranking results for classroom psychological monitoring alternatives. A case study involving thirty alternatives and ten psychological assessment criteria was conducted to demonstrate the applicability and computational effectiveness of the proposed framework. Comparative and sensitivity analyses were performed to verify the reliability, robustness, and ranking stability of the proposed approach under uncertain decision-making conditions. The obtained results indicate that the proposed framework provides an effective intelligent decision-support model for psychological assessment and adaptive educational management under uncertain classroom environments.