Mixed-methods framework for teaching quality management in art and design education: Integrating Delphi, QCA, and SEM in Liaoning province

Art and design education requires robust teaching quality management due to its inherently creative, practice-oriented, and professional nature. However, previous research has largely relied on isolated methodological approaches, limiting comprehensive evaluation and empirical validation. This study aims to develop and validate an integrated mixed-methods framework for teaching quality management in art and design education by combining the Delphi technique, Qualitative Comparative Analysis (QCA), and Structural Equation Modeling (SEM) within colleges and universities in Liaoning Province, China.Data were collected from multiple sources, including expert panels, institutional documents, and questionnaire responses from art and design faculty members. The analytical framework was implemented in three sequential phases. First, the Delphi method was employed to refine and confirm key teaching quality dimensions through iterative expert consensus. Second, QCA was applied to identify multiple causal configurations of conditions influencing teaching quality management outcomes. Finally, SEM was used to examine the strength and direction of relationships among the validated constructs using covariance-based modeling. Quantitative analyses were conducted using SPSS to assess reliability, construct validity, and overall model fit. Model fit indices—including the Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA)—met accepted threshold values. The results demonstrate strong construct reliability, significant structural relationships, and robust model fit, with factor loadings ranging from 0.77 to 0.88, explained variance between 10.4% and 17.2%, and statistically significant path coefficients ranging from 0.12 to 0.32 (p < 0.05). Overall, the proposed framework highlights that teaching quality management in art and design education is shaped by the interaction of multiple conditions, offering empirical support for more effective quality governance in creative higher education.