Background <p>University Course Timetabling Problems (UCTTP) in medical schools have become more complex with the integration of metaverse technologies such as virtual and augmented reality. These immersive environments enhance medical education but also introduce new scheduling constraints involving specialized hardware, virtual classrooms, and simulation sessions. Addressing this need, the present study develops a human-centered (faculty-focused) optimization framework that aligns scheduling decisions with instructors’ behavioral intentions to adopt metaverse-based teaching methods.</p> Methods <p>A binary integer linear programming model was formulated to jointly assign regular and metaverse courses while satisfying institutional constraints. Professor-specific behavioral intention weights were derived using the Analytic Hierarchy Process (AHP) informed by constructs identified through Structural Equation Modeling (SEM). Two heuristic approaches—the Greedy Reassignment and Assignment for Professor Equity (GRAPE) and Simulated Annealing (SA)—were developed to solve the model. The parameters of SA were optimized using the Taguchi Design of Experiments method. Computational experiments were conducted on 45 synthetically generated instances of varying sizes.</p> Results <p>The results show that the GRAPE algorithm provides rapid feasible solutions, whereas the SA algorithm yields higher solution quality, particularly for large and complex problem instances. The Taguchi analysis indicated that the cooling rate and number of iterations significantly influence performance, achieving an excellent model fit (R² = 0.998). Overall, the SA method consistently produced near-optimal schedules with low relative error values while maintaining reasonable computation times.</p> Conclusions <p>This study introduces the first optimization framework that integrates behavioral intention modeling into course timetabling for metaverse-based education. The proposed approach enables institutions to allocate metaverse courses to instructors who demonstrate higher readiness to adopt immersive teaching technologies. These findings support the design of adaptive, human-centered scheduling systems aligned with faculty readiness. The current framework focuses on faculty-level behavioral factors; student-related dimensions, such as technological access and cohort preparedness, are acknowledged as important areas for future research.</p>