Governance-Oriented Decision Support for Internship Scheduling Using a GRASP Heuristic in University Psychology Clinics

University psychology clinics represent complex coordination environments in which pedagogical requirements, supervision constraints, and institutional governance mechanisms intersect. Traditional coordination practices based on spreadsheets often fail to ensure transparency, consistency, and traceability in internship scheduling. This study addresses this challenge by adopting a Design Science Research (DSR) approach to design and evaluate a governance-oriented decision-support system for internship coordination. The proposed artifact models the scheduling problem as a rule-constrained allocation process and implements a modular architecture integrating a GRASP heuristic with randomized greedy construction, single-pass triadic repair, a post-generation validation layer, role-based access control, and audit logging mechanisms. The system generates feasible schedules while enforcing operational and pedagogical constraints and supporting peer-observation structures, including binary and triadic supervision arrangements. A real-world pilot deployment provided case-based evidence of scheduling feasibility and operational utility, while the controlled computational evaluation demonstrated complete coverage of the institutional instance and no detected structural violations in the evaluated schedules. Role-based access control, persistent audit logging, and the controlled approval/publication workflow were completed and technically verified subsequent to that pilot, extending rather than altering the scheduling and validation components exercised during it. The results provide case-based evidence of the artifact’s utility and illustrate how a governance-oriented decision-support architecture can make rule validation, authorization, and scheduling provenance explicit within an institutional workflow. In the institutional instance evaluated with 30 randomized seeds, the single-pass triadic repair phase reduced the mean number of unassigned requirements from 3.60 to 0 while preserving feasibility across all runs.