When University Policies Backfire: AI-Simulated Stakeholder Perspectives and Thematic Analysis in a Performance Technology

This study examines how AI-simulated stakeholder perspectives can be thematically analysed through a performance technology lens to evaluate the perceived consequences of a university policy. Using four large-language models, the research generated administrative, faculty, and performance technologist interpretations of a policy requiring deduction of owed teaching hours from summer compensation contracts. The analysis revealed that while the policy aims for fair compensation, it unintentionally creates motivational, cultural, and operational misalignments. These findings suggest risks to incentives, morale, and institutional culture that were not evident from the policy’s stated intent. By using AI and a performance technology frame to pre-test these perspectives, the study offers a proactive way to identify systemic issues before they happen. This research demonstrates that AI-based stakeholder simulation offers a practical and reflective tool for analyzing policy effectiveness and strengthening decision-making in higher education.