Institutional Regulation of Using AI-Based Services Through the Kano Model: The Case of Kherson State University

The rapid advancement of artificial intelligence technologies has fundamentally transformed higher education, encouraging universities to develop comprehensive institutional policies for AI implementation across academic and administrative processes. This study presents Kherson State University's institutional experience developing regulatory frameworks for implementing AI-based services. The research employs the Kano model methodology to assess user satisfaction and prioritize AI-based services among university participants. A comprehensive survey involving 502 students, 141 academic staff members, and 61 non-academic staff members was conducted to determine preferences and satisfaction levels with the 13 most demanded worldwide AI-based services, including ChatGPT, Google Gemini, Gamma, and others. The study introduces a conceptual distinction between routine and creative intellectual activities, establishing a framework for appropriate AI utilization. Based on survey results, a four-tier typology of AI-based services usage was developed: mandatory use, recommended use, not recommended use, and prohibited use. This classification system addresses different participant groups and activity types within educational, scientific, administrative, and management domains. Key findings reveal that ChatGPT (35.1%), students consider Gamma (21.1%), and Google Gemini (19.7%) most functional, while academic staff prioritize ChatGPT (55.3%), Google Gemini (46.1%), and Gamma (38.3%). Notable differences emerged between natural science and humanities students regarding specific AI tool preferences. The research resulted in comprehensive institutional regulations that define ethical principles, security requirements, and implementation guidelines for AI-based services. The study provides practical recommendations for enterprise-level AI tools, including security protocols for confidential data processing and integration with university authentication systems. This institutional model offers valuable insights for other universities developing AI governance frameworks, contributing to responsible AI adoption in higher education while maintaining academic integrity and educational quality standards.