Conceptual Model for AI-Based Student Support Services to Enhance Undergraduate Retention Rates
Keywords:
Artificial Intelligence (AI), Student Support Services, Retention, Higher EducationAbstract
This study investigates the application of Artificial Intelligence (AI) in enhancing student support services to promote undergraduate student retention in higher education. The research was conducted through a three-phase mixed-method approach: documentary analysis, model development, and expert evaluation. Phase 1 examined student service models from 20 universities—10 in Thailand and 10 internationally—focusing on six core domains: mental and physical health, financial aid, career guidance, student welfare, co-curricular engagement, and behavioral support. The analysis revealed a clear gap in AI integration, with international institutions demonstrating more advanced and systematic implementation than their Thai counterparts. In Phase 2, a conceptual model was developed, structured into three layers: (1) AI-based service systems, (2) short-term outcomes such as increased access and satisfaction, and (3) long-term outcomes including student retention and holistic development. The model integrates four key AI applications—chatbots, predictive analytics, recommendation systems, and sentiment analysis—across all six domains. These technologies enable real-time support, personalized guidance, and early intervention strategies tailored to student needs. Phase 3 involved expert evaluation through a focus group of administrators, faculty, and student representatives.
Findings emphasized the need for ethical implementation, institutional readiness, and student-centered design. Recommendations include developing strategic frameworks for AI adoption, ensuring data ethics, piloting targeted applications, and fostering cross-functional collaboration. This research contributes a validated framework and practical guidance for leveraging AI in higher education. The findings underscore AI’s potential to transform student services into inclusive, data-driven, and retention-focused systems that support student success throughout the academic lifecycle.
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