Leveraging artificial intelligence for intelligent student support: An AI-enabled SRM framework for higher education

The increasing demand for responsive, personalized, and scalable student services has positioned Student Relationship Management (SRM) systems as critical tools in higher education. However, traditional SRM systems are often administrative, static, and reactive—failing to meet the real-time and diverse support needs of today’s students. This study examines how Artificial Intelligence (AI) can be systematically integrated into Student Relationship Management (SRM) systems to improve student engagement, academic advising, and institutional efficiency. The study employed a qualitative descriptive design, utilizing semi-structured interviews, focus group discussions, and document analysis across three public universities. Thematic analysis, facilitated through NVivo 12 software, revealed four key themes: Institutional AI Readiness, Gaps in existing SRM Practices, Perceived Benefits of AI Integration, and Ethical and Governance Concerns. These themes informed the development of a conceptual AI-enabled SRM framework comprising four core layers: AI Services, Student Interaction, Data Infrastructure, and Governance and Ethics. The framework was validated through an expert review, which affirmed its feasibility, ethical grounding, and adaptability across various institutional contexts. Document analysis also highlighted a strategic gap between digital transformation aspirations and the absence of concrete AI implementation policies. The study concludes that integrating AI into SRM can lead to more intelligent, proactive, and student-centered support systems, provided that institutions address infrastructural readiness and adopt robust governance protocols. The findings contribute both theoretically and practically to the field of educational technology by offering a flexible, stakeholder-informed framework that institutions can customize to align with their digital maturity and strategic goals. Recommendations for future research include pilot implementations and comparative evaluations to assess the framework’s impact on student success and institutional performance.