AI-Driven Personalization in Educational Marketing: A Framework for Enhancing Student Recruitment and Retention
Higher education universities require marketing knowledge to overcome growing student enrollment and retention issues in universities. This research examines how educational institutions perform in commercialization alongside studying AI customization effects on that area. In the study narrative literature review combined with secondary data analysis enabled researchers to create a conceptual framework which demonstrates how artificial intelligence tools should be applied for awareness, engagement, conversion, retention, and Advocacy phases. The study investigates three technological solutions consisting of predictive analytics, intelligent chatbots, and customer relationship management (CRM) systems which apply both Technology Acceptance Model and Customer Relationship Management theory. The study recommend educational marketers should direct efforts toward improving targeting accuracy while engaging students through personal content delivery systems since these represent the main outcomes from AI integration which encompasses predictive analytics and personalized content delivery. This framework offers the perfect set of solutions for scaling recruitment and retention planning for any institution. AI can definitely allow universities to provide more personalized experiences, better communication, and a happier student base, thereby increasing student enrollment and persistence. This then puts the institution on the road to a strategized and forward-looking competitive educational landscape.
Authors
- Naeem Akhtar (ORCID: https://orcid.org/0000-0001-9382-5869)
- Muhammad Kashif Imran (ORCID: https://orcid.org/0000-0002-2030-922X)
- Fajer Danish (ORCID: https://orcid.org/0009-0000-7422-1779)
Institutions
- Mohammad Ali Jinnah University (PK)
- Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (PK)
- Royal University for Women (BH)
Publication Details
- Journal
- Journal of Political Stability Archive
- Published
- 2025-05-30
- DOI
- https://doi.org/10.63468/jpsa.3.2.33
- Citations
- 7
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 15.99