Learner Support and AI – From Assumptions to Realities: A Systematic Review

Learner support is a cornerstone of open, distance, and digital education (ODDE), yet providing effective, scalable, and equitable support remains a persistent challenge. With the rise of artificial intelligence (AI) and increasing publications on it, it is important to see how higher education institutions are adopting AI-enabled tools to support their learners. This study systematically reviewed and synthesized 61 publications (2010–2025) to examine how AI has been applied to learner support, what practices and constraints are reported, and whether current approaches meet diverse learner needs. Using the Institutional Support for Student Persistence Model (ISSPM) and an AI typology, the review finds that adaptive systems and personalization—particularly chatbots and recommender systems—are the most common applications, followed by profiling and prediction. Reported benefits include timely interventions, personalized guidance, and workload reduction for staff; however, technical limitations, privacy concerns, limited inclusivity, and reduced human interaction pose significant challenges. The review finds critical blind spots, especially in addressing diverse learner needs and in the underexplored transition phase of the student journey. Notably, diversity in population, such as marginalized groups or neurodivergent learners were absent. By offering a structured synthesis from an institutional perspective, this review contributes to the established learner support research about the role of AI in shaping equitable and sustainable learner support in digital higher education.