Teacher-AI-Student Relationships in Student-Centered Learning: A Systematic Review
Teacher-AI-Student Relationships in Student-Centered Learning: A Systematic Review

Summary/Abstract: Background/purpose. Artificial intelligence (AI) offers transformative potential for advancing student-centred learning (SCL) through the Teacher–AI–Student relationship, where AI acts as a mediator to personalise, adapt, and enhance learning experiences. However, AI bias, limited teacher preparedness, ethical concerns, and infrastructure gaps hinder equitable adoption, particularly in resource-constrained contexts such as Africa. This study systematically reviews global and local literature to explore strategies for leveraging AI within this triadic model to strengthen SCL while ensuring cultural and linguistic inclusivity. Materials/methods. A systematic literature review was conducted, examining studies published between 2000 and 2025 from academic databases, policy reports, and case studies. Inclusion criteria focused on AI-enabled SCL models, teacher–AI collaboration, and contextsensitive implementation. Thematic synthesis identified recurring challenges, success factors, and best practices, with an emphasis on codesign approaches, ethical use of AI, and teacher professional development. Results. Effective AI integration in SCL requires a phased approach that incorporates diverse, locally relevant datasets, culturally responsive AI tools, sustained educator capacity-building, and collaborative learning communities. The Teacher–AI–Student relationship, when optimised, enhances learner autonomy, facilitates real-time feedback, and supports differentiated instruction aligned with individual learner needs. Conclusion. Context-sensitive, ethically grounded, and culturally aligned AI adoption is essential to maximise SCL benefits. By embedding AI into the Teacher–AI–Student relationship through co-design, inclusive datasets, and targeted professional development, education systems can address systemic inequities and create personalised, meaningful learning experiences. This study contributes a practical roadmap for policymakers, educators, and technology developers.

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