Leveraging Reinforcement Learning from Human Feedback (RLHF) for Adaptive Learning in STEM Higher Education: Future Directions for Africa

By

*Dr. Clement Orver Igyu, +2348134507985, This email address is being protected from spambots. You need JavaScript enabled to view it., Department of Science and Mathematics Education, Rev. Fr. Moses Orshio Adasu University, Makurdi, Benue State, Nigeria, and

Prof. Nicholas Akise Ada, FSTAN, Department of Science and Mathematics Education, Rev. Fr. Moses Orshio Adasu University, Makurdi, Benue State, Nigeria.

Abstract

Artificial intelligence (AI) systems that incorporate human feedback into reinforcement learning (RL) are transforming adaptive control and personalized learning. Reinforcement learning from human feedback (RLHF) enables Large Language Models and other AI assistants to align with human preferences and educational goals, rather than relying solely on engineered reward functions. In science, technology, engineering, and mathematics (STEM) education, RLHF provides a framework for building adaptive tutoring systems, intelligent laboratories, and robotics platforms that respond to teachers’ and students’ evaluations in real time. Drawing on recent research in adaptive control, human-AI collaboration, and educational technology, this article situates RLHF at the intersection of computational accuracy and human involvement, yet identifies a major gap in the context of education in Africa. It combines evidence from current sources to explore conceptual bases, integration methods, and pedagogical uses, while addressing ethical and operational challenges such as feedback quality, algorithmic bias, data privacy, teacher oversight, explainability, and fair implementation. The discussion shows how RLHF can support adaptive and ethical learning systems that enhance rather than replace human expertise. It is recommended that future research focus on developing robust, transparent, and human-centered RLHF systems through long-term, cross-cultural studies, improved teacher–AI collaboration, and standardized evaluation frameworks to enhance the effectiveness, fairness, and sustainability of adaptive STEM education in Africa.

Keywords: reinforcement learning, human feedback, artificial intelligence, STEM, higher education, Africa. Download PDF