International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/3968 2020 2020 College of Administrative & Financial Sciences, Gulf University, Bahrain Sonia Sonia Manav Rachna International Institute of Research and Studies, India Priyanka Sharma Manav Rachna International Institute of Research and Studies, India Aastha Budhiraja Manav Rachna International Institute of Research and Studies, India Anshu Sharma Tashkent State University of Economics, Uzbekistan Tojiyev Rakhmatilla School of Computer Science Engineering, Galgotias University, Greater Noida, India Sonia Setia Personalized learning pathways in digital education platforms have become essential for addressing the unique needs and behaviors of individual learners. However, traditional adaptive systems often fail to account for the uncertainty, ambiguity, and inconsistency inherent in educational data. This paper proposes a novel neutrosophic decision-support framework that models learner profiles using truth (T), indeterminacy (I), and falsity (F) scores derived from student interaction and performance data. Utilizing the Open University Learning Analytics Dataset (OULAD), we compute neutrosophic learner vectors based on assessment outcomes, engagement patterns, and virtual learning environment (VLE) activity. A rule-based decision engine then recommends adaptive learning pathways—ranging from remedial to advanced—by interpreting the TIF distributions through a neutrosophic logic framework. Experimental results demonstrate that the proposed model enhances pathway assignment accuracy and provides better support for learners with incomplete or uncertain data compared to traditional fuzzy and crisp models. The neutrosophic approach also ensures interpretability and flexibility, making it well-suited for real-world educational platforms aiming to achieve adaptive learning at scale. 2026 2026 147 165 10.54216/IJNS.270114 https://www.americaspg.com/articleinfo/21/show/3968