AlphaLearn: A Multi-Objective Evolutionary Framework for Fair and Adaptive Optimization of E-Learning Pathways

Abstract

Personalized e-learning seeks to adapt sequences of learning activities to individual learners, yet most existing adaptive platforms continue to rely on heuristic rules or single-objective optimization strategies. This paper introduces AlphaLearn, a conceptual evolutionary agent that frames learning pathway design as a constrained multi-objective optimization problem. The framework integrates knowledge graphs, learner modelling, and evolutionary algorithms to generate, evaluate, and iteratively refine candidate learning pathways under multiple pedagogical criteria. The contribution of this work is threefold. First, it presents a structured architectural framework for evolutionary learning pathway optimization, including a formal description of the optimization cycle and pathway representation. Second, it provides a descriptive analysis of large-scale learning analytics data from the Open University Learning Analytics Dataset (OULAD), illustrating substantial variability in learner outcomes, failure rates, and dropout across modules. Third, it offers an explicit discussion of fairness and bias mitigation, positioning equity as an integral dimension of adaptive pathway optimization rather than a post-hoc concern. The descriptive findings highlight pronounced heterogeneity in learner performance and engagement, motivating the need for adaptive systems capable of balancing learning effectiveness, efficiency, engagement, and fairness. While AlphaLearn is presented as a conceptual and methodological framework rather than a validated system, it establishes a foundation for future empirical evaluation and the development of fairness-aware evolutionary approaches to personalized e-learning.