Swarm-Guided Personalized Learning: Integrating the Salp Swarm Algorithm with Large Language Models for Adaptive Educational Pathways

抄録

Large language models (LLMs) can generate personalized educational content on demand but offer no principled way to optimize the sequence, difficulty, and pacing of that content against the competing objectives of effective learning—mastery, engagement, cognitive load, and prerequisite satisfaction. Bio-inspired metaheuristics such as the Salp Swarm Algorithm (SSA) are well suited to this constrained, multi-objective scheduling problem but have not been coupled to a generative content engine for learning-pathway design. We propose SSA-LLM, a human-in-the-loop framework in which SSA searches the space of pathway configurations, a generative engine realizes each configuration as content and assessment, and learner signals return to the optimizer as its fitness. We prove that the formulation strictly generalizes prior single-objective optimization, returns prerequisite-feasible pathways, is "no-harm" relative to its feasibility-repaired seed, and has inference cost bounded by content diversity. In a controlled simulation and on a curriculum whose content is actually authored by a frontier language model, the optimizer's fitness responds significantly to objectively measured content quality (p < 10^-21). Under a leveled comparison that gives every baseline the same feasibility decoder, SSA-LLM achieves the best fitness (Cohen's d = 0.33–2.1); a hardness analysis candidly locates its advantage in small-to-moderate selection problems, while a strong heuristic is preferable for very large pools. Running the generator in a live classroom loop remains future work.