Title: KNOWPLAN: Knowledge-Driven AI Agents for Smart Degree Pathway Planning
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Large Language Model, Artificial Intelligence, Data Mining, Recommender Systems, Course Planning

Abstract: Recent advances in large language models (LLMs) provide powerful capabilities for knowledge-driven course planning. However, building reliable, constraint- aware study planners from publicly available course webpages remains challenging due to heterogeneous data sources, complex multi-constraint requirements, and dynamic course catalogs. To address these challenges, this paper proposes KNOWPLAN, a proactive, self-evolving multi-agent AI platform that integrates LLM-based extraction, knowledge-graph retrieval, and constraint-aware reasoning to generate adaptive, personalized study plans. The platform addresses four key challenges in degree planning. First, an Agent Forest leverages LLMs to transform heterogeneous, unstructured course catalogs into unified representations of courses and prerequisite dependencies. Second, a graph-construction agent assembles these representations into a directed knowledge graph, forming the platform’s core infrastructure that faithfully preserves complex multi-prerequisite structures and multiple dependencies. Third, a course-to-fine recommender pro- duces highly personalized course plans: the Course Planning Agent generates coarse, multi-constraint learning trajectories, while the Curriculum Alignment Agent refines these trajectories into term-level schedules, systematically resolving temporal conflicts and optimizing plans according to individual preferences and backgrounds to enable timely degree completion. Finally, a unified Graph- RAG backbone ensures cross-agent consistency and adapts to catalog drift, enabling dynamic refinement with evolving curricula. Across multiple universities, KNOWPLAN achieves 99.5% accuracy in major requirements and 98.7% ac- curacy in prerequisites. By integrating graph-based reasoning with a term-level scheduler, it delivers feasible, compliant, and personalized study plans that adapt to preferences, workload limits, and exceptions, demonstrating superior performance compared to state-of-the-art methods.