发表机构
Beihang University; Beijing Institute of Technology; Harbin Institute of Technology (Shenzhen); Baidu Inc.（北京航空航天大学; 北京理工大学; 哈尔滨工业大学（深圳）; 百度公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对以知识为中心的个性化学习路径规划，提出PersonaPath基准，含2000个学习者画像与层级知识图谱，评估LLM发现适应性是主要瓶颈，最强模型通过率仅29.5%。
AI 中文摘要
自适应学习系统通常将学习路径规划表述为以习题为中心（Exercise-Centric, EC）的推荐，即根据项目级交互日志推断下一步行动。评估目标导向的指导还需要明确的学习者目标和课程规模的先决条件：具有相似习题记录的学习者可能需要对各自目标采取不同的路径。因此，我们研究以知识为中心（Knowledge-Centric, KC）的个性化学习路径规划，其中规划器必须对学习者档案、掌握状态和先决知识结构进行推理，以决定下一步应学习哪本教科书、哪个单元和哪个概念。为支持这一设定，我们引入了PersonaPath，一个将2,000个细粒度学习者画像与跨77个学科的347本教科书、1,751个单元和4,092个概念的层级知识图谱配对的基准。我们在PersonaPath上评估了具有代表性的LLM。结果表明，即使最强的LLM在基础教育中的最终通过率也仅为29.5%，主要瓶颈在于适应性，即没有模型在针对个体学习者定制路径方面超过44.7%。
英文摘要
Adaptive learning systems commonly formulate learning path planning as Exercise-Centric (EC) recommendation, where the next step is inferred from item-level interaction logs. Evaluating goal-oriented guidance additionally requires explicit learner goals and curriculum-scale prerequisites: learners with similar exercise records may need different paths toward their targets. We therefore study Knowledge-Centric (KC) personalized learning path planning, where a planner must reason over learner profiles, mastery states, and prerequisite knowledge structures to decide which textbook, unit, and concept should be studied next. To support this setting, we introduce PersonaPath, a benchmark that pairs 2,000 fine-grained learner personas with a hierarchical knowledge graph of 347 textbooks, 1,751 units, and 4,092 concepts across 77 subjects. We evaluate representative LLMs on PersonaPath. Results show that even the strongest LLM reaches only a 29.5% final pass rate in Basic Education, and that the main bottleneck lies in adaptivity, where no model exceeds 44.7% in tailoring paths to individual learners.
CommentsAccepted to AACL-IJCNLP 2026 Main Conference
