@inproceedings{sheng-etal-2026-learnercompass,
    title = "{L}earner{C}o{MPASS}: Intelligent Tutoring System with Dynamic Cognitive Diagnosis and Multi-Model Path Planning",
    author = "Sheng, Ziji  and
      Tie, Guiyao  and
      Wang, Weidong  and
      Zhou, Pan  and
      Liu, Daizong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.408/",
    pages = "9016--9042",
    ISBN = "979-8-89176-390-6",
    abstract = "Existing adaptive learning systems struggle to simultaneously achieve deep personalization, dynamic adaptability, and content trustworthiness, particularly in logically rigorous STEM fields where Large Language Models (LLMs) are prone to ``hallucination''. This paper introduces LearnerCoMPASS (Cognitive Multi-model Planning Adaptive System), an integrated, end-to-end framework for adaptive learning. At its core, the framework features a novel multi-model path planning algorithm that orchestrates and fuses the outputs of heterogeneous LLM experts to generate and optimize learning sequences. To enable deep personalization, we design a dynamic cognitive diagnosis module that employs an innovative encoder-decoder architecture to generate precise, multi-dimensional cognitive state vectors for learners. To ensure trustworthiness, the system leverages an adaptively constructed dynamic knowledge graph and a Graph-RAG mechanism to provide factual anchors and logical constraints for LLM reasoning, thereby mitigating hallucinations. Extensive experiments demonstrate that LearnerCoMPASS significantly outperforms state-of-the-art baselines in generating high-quality personalized learning paths. Furthermore, ablation studies validate the critical contributions of our dynamic cognitive diagnosis and multi-model planning components."
}