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    <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.</abstract>
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%0 Conference Proceedings
%T LearnerCoMPASS: Intelligent Tutoring System with Dynamic Cognitive Diagnosis and Multi-Model Path Planning
%A Sheng, Ziji
%A Tie, Guiyao
%A Wang, Weidong
%A Zhou, Pan
%A Liu, Daizong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F sheng-etal-2026-learnercompass
%X 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.
%U https://aclanthology.org/2026.acl-long.408/
%P 9016-9042

Markdown (Informal)

[LearnerCoMPASS: Intelligent Tutoring System with Dynamic Cognitive Diagnosis and Multi-Model Path Planning](https://aclanthology.org/2026.acl-long.408/) (Sheng et al., ACL 2026)

• LearnerCoMPASS: Intelligent Tutoring System with Dynamic Cognitive Diagnosis and Multi-Model Path Planning (Sheng et al., ACL 2026)

ACL

• Ziji Sheng, Guiyao Tie, Weidong Wang, Pan Zhou, and Daizong Liu. 2026. LearnerCoMPASS: Intelligent Tutoring System with Dynamic Cognitive Diagnosis and Multi-Model Path Planning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9016–9042, San Diego, California, United States. Association for Computational Linguistics.