LearnMate^2: Design and Evaluation of an LLM-powered Personalized and Adaptive Support System for Online Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917468363030528 |
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| author | Wang, Xinyu Jessica Lee, Christine P. Mutlu, Bilge |
| author_facet | Wang, Xinyu Jessica Lee, Christine P. Mutlu, Bilge |
| contents | Personalization is crucial for effective learning, yet online learning, designed for widespread availability and open access, lacks personalized guidance. Recent advancements in large language models (LLMs) offer opportunities to bridge this gap. We explore how LLM-driven tools may be designed to support personalized and adaptive learning and examine how they shape user experience and learning outcomes. We iteratively designed \tool{} to support online learning by providing personalized study plans, real-time contextual assistance, and adaptive learning activities. A preliminary study ($n=24$) assessed the effectiveness and usability of \tool{} and informed refinements in our system, which we then evaluated ($n = 16$) against a combination of a state-of-the-art online learning platform and an LLM for learning support. Results indicate that \tool{} advances AI pedagogy by improving both learning outcomes and user experience compared to existing online learning and support tools. This work advances our understanding of the design space of personalized, AI-driven educational tools and their potential impact on user experience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06257 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LearnMate^2: Design and Evaluation of an LLM-powered Personalized and Adaptive Support System for Online Learning Wang, Xinyu Jessica Lee, Christine P. Mutlu, Bilge Human-Computer Interaction Personalization is crucial for effective learning, yet online learning, designed for widespread availability and open access, lacks personalized guidance. Recent advancements in large language models (LLMs) offer opportunities to bridge this gap. We explore how LLM-driven tools may be designed to support personalized and adaptive learning and examine how they shape user experience and learning outcomes. We iteratively designed \tool{} to support online learning by providing personalized study plans, real-time contextual assistance, and adaptive learning activities. A preliminary study ($n=24$) assessed the effectiveness and usability of \tool{} and informed refinements in our system, which we then evaluated ($n = 16$) against a combination of a state-of-the-art online learning platform and an LLM for learning support. Results indicate that \tool{} advances AI pedagogy by improving both learning outcomes and user experience compared to existing online learning and support tools. This work advances our understanding of the design space of personalized, AI-driven educational tools and their potential impact on user experience. |
| title | LearnMate^2: Design and Evaluation of an LLM-powered Personalized and Adaptive Support System for Online Learning |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2605.06257 |