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Bringing Generative AI to Adaptive Learning in Education

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arxiv 2402.14601 v3 pith:XSZTR3ES submitted 2024-02-02 cs.CY cs.AIcs.HCcs.LG

classification cs.CYcs.AIcs.HCcs.LG
keywords learningadaptiveeducationgenerativedevelopmentmodelsapplicationsargue
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The recent surge in generative AI technologies, such as large language models and diffusion models, has boosted the development of AI applications in various domains, including science, finance, and education. Concurrently, adaptive learning, a concept that has gained substantial interest in the educational sphere, has proven its efficacy in enhancing students' learning efficiency. In this position paper, we aim to shed light on the intersectional studies of these two methods, which combine generative AI with adaptive learning concepts. By presenting discussions about the benefits, challenges, and potentials in this field, we argue that this union will contribute significantly to the development of the next-stage learning format in education.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CAFES: A Collaborative Multi-Agent Framework for Multi-Granular Multimodal Essay Scoring

    cs.CL 2025-05 conditional novelty 5.0 of 10

    A student-teacher multi-agent pipeline with positive-only feedback improves QWK agreement with human essay scores by 21% on a multimodal benchmark, with gains concentrated in traits where baselines were weakest.

  2. From Generation to Adaptation: Comparing AI-Assisted Strategies in High School Programming Education

    cs.CY 2025-06 conditional novelty 4.0 of 10

    Adapting small working code examples with AI coding assistants helped five novice students build functional mini-programs, while generating code from scratch mostly failed.

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