Exploration of adaptive teaching strategies for university education supported by artificial intelligence

Chengye Jia

doi:10.47297/wspiedWSP2516-250033.20250906

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Exploration of adaptive teaching strategies for university education supported by artificial intelligence

9卷第6期

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Exploration of adaptive teaching strategies for university education supported by artificial intelligence
Exploration of adaptive teaching strategies for university education supported by artificial intelligence
School of Economics, Shandong University of Finance and Economics, Jinan, Shandong, 250002
2025年9卷第6期页码：154-157
纸质出版日期：2025-06-24
DOI：10.47297/wspiedWSP2516-250033.20250906
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(2025). Exploration of adaptive teaching strategies for university education supported by artificial intelligence. 国际教育与发展（英文版）, 9(6), 154-157.

Jia Chengye.Exploration of adaptive teaching strategies for university education supported by artificial intelligence[J].Journal of International Education and Development,2025,09(06):154-157.
(2025). Exploration of adaptive teaching strategies for university education supported by artificial intelligence. 国际教育与发展（英文版）, 9(6), 154-157. DOI： 10.47297/wspiedWSP2516-250033.20250906.

Jia Chengye.Exploration of adaptive teaching strategies for university education supported by artificial intelligence[J].Journal of International Education and Development,2025,09(06):154-157. DOI： 10.47297/wspiedWSP2516-250033.20250906.

摘要

Abstract

The deep integration of artificial intelligence (AI) with higher education offers a new approach to resolving the conflict between large-scale and personalized teaching. This study explores the advantages and implementation strategies of AI-supported adaptive teaching in university education

highlighting how it achieves precise teaching through data-driven learner profiling

dynamic resource matching

and closed-loop feedback mechanisms.The study puts forward three strategies: personalized path recommendation based on learning behavior data

real-time q&A driven by intelligent teaching assistant

and dynamic difficulty adjustment of adaptive test

all of which are highly feasible based on the existing technical framework.

关键词

Keywords

references

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