| Issue |
ITM Web Conf.
Volume 77, 2025
2025 International Conference on Education, Management and Information Technology (EMIT 2025)
|
|
|---|---|---|
| Article Number | 01034 | |
| Number of page(s) | 4 | |
| DOI | https://doi.org/10.1051/itmconf/20257701034 | |
| Published online | 02 July 2025 | |
AI-Enabled personalized learning ecology construction and its implications for prevocational education
Shandong Xiehe University, Yaoqiang Town, Licheng District, Jinan City, China
* Corresponding author: 1325745143@qq.com
This paper discusses the construction of AI-enabled personalized learning ecology and its implications for pre-professional prospective education. With the rapid development of AI technology, the field of education is undergoing profound changes. This study analyzes the problems of homogeneous teaching, low learning efficiency and lagging career planning in the current education field, and describes the current status of the application of AI technology in personalized learning. On this basis, the idea of constructing an AI-enabled personalized learning ecosystem is proposed, including the core elements of intelligent diagnosis and assessment, adaptive learning path planning, virtual tutor and intelligent tutoring, and learning data analysis and feedback. The study further explores the impact of this new type of learning ecosystem on pre-vocational education, including aspects such as early identification of career interests and abilities, dynamic career path planning, and practice-oriented learning experience design. Finally, the article proposes implementation strategies and challenges, providing new ideas and directions for future educational development.
Key words: Artificial intelligence / Personalized learning / Educational ecology / Pre-vocational education / Adaptive learning
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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