Search

AI-Enabled English for Vocational Undergraduate Education from the Perspective of Multimodal Cognitive Adaptation Exploration of Personalized Learning Path

Author(s)

Jing Lin

Affiliation(s)

Guangdong University of Business and Technology, Institute of Applied Translation, Zhaoqing, Guangdong, China

Abstract

This study employs the framework of multimodal cognitive adaptation theory to investigate personalized learning phenomena in AI-enhanced English classrooms at vocational undergraduate institutions. The theoretical analysis begins with a detailed exploration of the concept's core principles, followed by an examination of the theoretical foundations supporting AI-integrated teaching environments. Current practices reveal both distinctive features and limitations of personalized learning approaches. These findings establish crucial empirical groundwork for future research. Notably, the multimodal cognitive adaptation-oriented learning pathway design principles developed from these insights demonstrate significant theoretical value. Practical implementation strategies for constructing personalized learning pathways have been validated in vocational English education. The study provides dual benefits: it offers theoretical references for integrating AI into classroom teaching reforms, while also contributing to improving foreign language instruction quality and fostering learners' individualized language development in vocational colleges.

Keywords

Multimodal Cognitive Adaptation; AI-Enhanced English Classroom; Personalized Learning; Pathway Construction; Vocational Undergraduate English Teaching

References

[1] Cheng Zhenan. Research on New Pathways of Personalized Services in AI-Enabling Smart Cities. Mobile Information, 2025,47(1):207-209. [2] Liu Yong. Research on Point Cloud Completion Based on Deep Learning: From Single-Modal to Multi-Modal. Guangxi: Guangxi University, 2024. [3] Wu Qian, Liu Li. Optimization Path of Online Deep Learning from the Perspective of Embodied Cognition. Changjiang Series, 2025(3):82-84. [4] Zhang Xianheng. Research on Personalized Federated Learning Algorithms Based on Deep Learning. Qilu University of Technology, 2024. [5] The Connotation and Practice of AI Empowered Personalized Learning. Hubei Education, 2025(2):5. [6] Ma Mingyu. Research and Application of Knowledge Tracking Learning Process Modeling for Online Personalized Learning. Jiangxi: Jiangxi Normal University, 2024. [7] Chen Xianghui. AI-assisted personalized learning in fine arts. Hubei Education, 2025(2):16-17. [8] Liu Tianxiang. Research on Deep Learning Algorithms for Single-Modality and Dual-Modality Mirror Inspection. Anhui: Anhui University, 2024. [9] Li Yuqing. AI Empowered Personalized Learning Process Management. Hubei Education, 2025(2):14-15. [10] Luo Jiahui. Research on the Practical Path of Interdisciplinary Thematic Learning for Primary Schools Oriented towards Deep Learning. Guizhou: Guizhou Normal University, 2024. [11] Jiang Jiajun, Ma Shijia, Liu Haohui, et al. Body, Cognition, and Context: Defining the Logic, Valuing the Implications, and Charting the Course for Interdisciplinary Thematic Learning in Physical Education and Health. Journal of Physical Education, 2024,40(4):1-9. [12] Hu Hang, Yang Lin. AI-enabled personalized collaborative learning model construction and application. Hubei Education, 2025(2):9-11. [13] Liu Jiaqi. Innovative Approaches to Personalized Interpretation Learning: An Action Research on Blended Teaching Models. Journal of Teacher Education, 2024(18):103-105. [14] Xu Shisheng, Luo Caitian. Implementation Path and Case Analysis of Video Teaching in Junior High School English Classrooms. English Learning, 2024(6):37-43. [15] Zhong Shaochun, Yang Lan, Fan Jiarong. Data-driven personalized learning: actual issues, ideal logic, and implementation pathways. Research on Electro-education, 2025,46(1):13-19,33. [16] Zhou Yingchun. AI+Personalized Teaching: Empowerment Pathways, Practical Challenges, and Future Prospects. Innovative Talent Education, 2024(4):62-66.