Proceedings of the 2025 10th International Conference on Modern Management, Education and Social Sciences (MMET 2025)

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Generative AI for Contextualized Vocabulary Learning: The TQI Framework

Authors
Huilin Yang1, *
1Institute of Foreign Language, Liaocheng University, Liaocheng, China
*Corresponding author. Email: 19861903476@163.com
Corresponding Author
Huilin Yang
Available Online 11 November 2025.
DOI
10.2991/978-2-38476-475-4_41How to use a DOI?
Keywords
Generative Artificial Intelligence; Vocabulary Teaching; Contextualized Learning; Multimodal Teaching; TQI Framework; Teacher Empowerment
Abstract

Traditional English vocabulary instruction often struggles with a lack of context and the challenge of creating engaging resources. However, the rise of generative artificial intelligence (AI) offers transformative potential for educators. This paper introduces and validates the Text-Question-Illustration (TQI) framework, a practical and streamlined approach for vocabulary teaching. The TQI framework provides a minimalist, one-click workflow enabling teachers to rapidly generate comprehensive learning packages using free AI platforms. Within minutes, educators can create contextualized stories, supportive questions, and narrative illustrations, overcoming the long-standing resource creation dilemma. Furthermore, the TQI framework is highly adaptable, facilitating differentiated and hierarchical instruction and seamless integration of specific grammar points through simple adjustments to AI prompts. This framework not only offers an efficient and engaging solution for vocabulary acquisition but also empowers teachers to become innovative instructional designers. It heralds a future of AI-assisted education that is more personalized and creative.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 10th International Conference on Modern Management, Education and Social Sciences (MMET 2025)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
11 November 2025
ISBN
978-2-38476-475-4
ISSN
2352-5398
DOI
10.2991/978-2-38476-475-4_41How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Huilin Yang
PY  - 2025
DA  - 2025/11/11
TI  - Generative AI for Contextualized Vocabulary Learning: The TQI Framework
BT  - Proceedings of the 2025 10th International Conference on Modern Management, Education and Social Sciences (MMET 2025)
PB  - Atlantis Press
SP  - 344
EP  - 354
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-475-4_41
DO  - 10.2991/978-2-38476-475-4_41
ID  - Yang2025
ER  -