Transformer-Empowered AIGC: Enhancing and Reshaping New Media Art Pedagogy

DOI

10.2991/978-94-6463-815-8_29How to use a DOI?

Keywords

Transformer architecture; Artificial Intelligence Generated Content (AIGC); New Media Art; Pedagogical Paradigm Reshaping

Abstract

Addressing the challenge that general-purpose Artificial Intelligence Generated Content (AIGC) tools often fail to deeply align with specific teaching objectives in new media art education (such as conceptual deepening, personalized expression, and critical thinking development), this paper proposes and elaborates on a method for constructing a “Pedagogically Advantaged AIGC Model for New Media Art” (PAAM-NMA) based on the Transformer architecture. This method adapts and integrates advanced AIGC technologies through teaching-objective-driven data strategies, functional module design (e.g., conceptual deepening, style guidance, reflective support), and interactive mechanism innovation. To validate its effectiveness, a quasi-experimental design was developed, comparing PAAM-NMA with general AIGC tools and traditional teaching methods. Experimental results indicate that PAAM-NMA will be significantly superior to general AIGC and traditional teaching methods in enhancing the conceptual depth of student works (expected mean 6.5 vs. 5.0/4.8), personalized expression (6.0 vs. 4.8/4.2), critical AI application ability (6.3 vs. 4.5), as well as intrinsic learning motivation (6.1 vs. 5.7/5.0) and critical thinking (75.5 vs. 68.0/65.0). This suggests that Transformer-based AIGC systems, customized and optimized for specific educational fields, can more effectively promote the achievement of deep learning goals and drive pedagogical paradigm shifts.

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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TY  - CONF
AU  - Yushu Cao
PY  - 2025
DA  - 2025/08/13
TI  - Transformer-Empowered AIGC: Enhancing and Reshaping New Media Art Pedagogy
BT  - Proceedings of the 2025 4th International Conference on Art Design and Digital Technology (ADDT 2025)
PB  - Atlantis Press
SP  - 271
EP  - 280
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-815-8_29
DO  - 10.2991/978-94-6463-815-8_29
ID  - Cao2025
ER  -