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Understanding ChatGPT Adoption for AI Image Generation Among Visual Communication Design Students

2026-08-10 · bit-Tech

One-line summary

An AI research paper on Understanding ChatGPT Adoption for AI Image Generation Among Visual Communication Design Students.

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充:本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

The use of Artificial Intelligence Generated Content (AIGC) for image creation is growing rapidly among Visual Communication Design (VCD) students, yet concerns over copyright, creative originality, and professional relevance persist. This study examines VCD students’ behavioral intention rather than actual usage behavior, frequency, or design performance to use ChatGPT as an AIGC image-generation platform, employing a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model extended with Perceived Risk (PR) and Perceived Anxiety (PA). ChatGPT was operationalized as use of its integrated image generation capability, including GPT-4o’s native visual output, rather than using ChatGPT solely to draft prompts for other image tools. A cross-sectional survey of 386 VCD students from 37 Indonesian universities, selected through purposive sampling, was analyzed using PLS-SEM via SmartPLS 4, testing 14 hypotheses across seven constructs. Of 14 hypotheses, five were supported: Social Influence (SI) and Effort Expectancy (EE) positively influenced Behavioral Intention (BI), while PR negatively influenced BI, and SI significantly reduced PR. PA negatively affected EE and unexpectedly showed a positive direct effect on BI, contrary to the hypothesized direction; this finding should be interpreted cautiously and warrants further investigation. The model explained 30.5% of BI variance. ChatGPT adoption among VCD students is predominantly driven by social dynamics and ease of use rather than functional performance or infrastructure availability, highlighting the need for institutional policies addressing perceived risk and social normalization strategies to support ethical AIGC integration in creative design education.

5.0Engineering value
7.0Research novelty
4.0Business relevance

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