Artificial intelligence, social influence, and AI anxiety: analyzing the intentions of science doctoral students to use ChatGPT with PLS-SEM

Abstract

The widespread use of AI-based applications, such as ChatGPT, in higher education has raised questions about how doctoral students adopt these technologies, particularly in science departments. However, traditional models, such as the Technology Acceptance Model (TAM), may fall short in fully capturing users’ behavioral intentions toward emerging technologies. This study aims to investigate PhD students’ behavioral intention to use ChatGPT for learning purposes by proposing an extended version of the TAM. The proposed model incorporates five additional factors: Social Influence (SI), Perceived Enjoyment (PEN), AI Self-Efficacy (AI-SE), AI’s Sociotechnical Blindness (AI-STB), and Perceived Ethics (AI-PET). Data were collected from 361 PhD students across 35 universities and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that all proposed factors significantly influence students’ behavioral intention to use ChatGPT. This study contributes to the literature by introducing new external variables into the TAM framework and providing empirical insights into doctoral students’ acceptance of AI tools in science education, thereby offering a novel perspective for future research and educational practice.

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