Research of Ethical Adoption of College Students' Learning Applications of Generative Artificial Intelligence

ABSTRACT Background The application of generative artificial intelligence (GenAI) in education has been deepening. However, at the same time, behaviours that jeopardise academic health, such as learners' over‐reliance on generative AI and massive plagiarism of generated content of generative AI in essay writing, have begun to emerge, and the issue of generative AI ethics should not be underestimated. It is necessary to develop an in‐depth understanding of the issue of ethical adoption of generative AI for learners. Objectives This article examines the determinants of ethical adoption of generative artificial intelligence (GenAI) learning applications among college students. It explores the mechanisms through which these factors operate and investigates the moderating effects of key variables. Based on these findings, the study proposes targeted recommendations to foster responsible GenAI integration in education, offering valuable insights for the wider adoption of GenAI technologies in educational contexts. Methods This study constructs an ethical adoption model for college students' use of GenAI learning applications, integrating the technology acceptance model and the unified theory of acceptance and use of technology. Following the theoretical model development, empirical research was conducted—encompassing questionnaire surveys, quantitative data analysis and results interpretation—to validate the proposed framework. Results The results demonstrate that college students' intention to adopt ethical practices regarding generative AI, facilitating conditions and the management system exhibit a positive correlation with actual compliance with ethical norms. Among these factors, ethical intention exerts the strongest effect. Furthermore, students' performance expectation concerning the ethical adoption of generative AI is positively correlated with their ethical adoption intention. Gender, grade level, voluntariness of use and prior experience significantly moderate these influence pathways. Conclusions This study identifies key factors influencing college students' adoption of generative artificial intelligence (GenAI) in learning applications. The findings offer theoretical and practical insights to inform the responsible integration of GenAI technologies in educational settings.