Responsible AI usage for academic integrity in China

Purpose This paper aims to explore how responsible artificial intelligence (AI) technology can enhance various aspects of academic achievement, benefiting institutions, teachers, students and society in creating productive and better educational systems. Design/methodology/approach Data were collected through interviews using convenience sampling involving students, university managers and lecturers. An inductive approach was applied among academic stakeholders in China. Findings The results show that students and lecturers use and emphasize AI technology in academics. Considering the responses, this study found that human-machine integration significantly improves the learning experience, including independent learning, better suggestions, ideas for solutions, module checking, curriculum setup and others. This study identifies the challenges associated with academic integrity and the unethical use of AI to enhance the learning experience and achieve better academic results. Practical implications The findings of this study help better understand the parameters that affect college students’ and educators’ confidence in and acceptance of AI systems. This paper has provided a theoretical foundation for developing AI systems that help students succeed academically and in various other learning contexts. Originality/value This paper has proposed a framework of responsible AI usage for academic integrity that can be used as the basis for understanding best practices. The study has identified the challenges and proposed a solution for the benefit of AI utilization in academia. AI technology is continuously developing, and its utilization will evolve in the future compared to how people are currently using it. In this study, the authors conclude that AI will certainly be widely used and could not be avoided. Educational stakeholders must adhere to clear standard guidelines and fairly assess AI and plagiarism-related work using expert human judgment, free from personal bias. As AI continues to advance, instructors and students will need to develop skills in identifying, evaluating, and investigating various aspects of AI in academic contexts.