Ethical Considerations in the Application of Artificial Intelligence in Education: A Bibliometric and Thematic Inquiry
TL;DR
Thematic analysis of teacher candidates' responses indicated that ethical AI is both a technical and a human problem: teacher education must combine technical literacy, ethical reflection, ethical reflection, and practical pedagogy (classroom policies, assessment design, and student support).
Abstract
The integration of artificial intelligence (AI) in education and training presents not only significant opportunities but also critical ethical challenges that warrant careful consideration. This study investigates the current state of knowledge on AI ethics in education by analysing annual publication trends, key scholarly sources, prominent conceptual themes (keywords), and the interrelationships among these concepts. Additionally, it explores prospective teachers’ perspectives on ethical issues associated with AI in educational settings and their proposed solutions. The research is structured in two parts. Part A employs a bibliometric analysis of publications indexed in the Web of Science and Scopus databases to examine trends in scholarly output, identify influential sources, and map the conceptual structure of the field. Part B utilises thematic content analysis to interpret teacher candidates’ views on ethical challenges in the educational application of AI and their suggestions for addressing these concerns. Findings reveal that the annual distribution of publications on AI ethics in education has followed a gradual growth pattern, with minimal output from 1985 until a notable increase post-2021, reflecting the emerging nature of this field. Thematic analysis of teacher candidates' responses indicated that ethical AI is both a technical and a human problem: teacher education must combine technical literacy (what models do and can do), ethical reflection (values, privacy, fairness), and practical pedagogy (classroom policies, assessment design, and student support). The predominant themes in teacher candidates’ likely solutions to the ethical problems in the application of AI in education underscore the importance of policy fluency, participatory design, ethical pedagogy, and continuous professional development ecosystems. The study concludes by proposing a future research agenda to advance the field and discusses implications for policy and educational practice.
