AI and higher education: teachers' perceptions of students' ethical tensions in online pedagogical methods

The emergence and development of generative AI tools have benefited higher education but have also created several ethical tensions in the educational system. This tension is higher in online teaching environments, where students often rely on AI tools. Previous studies have addressed ethical issues, yet how these issues have emerged across various teaching methods in online AI-driven classes has been overlooked. To address this gap, this qualitative study investigates teachers' perceptions about their students' ethical issues across five pedagogical methodologies: direct method, content and language integrating learning, task-based language teaching, communicative language teaching, and community language learning. These five methods represent widely used approaches to second- or foreign-language teaching rather than instructional methods in general, and the findings are accordingly specific to language-acquisition classrooms. Using thematic analysis of interviews with 25 teachers, this research recognized 20 unique ethical tensions across five teaching methodologies. These findings are synthesized into a holistic framework comprising three overarching ethical domains: authenticity of learning, authority challenge, and collaborative dynamics. Based on this framework, this research highlights the need for moving beyond general frameworks to method-based ethical policies in education. This research emphasizes a process-oriented assessment framework to ensure transparency in education and mitigate the risk of unethical issues and tensions in the educational system. Further implications for students, teachers, and policy makers have been discussed.