This study explores perceptions and practices of AI ethics among higher education students at Banaras Hindu University, using the AI and Ethics Perception Scale (AEPS) dimensions, including transparency, accountability, privacy, fairness, and human oversight, as a conceptual framework.
Abstract
AI ethics has become an essential part of higher education today, reshaping traditional ideas about academic honesty for the digital age. It remains unclear how students interpret the ethics of AI use, experience ethical principles, and apply guidelines in their academic settings. To fill the gap, this study explores perceptions and practices of AI ethics among higher education students at Banaras Hindu University (BHU), using the AI and Ethics Perception Scale (AEPS) dimensions, including transparency, accountability, privacy, fairness, and human oversight, as a conceptual framework. An interpretative qualitative case study was conducted with 15 purposively selected students from seven departments at BHU. We analysed responses from their semi-structured interviews using thematic analysis. The study findings show that most students (N = 10) perceive AI as lacking transparency, particularly regarding its sources and accuracy, and acknowledge their accountability for AI-assisted work. Students (N = 15) expressed concern about privacy, particularly about avoiding the entry of personal, financial, or academic information into AI systems. The study also found that teacher review plays an important role in maintaining academic integrity. Students reported frequent use of AI in their own educational tasks, often without considering ethical implications. The use of AI has affected their study habits, time management, and learning strategies, while also reducing their stress levels. This study suggests that universities should develop their own ethical guidelines and frameworks for the use of AI. They should also conduct seminars and workshops to raise awareness among students and teachers about ethical AI use in academia.
This study, conducted using an explanatory sequential mixed-methods design, aims to examine the views of faculty members and students on ethical issues related to the use of artificial intelligence (AI) in higher education. In the quantitative phase, data were collected via a survey from 971 students and 135 faculty members, followed by semi-structured interviews with 23 students and 14 faculty members to obtain qualitative data. The findings show that both groups generally expressed supportive views regarding the ethical use of AI, but these views took different forms at the individual, technological, institutional, and societal levels. Faculty members emphasized the importance of ethical principles but pointed out the lack of institutional guidelines and support. Students, on the other hand, stated that AI tools were beneficial in their learning processes, but expressed uncertainty about the sharing of ethical responsibilities and the assessment of the ethical appropriateness of the processes. Qualitative findings showed that perspectives were shaped in six themes, revealing a multidimensional ethical structure in the use of AI in education. Participants emphasized that while AI facilitates learning, its excessive use can have negative effects on cognitive skills. The results indicate that, in order for AI to be used ethically in higher education, professional development support should be provided to faculty members, courses covering ethical dimensions should be added to curricula, clear ethical usage guidelines should be established at universities, and these guidelines should be updated through interdisciplinary collaboration.
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