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This study investigates students’ aversion to AI grading systems compared to human professors, focusing on how dissatisfaction with the current evaluation system and grade outcomes affect fairness perceptions. Drawing from 228 college students in South Korea, the experiment tested three hypotheses: (1) students prefer human graders (i.e., AI aversion), (2) dissatisfaction with the current system mitigates the AI aversion, and (3) this mitigation is contingent on whether students receive high or low grades. Results confirm a general aversion to AI graders. Yet, students who are dissatisfied with the status quo system displayed increased preference for AI graders (reduced AI aversion), especially when receiving low grades. In contrast, those receiving high grades continued to prefer human professors regardless of dissatisfaction. These findings suggest that students’ openness to AI graders is shaped by their discontent with the current systems and their self-interest, influencing fairness perceptions in educational settings.

As AI tools become increasingly integrated into academic settings, it is critical to understand their roles in teamwork in collaborative settings. This study aims to understand how AI Usage is reshaping teamwork by examining college students' engagement with AI in group projects, focusing on usage scenarios, perceived AI roles, and task allocation. We conducted semi-structured interviews with 20 undergraduate and graduate students across diverse disciplines and coded the transcripts according to a team-AI collaboration framework. Findings reveal that: 1) students employed AI in various task types, including creative, information processing, output-oriented, and planning tasks, assigning roles such as ideation partner, expert advisor, and time planner depending on the context; 2) while AI use enhanced productivity and efficiency, it also led to reduced human communication, reduced sense of ownership, and concerns about skill development and academic integrity; 3) limited transparency regarding AI use within teams may lead to distrust and uncertainty about individual contributions, highlighting the importance of establishing shared awareness in collaborative work involving AI. The findings suggest that teams should be more deliberate in human-AI task allocation to take advantage of AI capabilities and be more transparent among team members of AI use during collaboration.

Explorations of AI use in higher education have included ethical concerns, though primarily have taken a broad view, focusing on concerns with academic integrity and plagiarism. More nuanced explorations into the ethics of AI use, especially from a qualitative perspective, have been lacking. Utilizing Floridi’s unified framework of ethical principles for AI as a guide, this study addresses this gap with a qualitative exploration of undergraduate students’ perceptions of the ethical underpinnings of AI tools in the context of use for course work completion. Findings suggest that beneficence, non-maleficence, and autonomy are clearly present in students’ perceptions of ethics in AI, but justice and explicability were not. This suggests a deeper understanding of ethics in AI use beyond fear of plagiarism, but a noted lack of understanding around the true nature or impact of AI. These findings invite additional research around students’ understanding of AI and the inclusion of faculty.

On-campus generative AI (GenAI) deployment enhances the digital infrastructure of universities and can provide opportunities for students to interact with GenAI. However, limited research has explored the attitudes of college students initially exposed to on-campus GenAI deployment. This study conducts semi-structured interviews with 13 participants based on the socio-technical configuration perspective. The preliminary findings show that the on-campus GenAI deployment as a socio-technical enacted information practice has four characteristics: AI-focused activities, generative of rules and norms, the inclusion of individual and collective agency, and the embrace of the body and materiality. Furthermore, our findings illuminate students' general understanding of on-campus GenAI deployment, their developing prompt literacy, and their primary concerns and worries regarding its use. This work provides valuable insights into student attitudes toward on-campus GenAI deployment and contributes to a nuanced understanding of such information practices.