From Ethical Discourse to Empirical Evidence: How Ethical Concerns are Operationalized in Studies on Generative AI in Higher Education

The rapid adoption of generative artificial intelligence (GenAI) in higher education has raised widespread ethical concerns, particularly in computer science (CS) education. While issues such as academic integrity, fairness, bias, and responsibility are frequently discussed, it remains unclear how these concerns are empirically examined in existing research. This paper presents a systematic analysis of how ethically relevant concerns are addressed in peer-reviewed empirical studies on GenAI use in higher CS education. Using a structured selection protocol, we analyze a corpus of empirical studies published since the public release of large language models, focusing on how concerns such as academic integrity, fairness, bias, responsibility and student autonomy are defined, studied, and methodologically grounded. Our analysis reveals a substantial gap between ethical discourse and empirical practice: although ethics is often mentioned, only a small subset of studies integrates ethically relevant concerns as a core analytical dimension with explicit empirical grounding. Most studies rely on indirect proxies or address ethics implicitly, without clear operational definitions or evaluative frameworks. Based on these findings, we propose an empirically grounded categorization of ethical concerns as addressed in current research and outline methodological directions for more robust integration of ethics into future empirical studies on GenAI-supported CS education.