This paper provides a systematic and critical review of the existing literature on the phenomenon of ‘commercial contract cheating’ (CCC). Unlike some existing systematic reviews _generally_ on CCC, this paper focuses on the potential causes and suggested preventative measures specifically, intending to develop effective interventions on the basis of empirical insights. We reviewed primary studies with empirical data and systematic reviews focusing on higher education published between 2012 and 2020. A logic model is developed to graphically indicate the complex and (...) dynamic interplay between a variety of factors identified. Our inquiry reveals a highly specified, uncoordinated and fragmented research landscape that urgently needs integrated, holistic and critical reflection. It shows that the current research is still far from establishing causal relationships as the scholarship opts to reveal an abundance of contextual factors identified only, without probing the relational dynamics or striving for causality. A range of broad and tentative recommendations are proposed on that basis but are barely empirically examined. We also attend to lack of conceptual clarity and work towards a more inclusive and future-proof definition of CCC beyond assignment-based conceptualisation. It is argued that CCC should never be reduced to plagiarism (a natural inclination inspired by the plagiarism research tradition) nor neutralised as a legitimate business (a potential risk revealed mostly by the supply side research). As an interdisciplinary field, CCC should move beyond student perceptions of education cheating and affordability, while including more inquiries into the exploitative, predatory nature of the industry. (shrink)

The advent of ChatGPT by OpenAI has prompted extensive discourse on its potential implications for science and higher education. While the impact on education has been a primary focus, there is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice. To investigate this further, we conducted a Delphi study involving 72 researchers specializing in AI and digitization. The study focused on applications and limitations of LLMs, their effects on the science (...) system, ethical and legal considerations, and the required competencies for their effective use. Our findings highlight the transformative potential of LLMs in science, particularly in administrative, creative, and analytical tasks. However, risks related to bias, misinformation, and quality assurance need to be addressed through proactive regulation and science education. This research contributes to informed discussions on the impact of generative AI in science and helps identify areas for future action. (shrink)