Trust plays a pivotal role in Lecturer-Student-Collaboration, encompassing
teaching and research aspects. The advent of Large Language Models (LLMs) in
platforms like Open AI's ChatGPT, coupled with their cost-effectiveness and
high-quality results, has led to their rapid adoption among university
students. However, discerning genuine student input from LLM-generated output
poses a challenge for lecturers. This dilemma jeopardizes the trust
relationship between lecturers and students, potentially impacting university
downstream activities, particularly collaborative research initiatives. Despite
attempts to establish guidelines for student LLM use, a clear framework
mutually beneficial for lecturers and students in higher education remains
elusive. This study addresses the research question: How does the use of LLMs
by students impact Informational and Procedural Justice, influencing Team Trust
and Expected Team Performance? Methodically, we applied a quantitative
construct-based survey, evaluated using techniques of Structural Equation
Modelling (PLS- SEM) to examine potential relationships among these constructs.
Our findings based on 23 valid respondents from Ndejje University indicate that
lecturers are less concerned about the fairness of LLM use per se but are more
focused on the transparency of student utilization, which significantly
influences Team Trust positively. This research contributes to the global
discourse on integrating and regulating LLMs and subsequent models in
education. We propose that guidelines should support LLM use while enforcing
transparency in Lecturer-Student- Collaboration to foster Team Trust and
Performance. The study contributes valuable insights for shaping policies
enabling ethical and transparent LLMs usage in education to ensure
effectiveness of collaborative learning environments.

利用大型语言模型 (Large Language Models，LLMs) 对学生的影响如何对信息公正性和程序公正性产生影响，从而影响团队信任和预期团队绩效，对教育中利用 LLMs 的使用进行了研究并提出了相应的指导方针。