Jungherr, AndreasAndreasJungherr0000-0003-2598-24532023-04-142023-04-142023https://fis.uni-bamberg.de/handle/uniba/58950Large language models (LLMs), like ChatGPT, GitHub Copilot, and Microsoft Copilot, present challenges in university education, particularly for paper assignments. These AI-driven tools enable students to (semi)automatically complete tasks that were previously considered evidence of skill acquisition, potentially affecting grading and skill development. However, the use of these tools is not legally considered plagiarism and is becoming increasingly integrated into various software solutions. University education in the social sciences aims to develop students' abilities to make sense of the world, connect their observations with abstract structures, measure phenomena of interest, systematically test expectations, and present findings in structured accounts. These practices are learned through repeated performance of tasks, such as writing research papers. LLM applications like ChatGPT create conflicting incentives for students, who might rely on them to produce parts of their papers instead of engaging in the learning process. While LLMs can be helpful tools for knowledge discovery, writing assistance, and coding assistance, using them effectively and safely requires an understanding of their underlying mechanisms, potential weaknesses, and enough domain knowledge to identify mistakes. This makes LLMs particularly challenging for students in the early stages of acquiring scientific skills and domain knowledge. Educators must enable and train students to responsibly use these new tools, reflecting on the underlying tensions and their strengths and weaknesses for academic writing tasks. This working paper aims to provide guidelines on responsible LLM use in academic contexts, specifically for students at the Chair for the Governance of Complex and Innovative Technological Systems at the University of Bamberg. The paper discusses the function of written paper assignments, the tasks necessary to complete them, and evaluates ChatGPT's performance in assisting with these tasks. It concludes with observations and advice for students to maximize the benefits of LLMs while mitigating potential risks in academic contexts, focusing on enabling learning.engAIartificial intelligenceChatGPTlarge language modelsLLMspolitical scienceresearch paperteachingwriting320workingpaperurn:nbn:de:bvb:473-irb-589507