In this article in the legal journal Juristische Schulung, research assistants Christian Braun, Sarah Großkopf and Simon A. Nonn discuss the question of whether ChatGPT can replace university lecturers - especially with regard to the teaching of legal reasoning skills and the "Gutachtenstil" using AI feedback.

In recent years, the Covid-19 pandemic and the associated rapid developments and advances in the field of digitalization have shown that didactics in higher education is currently undergoing change and that this process can and should be actively influenced in order to maintain the future viability and competitiveness of universities. A large part of this is the use of innovative technology and tools, such as artificial intelligence (AI), in particular large language models (LLM) and natural language processing (NLP), to create digital teaching and learning spaces for students of future generations.

This study reports the Intraclass Correlation Coefficients of feedback ratings produced by OpenAI's GPT-4, a large language model (LLM), across various iterations, time frames, and stylistic variations. The model was used to rate responses to tasks related to macroeconomics in higher education (HE), based on their content and style. Statistical analysis was performed to determine the absolute agreement and consistency of ratings in all iterations, and the correlation between the ratings in terms of content and style. The findings revealed high interrater reliability, with ICC scores ranging from 0.94 to 0.99 for different time periods, indicating that GPT-4 is capable of producing consistent ratings. The prompt used in this study is also presented and explained.

In this study we (Abdullah Al Zubaer, Michael Granitzer and Jelena Mitrović) investigate the effectiveness of GPT-3.5 and GPT-4 for argument mining in the legal domain, focusing on prompt formulation and example selection using state-of-the-art embedding models from OpenAI and sentence transformers. Our experiments demonstrate that relatively small domain-specific models outperform GPT 3.5 and GPT-4 in classifying premises and conclusions, indicating a gap in these models' performance for complex legal texts. We also observe comparable performance between the two embedding models, with a slight improvement in the local model's ability for prompt selection. Our results indicate that the structure of prompts significantly impacts the performance of GPT models and should be considered when designing them.

From 24-26 June 2022, ELSA-Passau organised the second June conference under the motto "Smart Law". This conference was to address nothing less than the future of law and the digitalisation of the legal profession. In the attached conference report, the project DeepWrite, among others, is discussed.