AI‑supported literature research makes it possible to systematically explore large data sets, quickly identify relevant publications, and reveal complex thematic connections. Furthermore, AI Tools provide effective support in preparing presentations or academic papers, as they can help with topic development, literature analysis and text revision.
When used in a purposeful and responsible manner, AI enhances the literature research and facilitates academic work.
Training course: ‘Literature Research with AI – Tips and Tools’
This training course provides an overview of how AI can be used in literature research. It explains how generative AI assistants work and introduces tools such as Consensus (for precise literature discovery) and Research Rabbit (for visualizing thematic connections). The focus is on effectively combining AI tools with traditional research methods to optimize the literature research process. This course is held in German.
Training course: ‘AI Tools for Academic Work’
The training highlights key aspects of how generative AI is used and how it functions in the context of academic work. The thematic focus lies on the targeted formulation of prompts and the use of specific AI tools to support literature research and document analysis. The skills acquired enable an efficient and reflective use of current AI technologies in scholarly work. This course is held in German.
AI Literacy has established itself as an interdisciplinary approach to meet the challenges concerning AI. Its focus is on imparting knowledge and skills necessary to critically evaluate AI systems, collaborate with them, and apply them effectively in various areas of life. In terms of information literacy, engaging with AI therefore represents an important responsibility of the university library.
Artificial Intelligence (AI) refers to systems that mimic human-like cognitive abilities – ranging from speech recognition and text generation to decision-making and problem-solving. These technologies offer promising opportunities for academic work, but they also require a critical approach.
Generative AI is the umbrella term for all AI systems that use trained statistical models to autonomously create new (though not necessarily factually accurate) content such as text, images, audio, code or videos.
Large Language Models (LLMs) like ChatGPT or Open WebUI are a subcategory of generative AI that understand, process, and generat text. They are trained on an enormous amount of textual data and have analysed billions of word combinations. LLMs generate responses solely through statistical predictions within the given query context (the so‑called prompt). Since they interpret language only as patterns of probability, they lack critical reasoning and genuine language understanding, which is why they are also referred to as “stochastic parrots” (Bender et al. 2021).
Retrieval‑Augmented Generation (RAG) can enhance AI‑supported literature research by linking large language models with external data sources such as research papers (e.g., from PubMed) or personal PDF collections. This approach produces more accurate, evidence‑based results and helps minimize the risk of incorrect or fabricated information (“hallucinations”). It is already employed in AI tools such as Perplexity, Elicit, and Semantic Scholar. The quality of the output depends on the underlying data and still requires careful critical evaluation.
