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.
This course addresses the use of artificial intelligence in academic work and covers, among other things, general applications, opportunities, risks, and responsible and critically reflective approaches to its use. In the context of AI literacy, the topic is examined from a fundamental perspective. Specific tools will neither be demonstrated nor covered as part of the course.
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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) 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”). The quality of the output depends on the underlying data and still requires careful critical evaluation.
