How can generative AI be used in humanities research and academic writing in a responsible, reflective and academically sound way? This online workshop series is designed for PhD Candidates and Postdocs who want to develop a sound understanding of how generative AI works, where its limitations and risks lie, and how legal, organizational and academic guidelines shape its use in research contexts.

The first session focuses on basic principles, data protection, academic integrity, transparency, authorship and structured prompting with KI-Chat@JGU.

Between the two sessions, participants will test AI applications in their own research or writing context. The second session builds on these experiences and focuses on prompt refinement, quality assurance and context engineering.

The central question throughout is how AI can support academic work without replacing the researcher’s own intellectual contribution, authorship and argumentative responsibility.

Language-proficiency level minimum English C1 (CEFR).

PhD Candidates and Postdocs of the Humanities and Social Sciences.

Content:

• Basic principles of how generative AI works, including key limitations and risks such as hallucinations, bias, data flow and context dependency
• Legal, organizational and academic guidelines for responsible AI use, with a focus on data protection, transparency, documentation, authorship and academic integrity
• Structured prompting with KI-Chat@JGU, using participants’ own examples, experiences and challenges
• Quality assurance, AI-supported literature-related work and source criticism, including typical pitfalls such as fabricated references or misleading AI-generated certainty
• Basics of context engineering for humanities research and writing, including research question, text type, sources, theoretical perspective, argumentation, audience and assessment criteria

Learning Objectives:

Participants will

• be able to assess opportunities, limitations and risks of generative AI in their own humanities research context
• be able to apply relevant guidelines and principles of responsible AI use to typical academic work situations
• be able to formulate, test and improve prompts for their own academic work more systematically
• be able to critically evaluate AI-generated output in relation to argumentation, source work, authorship and scholarly responsibility
• develop first ideas for responsible, transparent and academically sound AI workflows in their own research practice