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Prerequisites       

This course is designed for participants with a minimum B1-level proficiency in German and B2-level proficiency in English, as defined by the Common European Framework of Reference for Languages (CEFR). Although prior knowledge in linguistics or language studies is recommended, it is not a prerequisite."

 

Approach  

This course aims to enhance participants’ understanding of language through the lens of Human-AI Interaction, with a particular focus on large language models and generative AI. It explores how linguistic principles shape interactions between humans and artificial intelligence, offering an interdisciplinary approach combining linguistics, pragmatics, discourse analysis, and conversation analysis. The course is structured into three thematic modules: the first module explores how AI systems engage in constructing identity and influence user behavior, the second focus is on how AI systems navigate ambiguous language and social relationships in conversations and the last module examines the mechanics of conversation, including how AIs manage dialogue flow, false starts, and repair strategies. Participants engage with core theoretical works in Digital Conversation Analysis and Digital Discourse Analysis while applying these insights to real-world interactions with generative AI. Through practical exercises, participants reflect on their own and others' utterances, analyze AI-generated responses, and evaluate interactions on various platforms. By the end of the course, students deepen their understanding of linguistics and pragmatics, develop empirical data analysis skills, and gain practical expertise in analyzing digital discourse.The learning objectives are also to prepare participants in their future studies in interactional linguistics and AI development.

Methods and forms of work    

The module covers the following main topics and involves a variety of learning activities, including: 

  • Topics: Human prompts, AI responses,  Human-AI conversations.

  • Lectures on theoretical foundations.

  • Engagement with Generative AI through hands-on interaction

  • Empirical Data Analysis

  • Presentations and Discussions

Performance monitoring

  • Active participation in class activities

  • In class quiz

  • Oral presentations

  • Final Paper

Learning materials: Specialist literature and reading materials will be provided to the participants before the course starts to support their learning.

Beginn/Term duration: On request 

Mode of delivery: Online, face-to-face (only company course); blended.