Teaching notes: Teaming with (AI) agents
- PREP/PRINT papers
- PREP Case descriptions (digital material)
| Time (start) | Duration | Topic | Additional materials |
|---|---|---|---|
| 00:00 | 15 min | Summary: Cutolo and Kenney | Paper summary / key points slide |
| 00:15 | 60 min | Agentic IS: Conceptual Foundations | Slides, key literature excerpts |
| 01:15 | 20 min | Studies: Empirical Exploration | Selected articles or abstracts |
| 01:35 | 70 min | LLMs and Prompting Examples | Code notebooks, prompt design handout |
| 02:45 | 165 min | Overall |
In this session, our goal is to explore how to effectively team with (AI) agents, particularly large language models (LLMs), using evidence-based prompting techniques and interaction modalities.
Develop prompts for a given scenario by selecting appropriate prompting techniques and interaction modalities. (must) • Selection of scenario decisions • Notes on solutions
Exercises: Option 1 (strong understanding): • Individual work on application scenarios (prompts) • Discussion of solutions
Option 2 (limited understanding): • Solving cases together
When time runs out: Additional cases can be prepared at home
Exercise 1: Catching Up After Vacation
You are an expert assistant helping me catch up on work after a 2-week absence. I will paste Slack messages and email excerpts from the last two weeks. Summarize the most important topics, decisions made, unresolved issues, and action items relevant to me. Group the information by project if possible.
Exercise 2: Self-Coaching Through a Challenging Situation
Act as a self-coaching assistant trained in productivity and well-being. Ask me reflective questions to help me identify my current challenges, clarify priorities, and explore ways to manage my time and energy more effectively. Begin by asking: “What are the main things on your mind right now?”
- Why suggest a specific question? -> Prompt context of interactive dialogue (output constraint).
Note: coaching more generally: provide the CV