Pre-workshop survey https://bino.qualtrics.com/jfe/form/SV_08L5ssRcXvhlrMy
Day 1 - April 4, 2025 (9:30 AM - 5:00 PM)
Morning Session (9:30 AM - 12:30 PM)
9:30 - 10:15 | Foundations
Introduction to GenAI applications in research
Key concepts and terminology
Preparation for the workshop
10:15 - 11:00 | Literature Review Management
Literature review tools
Literature management
11:15 - 12:30 | Deep Research & Automation
Deep research
Research workflow optimization
Customized assistants
Afternoon Session (1:30 PM - 5:00 PM)
1:30 - 3:00 | Stimuli Generation & AI Interviewers
Creating research materials using AI
Implementation of AI-powered interviews
3:15 - 5:00 | Text Analytics
Annotation and theme generation
Local model deployment for sensitive data
Day 2 - April 5, 2025 (9:30 AM - 4:00 PM)
Morning Session (9:30 AM - 12:30 PM)
9:30 - 10:45 | RAG and Knowledge Base
Implementation Retrieval-augmented generation techniques
Building research knowledge bases
10:45 - 12:30 | No-Code Programming Solutions
Afternoon Session (1:30 PM - 4:00 PM)
1:30 - 2:45 | Advanced topics
Data visualization
Data analysis
Fine-tuning models for research
2:45 - 4:00 | Synthetic dataset
Preparation
- Register a Google account if you do not have one.
- Register on one of the following AI platforms available in your region and compatible with your university policy: US-based: OpenAI, Claude, or Gemini France-based: Mistral China-based: DeepSeek
- Check this link and download the study materials: https://github.com/lanceyuu/osloworkshop
- Optional: You may want to install the following software: AnythingLLM: https://anythingllm.com/ (An all-in-one AI application that works locally and offline) LM Studio: https://lmstudio.ai/ (A tool to discover, download, and run local LLMs) Cursor: https://www.cursor.com/ (An AI-powered code editor)
- Optional: Read the assigned papers (They are all open-access)
Gilardi, F., Alizadeh, M., & Kubli, M. (2023). ChatGPT outperforms crowd workers for text-annotation tasks. Proceedings of the National Academy of Sciences, 120(30), e2305016120. https://doi.org/10.1073/pnas.2305016120
Wuttke, A., Aßenmacher, M., Klamm, C., Lang, M. M., Würschinger, Q., & Kreuter, F. (2024). AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers. ArXiv.org. https://arxiv.org/abs/2410.01824
Yeykelis, L., Pichai, K., Cummings, J. J., & Reeves, B. (2024). Using Large Language Models to Create AI Personas for Replication and Prediction of Media Effects: An Empirical Test of 133 Published Experimental Research Findings. arXiv preprint arXiv:2408.16073.
Zeph M. C. van Berlo, Colin Campbell & Hilde A. M. Voorveld (2024) The MADE Framework: Best Practices for Creating Effective Experimental Stimuli Using Generative AI, Journal of Advertising, 53:5, 732-753, https://www.tandfonline.com/doi/full/10.1080/00913367.2024.2397777