MIDAS AI in Research

Part I — AI Across the Research Lifecycle

  • Chapter 1: Overview: Artificial Intelligence in Research
  • Chapter 2: How Modern AI Works
  • Chapter 3: Prompt Engineering
  • Chapter 4: When to Use AI in Research
  • Chapter 5: Literature Review with AI
  • Chapter 6: Research Planning and Hypothesis Development with AI
  • Chapter 7: AI-Assisted Writing and Research Communication
  • Chapter 8: Grant Writing with AI
  • Chapter 9: AI-Assisted Reviewing
  • Chapter 10: Ethics, Privacy, and Compliance
  • Chapter 11: Checking AI Output

Part II — AI in Data Analysis

  • Chapter 12: Getting Your Data: Access, Sources, and Compliance
  • Chapter 13: Computing Resources for AI Research
  • Chapter 14: Exploratory Data Analysis with AI
  • Chapter 15: Data Preparation with AI Assistance
  • Chapter 16: Feature Engineering for Research Data
  • Chapter 17: AutoGluon Fundamentals: Tabular Prediction
  • Chapter 18: Time Series Forecasting with AutoGluon
  • Chapter 19: Multimodal Learning with AutoGluon
  • Chapter 20: Pre-trained Models for Text, Vision, and Audio
  • Chapter 21: Validation and Interpretation of AI-Assisted Analysis
  • Chapter 22: Reproducibility in AI-Assisted Research

Part III — Building with Modern AI

  • Chapter 23: NLP with Pre-trained Language Models
  • Chapter 24: Building a Research Knowledge Base with RAG
  • Chapter 25: AI Agents: From Single Answers to Multi-Step Research Tasks
  • Chapter 26: LLM Evaluation and Fine-tuning

Part IV — Resources & Reference

  • Chapter 27: AI Resources at the University of Michigan
  • Chapter 28: External AI Resources for Research
  • Chapter 29: Quick Reference Templates
  • Chapter 30: AI Literacy Glossary
  • Chapter 31: Computing Fundamentals
  • MIDAS Video Resources
MIDAS AI in Research
  • Search

© Copyright 2026, Michigan Institute for Data and AI in Society.

Built with Sphinx using a theme provided by Read the Docs.