Claude-Powered Academic Research: From Ideation to Publication - A Short Course

The seminar is built around real research workflows. Rather than abstract demonstrations, you’ll work through the same tasks you face in your own projects: sharpening a research question, structuring a replication-ready codebase, scraping and wrangling data, running statistical models, and producing polished, submission-ready writing. Each module pairs conceptual grounding with hands-on exercises that you can immediately adapt to your own work.

A core theme throughout the seminar is replicability. Journal requirements for replication packages are now the norm rather than the exception, and the organizational habits and tooling introduced in this course make compliance a natural byproduct of good research practice rather than an afterthought.

This seminar uses Claude (Claude.ai, including the Projects feature), Claude Code, and Claude Cowork as the primary tools throughout. You’ll need a Claude Pro or Team subscription; a free-tier account is not recommended due to usage limits for hands-on exercises.

For project organization, the seminar demonstrates a GitHub-based workflow using Claude Code as the agentic interface. If you prefer to work with a local file system rather than a remote repository, you can follow along using Claude’s built-in work environment (Claude Cowork), which offers comparable organizational capabilities without requiring a GitHub account. Both approaches will be explicitly covered.

No prior experience with Claude Code or GitHub is required. You should be comfortable with basic command-line usage (navigating directories, running scripts) and have a working knowledge of at least one statistical computing language (Python, R, or Stata).

A setup guide with software installation instructions will be distributed about one week before the seminar begins.

This seminar is designed for academic researchers—faculty, postdoctoral scholars, and advanced graduate students—who want to build a systematic, reproducible AI-assisted research workflow. It is relevant across social science, behavioral science, economics, public health, and other empirical disciplines.

You should have some experience conducting original research (designing studies, collecting or working with data, writing for academic audiences) but you don’t need any prior experience with AI tools or programming. The course meets researchers where they are and builds practical capability from there.

Day 1: Ideation, brainstorming, and project organization

• • AI-assisted ideation and research design
    • Structuring conversations for research ideation: literature gap analysis, hypothesis generation, and theory development.
    • Using Claude Projects to maintain a persistent research context across sessions.
    • Hands-on: Building a research brief for a project of your own using Claude.

• • Organizing a research project with Claude Code and GitHub
    • Introduction to Claude Code and Cowork: what it is, how it works, and why it matters for researchers.
    • Setting up a research repository: folder structures, naming conventions, and README files that communicate intent.
    • Developing and applying Claude skills to enable and enhance task execution.
    • Using GitHub as a living research log: commits as a record of decisions, not just code.
      • Alternative: using Claude Cowork for local project management.
    • Hands-on: Initializing a project repository with Claude Code and scaffolding your own research directory.

Day 2: AI-assisted data collection and wrangling

• • AI-assisted data collection and ingestion
    • Using Claude Code to develop web scraping scripts (Python/R).
    • Working with APIs: Claude as a coding collaborator for data ingestion pipelines.
    • Text data at scale: using Claude for qualitative coding, thematic extraction, and classification tasks.

• • Data cleaning, validation, and documentation
    • Data cleaning and validation: prompting Claude Code to diagnose and repair messy datasets.
    • Data documentation: generating metadata and codebooks for reproducibility.
    • Hands-on: Cleaning and documenting a real dataset using Claude Code.

Day 3: Statistical analysis and reporting

• • Running and interpreting analyses
    • Claude Code as a statistical analysis environment: spinning up and executing models in Python and R.
    • Iterative analysis workflows: using Claude to debug errors, suggest alternative specifications, and interpret output.
    • Generating and refining tables and figures for academic publication.
    • Keeping analysis reproducible: scripts, seeds, environment files, and version control.
    • Hands-on: Running an end-to-end analysis pipeline with Claude Code on a provided dataset.

• • Replicability and analysis reporting
    • Keeping analysis reproducible: scripts, seeds, environment files, and version control.
    • Finalizing a replication package: what journals require, how to organize it, and how Claude Code can generate the documentation automatically.
    • Hands-on: Running an end-to-end analysis pipeline and preparing its replication files.

Day 4: Academic writing, editing, and deployment

• • AI-assisted academic writing
    • Using Claude as a writing collaborator: amplification, not replacement.
    • Building a manuscript-in-progress within a Claude Project: maintaining stylistic and argumentative consistency across sessions.
    • Using Claude to draft, expand, compress, and sharpen prose while maintaining your voice.
    • Writing with integrity: transparency norms, disclosure requirements, and where the line is.
    • Hands-on: Drafting a complete methods or results section using Claude.

• • Editing, review response, and deployment
    • Using Claude to tighten arguments, identify logical gaps, and anticipate reviewer concerns.
    • Responding to peer reviews with Claude as a thought partner: categorizing comments, drafting responses, and tracking changes.
    • Hands-on: Drafting a response-to-reviewers memo and replication package README using Claude.