AI Research Efficiency 101
Why Research Teachers Need AI Workflows
Many teachers hear “AI coding” and immediately step back. That reaction is understandable. Most researchers do not want another technical rabbit hole.
But the useful question is not “Can I code with AI?”
The useful question is: which parts of my research workflow can be delegated, checked, and reused?
A workflow is not a magic tool
A research workflow is the path from an unfinished task to a usable output.
For a literature review, that path might include search terms, screening criteria, note templates, argument structure, and draft revision. AI can help with many of those steps, but it should not replace the final judgment.
The point is not to automate scholarship. The point is to reduce the friction around scholarship.
Start with three tools
You do not need a large stack.
Start with:
- A chat model for explanation, rewriting, and quick reasoning.
- A coding agent for files, scripts, charts, and repeatable data tasks.
- A repository or workspace where useful prompts and templates can be reused.
This is enough to build a real system. Anything beyond that should earn its place.
Keep the human boundary clear
AI can summarize, compare, format, rewrite, and generate options.
You still own the research question, evidence standards, disciplinary judgment, and final wording.
That boundary is not a weakness. It is the whole reason the workflow is trustworthy.
A small first step
The next time you write a section of a paper or course handout, ask AI for a draft only after you give it the goal, audience, constraints, and acceptance criteria.
Then revise it yourself.
That small loop is already an AI workflow.
This is article 1 in the “AI Research Efficiency 101” series.