Leveraging AI Tools for Academic Research

A One-Week Boot Camp for Faculty Researchers

Author

The rgtlab Curriculum Project

Published

2026-09-01

Welcome

This is the online version of Leveraging AI Tools for Academic Research: A One-Week Boot Camp by The rgtlab Curriculum Project, a short workshop for faculty researchers who run their own grant-funded projects, teach and supervise students, and want AI tools to make that whole workload faster without making it riskier.

The primary payoff of this week is speed: compiling a working bibliography in minutes instead of hours, scaffolding a reproducible project skeleton in one prompt instead of an afternoon, and turning a scattered set of notes into a structured outline through a short dialog rather than a blank page. The book covers those efficiency techniques directly, alongside the verification and disclosure discipline (recognizing hallucination and fabricated citations, reviewing AI-generated code, protecting confidentiality, and disclosing AI use honestly) that keeps the speed from costing a retraction, a misconduct finding, or a withdrawn grant. That discipline matters, but it is in service of the efficiency, not the other way around: a faculty researcher takes this workshop to get more done in a week with the same number of hours, and reads the risk material because working fast on someone else’s terms (a journal’s, a funder’s, an IRB’s) is not actually fast if the work has to be redone. The book is designed for five consecutive days of work: one hour of lecture content each day, two hours of homework, no examinations.

The organizing principle is that AI tools are accelerants, not replacements, for the judgment a researcher already exercises. Every chapter opens with a concrete research task, develops the AI-assisted technique that addresses it, and returns to the task to judge what the technique did and did not solve, including where it can mislead. An appendix extends this discipline into three roles specific to faculty life that the five-day curriculum does not otherwise cover: writing grant proposals under funder-specific AI rules, setting AI policy for one’s own students, and resolving AI-related authorship questions with one’s own trainees.

The workshop is a companion to the rgtlab curriculum sequence:

A reader who has completed R for Biostatistics and Git and GitHub for Biostatistics arrives at this workshop ready to use AI tools inside an already-versioned, reproducible research workflow.

See the Preface for the design rationale and the Conventions page for visual cues.

License

This book is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International.

Code samples are licensed under Creative Commons CC0 1.0 Universal, i.e. public domain.