AI in research

Books matched to this capability.

5books in this track

L01 · User

Who is affectedOnly you
T15-L01

Use AI in Your Research and Say Exactly How

Use an AI assistant for literature triage, language and code without misrepresenting your work. Keep a one-line-per-use log as you go, check what your journal or funder requires, and write a disclosure statement specific enough that a reviewer knows what the tool did and what you did.

Outcome: A disclosure statement for one real piece of your work, checked against the target journal's or funder's AI policy, with the use log behind it; it passes when a co-author can match every sentence of the statement to a log entry and to the policy.

User13 minLast edited: Sep 23, 2026

L02 · Power user

Who is affectedYour team sees a bad document or a wrong figure
T15-L02

Record an AI Step So a Co-Author Can Rerun It

Make one AI-assisted research step repeatable by someone else. Pin the model, keep the input, prompt and raw output together, decide before rerunning what counts as the same answer, and report the differences honestly when a co-author, reviewer or auditor asks you to run it again.

Outcome: A shared folder for one AI-assisted step (input with checksum, prompt version, run record, raw outputs of at least two runs, comparison) plus a co-author's rerun from the folder alone; it passes when the co-author's rerun is compared on the rule you set beforehand and the Methods sentence states the agreement and what couldn't be fixed.

Power user16 minLast edited: Sep 23, 2026

L03 · Builder

Who is affectedColleagues depend on something you built
T15-L03

Get an AI Step on Participant Data Reviewed, Then Build It Inside the Approval

Before an AI step touches participant data, map where the data goes, compare it with what was approved, and ask the people who decide. Then build the step to fit the answer: minimised input, a local model, a test on hand-coded items, a README, and a colleague who can rerun it and knows when to stop.

Outcome: One review request with its data-flow map and the reviewers' answer, plus the working AI step: README, run record, the hand-coded test set and a colleague's rerun. It passes when no real data went anywhere before the answer, the test result meets the threshold you wrote down in advance, and the colleague reruns the step from the README and gets the same labels.

Builder18 minLast edited: Oct 12, 2026

L04 · Integrator

Who is affectedCompany or lab systems and real data are in the loop
T15-L04

Set Up AI for a Whole Research Group

Give your research group one AI setup instead of nine private ones: an agreed assistant on known terms, a map of the group's data and who can see it, three paste rules, a shared AI-use log that feeds methods sections, and an onboarding hour for the next PhD student. Reviewed and signed by the PI.

Outcome: A one-page group AI-use policy plus a data map (each data source, its class, the tools allowed, who has access), reviewed and signed by the PI with a review date; it passes when every data source the group uses appears in the map and a new member can answer three paste questions from the policy alone.

Integrator20 minLast edited: Sep 23, 2026

L05 · Operator

Who is affectedWork stops when it breaks, and it must survive you leaving
T15-L05

Keep Your Group's AI Use Compliant and Running After You Leave

Turn a group's AI setup into something that survives an audit, a reviewer and your departure: a register of every AI use with the data-protection and AI Act questions answered by the right office, an archive of prompts, models and versions next to the data, a retention plan, and a successor who has run it.

Outcome: A compliance and continuity dossier: a register of the group's AI uses with who decided each open question, a retention plan per item, and a record of a named successor's walkthrough; it passes when the successor has run the main AI-assisted step and restored one archived result without help, and the PI has signed.

Operator21 minLast edited: Sep 23, 2026