AI for Lab Researchers
Use AI for literature, protocols, analysis code and notes in the lab without compromising rigour
A taste of a lesson
AI wrote me an R script for my ANOVA and the results look significant. Can I use them in my thesis?
Not until you've checked it. First, understand every line: ask the AI to explain them and confirm it's using the right variables and factor levels. Second, check whether ANOVA's assumptions hold for your data, such as independence, roughly normal residuals and similar variances, or whether your design needs a different model. Third, test the script on a small dataset where you know the answer. Then discuss with your supervisor. What's your experimental design: how many groups and are measurements repeated on the same samples?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Use AI to scan and summarise literature and verify what matters
- Draft protocols and notebook entries without letting AI invent details
- Write and test analysis code with AI assistance
- Apply safety, data protection, image integrity and disclosure rules
Lesson plan
- 1 Literature without fabrication Use AI to scan literature and verify references and methods details. Start
- 2 Protocols and safety Draft protocols from your notes while keeping safety procedures and approval in charge. Start
- 3 Analysis code you understand Write and explain analysis scripts with AI, then test them properly. Start
- 4 Notebooks, images and integrity Use AI for structure only and follow rules on records and images. Start
- 5 Data, reproducibility and disclosure Protect participant data, record your process and disclose AI use correctly. Start
Try asking
About this tutor
For PhD students, postdocs, research technicians and lab managers in the life and physical sciences. We look at where AI helps day to day: scanning literature, drafting protocols from your notes, writing and explaining analysis code, structuring lab notebook entries, and preparing presentations. We also cover where it can damage research: invented references, plausible but wrong protocols, silent coding errors, image integrity, data privacy for human samples, and reproducibility. Safety is non negotiable: AI never replaces your lab's safety procedures, risk assessments or supervisor approval.
Reviews
4.5
2 ratingsSample
- Nils E.Sample
The 'test on data where you know the answer' habit caught a normalisation bug in my qPCR script. Probably saved a chapter of my thesis.
- Ayesha R.Sample
Good balance of usefulness and caution. The safety emphasis was right. Would love more on image analysis workflows.
About the teacher
Nurse turned informatics lead teaching safe AI use in health, research, care and public service
9 tutors 428 lessons taught Sample
I trained and worked as a nurse, moved into clinical informatics, and later supported research teams, social care services and public bodies with data and digital projects. That path taught me how much paperwork stands between professionals and the people they serve, and how badly things go when a tool is trusted beyond its limits. I teach clinicians, administrators, researchers,...
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