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AI exposure report
Will AI replace Astronomers?
Some of your tasks will be automated. People who use AI will have the edge.
Task exposure to today's AI
Moderate exposure
This is the occupation's typical task mix. Your own score can be up to 30 points higher or lower depending on how you work: routine share, people contact, judgment and how much you already use AI.
Get your personal scoreWill AI replace Astronomers?
Not entirely. Today's AI tools can take on about 45% of the typical work in this occupation, mostly: present research findings at scientific conferences and in papers written for scientific journals. The parts that stay human include: Teach astronomy or astrophysics.; Analyze research data to determine its significance, using computers.; Study celestial phenomena, using a variety of ground-based and space-borne telescopes and scientific instruments..
People who learn to use AI for the automatable parts keep the edge. The 3-phase plan below shows where to start.
Which tasks AI can take on
The largest tasks in this occupation, their share of the working week, and how much of each today's AI tools can do.
+ 11 more tasks (35% of the week) in the full test.
Your human edge
- Teach astronomy or astrophysics.
- Analyze research data to determine its significance, using computers.
- Study celestial phenomena, using a variety of ground-based and space-borne telescopes and scientific instruments.
AI tools to try
ChatGPT / Claude for literature and writingAI research tools (e.g. Elicit, Consensus, Semantic Scholar)Copilot for data analysis code (Python, R)Related roles
A 3-phase AI learning plan
Phase 1
Faster literature and writing
- Literature search and summaries with AI, always checked against the papers.
- Drafts of methods, reports and grant text.
- Reference management and formatting.
Project
Use AI to summarise 30 arXiv papers for your next paper's introduction, verify every claim and citation against the source PDFs, and log hours saved versus your usual review.Phase 2
AI for data analysis
- Analysis code in Python or R written with AI help.
- Visualisation and statistics explained step by step.
- Reproducibility: version code and prompts.
Project
Rebuild one past photometry or spectral reduction pipeline in AI-assisted Python, version the code and prompts in git, and check outputs match your published values within 1%.Phase 3
AI as a research method
- Classification, extraction or modelling with AI on your data.
- Validate against human labels or ground truth.
- Ethics, data protection and disclosure of AI use.
Project
Train an AI classifier on 1,000 labelled light curves or galaxy images, report accuracy against your human labels, and draft an AI-use disclosure paragraph for the paper.