AI Science Fair Project Ideas

An AI science-fair project should investigate a question, not merely demonstrate a tool. A useful question identifies what will be compared or measured: Does a classifier make more errors when the background changes? Does a small training set perform differently from a larger one? How does a simple rule-based baseline compare with a learned model on the same test examples? Keep the scope small enough to repeat and explain. Define the task, dataset, labels, baseline and evaluation method before running experiments. Separate training examples from test examples, record model and tool versions, and preserve a log of changes. Report failures as well as successes. If the project uses a public dataset, read its documentation and license; “available online” does not automatically mean unrestricted. Avoid using private or identifiable data without appropriate approval. Projects involving surveys, interviews, testing by other people or identifiable information may trigger human-participant review. The Society for Science’s ISEF rules explain that some student studies need prior Institutional Review Board review and consent, and that affiliate fairs can have additional requirements. Do not start data collection until the applicable teacher, sponsor or review committee confirms the plan. Avoid medical diagnosis, sensitive personal data or risky testing as a student project. AI use in the competition submission also has rules. Current ISEF guidance says AI may be used as a project resource with citation and acknowledgment, but prohibits generative AI from writing the research plan, abstract, poster or citations. Other fairs may set different rules. Check the current official organizer rules before beginning, keep the student’s own research and writing visible, and cite data, code, tools and collaborators. A successful project explains what the model did, where it failed and what the evidence can—and cannot—support.