Choose a precise scientific problem
Start with a phenomenon, unsolved technical limitation, measurable variable or reproducibility concern.
AI-assisted, literature-grounded research idea generation is planned. Until then, use this evidence-first process to develop feasible STEM research directions.
The future AI studio will combine verified literature, your available skills and equipment, feasible methods, known limitations and links to the original papers. It will never guarantee that a topic is novel.
Follow these steps independently, with a supervisor, or using an AI assistant while verifying its citations.
Start with a phenomenon, unsolved technical limitation, measurable variable or reproducibility concern.
Read highly relevant original papers. Keep a table of methods, datasets, results, weaknesses and citations.
Look for conflicting results, missing benchmarks, untested conditions, difficult assumptions or small experimental samples.
Specify what you will measure or prove, the comparison, scope and expected uncertainty.
Choose models, experimental controls, evaluation metrics, statistical tests and repeatability checks.
List required data, software, equipment, permissions, cost, timeline, safety and supervisor support.
Test one dataset, prototype or analytical case to discover problems before a major research commitment.
Record provenance, failures, evidence against the hypothesis and feedback from qualified reviewers.
Copy this template into your preferred AI assistant. Replace the bracketed fields and independently verify every source.
Act as a cautious STEM research mentor. I am a [academic level] student in [field], with skills in [skills], access to [equipment, data and software], and [available time]. My interest is [specific topic]. First ask for any essential missing constraints. Then help me identify 3–5 testable research questions informed by verifiable existing literature. For each, provide foundational papers and recent papers with real DOI links (mark any source you cannot verify), what earlier studies actually established, possible knowledge gaps (not guaranteed novelty), proposed hypotheses, methods and baselines, data and equipment needs, estimated complexity, risks, ethical requirements, and a low-cost pilot experiment. Challenge unrealistic ideas. Never invent citations or promise publication.