Reading Research Papers with AI

A research paper has several jobs: state a question, explain how evidence was collected, report results and interpret what those results may mean. The abstract is a useful entry point, but it compresses methods and limitations. AI can build a reading map or define unfamiliar terms, yet the reader should return to the actual article. Start by writing the question in one sentence and noting the study type. Who or what was studied, what was measured and what comparison was made? Read the methods before adopting the conclusion. A sample, exclusion rule or measurement choice may determine how far the result travels. Ask AI to point to the exact passage supporting its description of participants or procedure, then check the page. For quantitative work, inspect table headings, units, uncertainty intervals and whether a result was pre-specified or exploratory when the paper says so. For qualitative work, inspect the data sources, sampling and how interpretations were developed. Avoid treating a fluent summary as a substitute for methodological judgment. Compare results with discussion. Authors may report an association and later speculate about a mechanism; the evidence for those claims is not identical. A study with a narrow population does not establish a universal effect. Check caveats, missing data, conflicts and alternative explanations. If AI says a paper proves something, ask what result would disprove that stronger wording. When figures or equations carry the argument, view them directly because text extraction may omit labels or symbols. Make a short evidence note: question, design, sample, main result, limitation and the claim your assignment could responsibly make. Cite the actual paper and use any required access rules. Test your understanding by explaining the result to someone else with one qualification intact. AI is most useful when it makes the reading path less intimidating while preserving the evidence trail.