By Micah Asamba, Director, Keynea Research & Analytics Ltd
AI-assisted research is most useful when its outputs can be checked. Beyond summarising or rephrasing, researchers can build structured workflows for reading sources, reviewing data, editing manuscripts and documenting decisions. The practical question is how to use assistance while retaining authorship, methodological judgement and a clear evidence trail.
Start with a bounded research task
Define the question, the material available and the output you need before choosing a tool. A request to compare the limitations reported in three supplied papers is easier to verify than an open request to write a literature review. Record which documents were supplied and keep the original sources.
Use source-grounded reading
Ask for page references, quotations and a clear distinction between what a source reports and what the tool infers. Open each cited source and check the relevant passage yourself. A citation attached to a sentence is not proof that the source supports it. For systematic reviews, retain a reproducible search strategy, eligibility criteria and documented screening decisions.
Make data analysis reproducible
Before sharing a dataset, confirm that its use is permitted and remove information that the task does not require. Define variables, missing-value rules and the intended analysis. Ask for the analytical code and record transformations, assumptions and exclusions. Check outputs against the data and review whether the method fits the study design. A convincing chart does not establish that the analysis is valid.
Edit without losing scientific meaning
Use assistance to identify unclear sentences, inconsistent terminology and gaps in the argument. Review every proposed change against the original meaning. Keep a version of the author’s draft, check references directly, and distinguish language editing from changes to findings or interpretation. The researcher remains responsible for the final text.
Treat transcripts and qualitative coding as material for review
Check transcripts against recordings where consent and access permit. Review suggested codes against the source passages and research question. Document coding decisions, retain context and examine examples that challenge the developing interpretation. Automated suggestions should not replace the researcher’s engagement with participants’ accounts.
Choose tools through a small, documented test
Capabilities, plan limits and data-handling terms change. Check the provider’s current documentation and your institution’s requirements before adopting a tool. Test it on a small, non-sensitive sample whose answers you can verify. Compare source accuracy, reproducibility, privacy requirements, cost and ease of review. Do not assume that one product is best for every stage.
Keep human responsibility explicit
Follow institutional, journal and funder rules on permitted assistance and disclosure. Maintain a record of material uses of AI, verify quotations and references, and explain the methods actually used. Never upload identifiable participant information or confidential project files without the necessary permission and an appropriate data-handling arrangement.
For help establishing a defensible workflow, explore Keynea’s evidence synthesis, data analytics and publication support, or discuss your project.