Expert Session

How to Verify AI-Generated Research Answers: Evidence, Sources, and Trust

Speaker

Dr. Sasha Nikolic

Quick answer

When SciSpace generates a research answer, verify it by separating extracted evidence, synthesis, inference, recommendation, and unknowns. Open the supporting sources, confirm that each exists and supports the claim, check methods and numbers, compare contradictory evidence, and label uncertainty before using the answer in research or decisions.

About this session

Dr. Sasha Nikolic examines the societal and educational implications of generative AI while demonstrating the practical application of the SciSpace research tool. The session evaluates academic integrity and ethical conduct through specific student perspectives, providing a framework for verifying AI-generated research outputs, evidence, and synthesized information [L1, L2, L32-L40].

Trust in AI-assisted research should come from an inspectable process, not from fluent language or a brand name. This session develops a practical model for evaluating whether an output is grounded in appropriate evidence, whether the evidence is strong enough, and whether uncertainty and human review are visible.

What this tutorial covers

Distinguish source-grounded findings from AI-generated interpretation or suggestion.
Evaluate relevance, evidence quality, recency, and methodological fit rather than counting citations.
Corroborate consequential claims and inspect disagreement instead of hiding it.
Create a verification trail showing what was checked, by whom, and against which source.

Key concepts explained

Provenance: The user should be able to locate the source behind a statement.
Evidence quality: Peer review, study design, sample, bias, and applicability influence trust.
Calibration: Confidence should match the strength and consistency of evidence.
Auditability: Prompts, sources, revisions, and validation decisions make the workflow reviewable.

How to try it in SciSpace

1
Label each output component as extracted evidence, synthesis, inference, recommendation, or unknown.
Break the output into extracted evidence, synthesis, inference, recommendation, and unknown. This prevents a fluent answer from hiding which statements are directly supported.
2
Open the supporting sources and verify the most important claims and numbers.
Open the cited sources and verify the most consequential claims, numbers, methods, and quotations. Record the passage or table that supports each one.
3
Assess the quality and relevance of the evidence, including conflicting studies.
Assess relevance and quality, including study design, population, recency, bias, and conflicting evidence. A real citation is not automatically a strong or applicable source.
4
Record uncertainty, missing data, and alternative explanations.
Record uncertainty, missing data, alternative explanations, and the difference between evidence and interpretation. Ask the system to revise overconfident wording.
5
Approve, revise, or reject the output and retain the verification record.
Approve, revise, or reject each major conclusion and retain the source links and review notes. The final answer should be auditable by someone who did not run the original prompt.

Worked example

An AI answer claims that prolonged sitting increases mortality by a specific percentage. In SciSpace, the researcher opens the cited source, confirms whether it is a cohort study or meta-analysis, checks the exposure definition and confidence interval, and determines whether the percentage is relative or absolute. A second source uses a different sitting threshold. The final answer is rewritten to reflect the evidence range and uncertainty rather than repeating one precise but misleading number.

Practice prompt

Audit this research output for trustworthiness. For each major claim, identify the source, evidence type, relevance, study quality, corroboration, uncertainty, and whether the statement is extraction or inference. Flag anything that cannot be verified.

Key takeaway

Trustworthy AI research is evidence-traceable, uncertainty-aware, and explicitly verified by a responsible researcher.