About
Daniel explores the principles and practices that help academic communities evaluate the credibility, accuracy, and reliability of research.
His areas of interest include research verification, evidence evaluation, scholarly sources, citation reliability, research transparency, and the growing influence of artificial intelligence on academic information.
Through practical research scenarios, Daniel examines how researchers and educators can identify questionable references, evaluate the strength of supporting evidence, recognize inconsistencies, and distinguish credible scholarly findings from misleading or unsupported claims.
He also explores the limitations of automated verification tools and emphasizes the importance of human judgment, contextual understanding, and careful investigation when evaluating research.
His sessions encourage participants to develop stronger verification habits and approach scholarly information with curiosity, critical thinking, and a commitment to evidence-based decision-making.
Sessions
Verification Skills Every Researcher Now Needs
As AI makes information easier to generate and distribute, verifying scholarly claims becomes increasingly important. This session explores practical approaches to evaluating sources, checking citations, validating evidence, and maintaining confidence in research findings.
The Source Exists—but Does It Support the Claim?
Finding a legitimate source is only the first step in evaluating evidence. This session explores how researchers can determine whether cited material genuinely supports an argument, recognize misleading interpretations, and strengthen the connection between claims and evidence.
Can We Still Trust the Citation?
Citations can make academic work appear credible, but a reference does not automatically guarantee accuracy or reliability. This session explores the challenges of fabricated, inaccurate, and misleading citations, particularly in AI-assisted research, and what researchers should verify before trusting a reference.