Research Integrity and Responsible AI

Explain how honesty, accuracy, transparency, accountability, and respect underpin research integrity.

Select record-keeping practices that preserve data provenance, version history, research decisions, security, and traceability throughout a project.

Differentiate data fabrication, data falsification, and selective reporting, and explain why selective reporting can mislead even when data are not invented or altered.

Evaluate a proposed use of an AI tool in research for policy compliance, confidentiality, reliability, and human accountability, then specify the verification and disclosure required.