Master Responsible AI for Research and Development: Scientific Writing, Literature Review, Data Analysis and Ethical AI Use in 4 weeks through hands-on, project-based online training with DSTC.
Data Science & Analytics
Module-by-module breakdown of Responsible AI for Research and Development: Scientific Writing, Literature Review, Data Analysis and Ethical AI Use, from foundations to a certified capstone project.
Landscape
• Language models as fluent predictors, not sources of verified knowledge
• Hallucinated citations as the characteristic and most damaging failure
• General assistants against retrieval-grounded research tools
Literature
• AI-assisted screening in a systematic review and where PRISMA still binds
• Verifying every retrieved reference against the primary source
• The narrowing effect of letting a model choose what you read
Analysis
• Generated analysis code that runs but is silently wrong
• Confidentiality: what must never be pasted into a third-party service
• Reproducibility when part of the workflow is a non-deterministic model
Writing
• ICMJE and COPE positions: AI cannot be an author and use must be disclosed
• Publisher and funder policies and how sharply they differ
• AI-detection tools and their false-positive rates against non-native writers
Governance
• Institutional policy, research integrity and what counts as misconduct
• Attribution, plagiarism and derivative text in generated output
• A personal use policy recorded so decisions can be defended later
e-Certificate and e-Marksheet issued on successful completion.