Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This 3‑day live, hands‑on program equips researchers, PhD scholars, academicians, students and R&D professionals with the skills to use AI tools responsibly throughout the research lifecycle. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This 3‑day live, hands‑on program equips researchers, PhD scholars, academicians, students and R&D professionals with the skills to use AI tools responsibly throughout the research lifecycle.
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
What These Tools Actually Do
• 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
Search and Synthesis
• 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
Code and Data Work
• 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
Drafting and Disclosure
• 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
Using AI Defensibly
• 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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | ChatGPT |
| Covered Tool / Platform | Google Gemini |
| Covered Tool / Platform | Perplexity |
| Covered Tool / Platform | Elicit |
| Covered Tool / Platform | Scite |
| Covered Tool / Platform | Consensus |
| Covered Tool / Platform | Research Rabbit |
| Covered Tool / Platform | Connected Papers |
| Covered Tool / Platform | Semantic Scholar |
| Covered Tool / Platform | Google Scholar |
Frequently Asked Questions
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