Weekly Structure
📖 Pre-Class Reading materials and videos to prepare for discussion
💬 In-Class Interactive discussions, activities, and collaborative learning
📝 Post-Class Deeper reading and practical assignments to apply concepts
What You'll Be Able to Do
By the end of the twelve weeks, you should be able to:
- ▸Read AI claims calibratedly — take any claim about what AI can do for research and place it honestly as real, overclaimed, or aspirational, and check it against primary sources rather than press releases. This is the habit the whole course is built around.
- ▸Use AI across the research lifecycle — literature search, writing and ideation, data analysis and code, and multimodal material — while knowing where each use is genuinely strong and where it quietly fails.
- ▸Verify AI output systematically — catch hallucinated citations, silent errors in an analysis, and confident-but-wrong reasoning, and build the verification into your normal workflow.
- ▸Work ethically and transparently — apply an explicit ethical framework to your own AI use and disclose that use to the standard journals and funders now expect.
- ▸Reason about cost and access — account for the material and environmental footprint of AI, and for the global inequities in who can actually use it.
- ▸Make and defend a judgement — decide where AI does and does not belong in your research, and articulate that position clearly enough to defend it (the Week 12 capstone).
No single week delivers all of these — they accumulate. If you remember one thing, make it the first: calibrated reading is the through-line that ties the other five together.
12-Week Journey
Week 1
Foundations of Generative AI
- Historical development and recent breakthroughs
- Distinctions between different AI paradigms
- Introduction to generative AI architectures (transformers, diffusion models)
Week 2
Technical Underpinnings of Modern AI Systems
- How large language models work
- Training, fine-tuning, and inference processes
- Computational resources and infrastructure requirements
Week 3
Environmental Implications of AI
- Energy consumption of large model training and deployment
- Carbon footprint calculations
- Sustainable AI practices and green computing
Week 4
Ethical Frameworks for AI in Research
- Transparency and attribution
- Privacy considerations
- Bias, fairness, and representation
- Academic integrity when using AI tools
Week 5
AI for Research Ideation
- Using AI for hypothesis generation
- Exploring research questions and new perspectives
- Techniques for creative prompting and idea development
Week 6
AI-Assisted Literature Reviews
- Strategies for effective literature searching
- Summarization and synthesis of research papers
- Citation management and tracking
Week 7
Data Exploration and Analysis with AI
- AI tools for data preprocessing and cleaning
- Pattern recognition and anomaly detection
- Limitations of AI in data analysis
Week 8
Advanced Research Capabilities
- Knowledge retrieval techniques
- Implementing deep research functionality
- Context windows and information retrieval limitations
Week 9
AI as a Scientific Writing Assistant
- Drafting and editing with AI
- Technical writing enhancement
- Maintaining voice and academic standards
Week 10
Limitations and Pitfalls
- Hallucinations and factual reliability
- Domain-specific challenges in scientific disciplines
- Critical evaluation of AI-generated content
Week 11
Future Trends in AI for Research
- Emerging models and capabilities
- Multimodal AI in scientific contexts
- The evolving regulatory landscape
Week 12
Integrative Workshop
- Students present AI-enhanced research projects
- Peer review and feedback
- Reflection on course learnings and applications