Student-led exploration of generative AI applications across diverse domains
Format: 8-minute presentations with Q&A sessions
Themes ranged from financial prediction to creative writing, education, and social impact
Key Learning: Practical insights into AI capabilities, limitations, and ethical considerations
Students explored AI applications across 10 different domains, from finance and education to creative writing and emergency support
Every presentation highlighted the critical need for human oversight, expert validation, and ethical considerations in AI implementations
Students discovered significant limitations including cultural biases, overconfidence, and the challenge of assessing qualitative aspects
NVIDIA Stock Forecasting with GPT-4
Methodology: Prompt engineering with different question formulations
Conclusion: AI useful for research and analysis but not reliable for specific predictions
Transforming Banking Operations
Methodology: Real-world banking experience and case studies
Conclusion: AI as powerful tool requiring multi-layer verification systems
Taylor Series Learning with ChatGPT vs. DataWise
Methodology: Comparative analysis of general vs. specialized AI tutors
Conclusion: AI effective as study partner but not ready as full tutor
Language Enhancement and Idea Generation
Methodology: Personal writing experience with Grammarly and ChatGPT
Conclusion: AI as writing partner, not substitute, maintaining authenticity and authorship
Crisis Support in Rural India
Methodology: Scenario testing with real-world crisis prompts
Conclusion: Hybrid model needed: AI as first response, connected to human networks
Automated Review Processing with Prompt Engineering
Methodology: Custom prompt templates with structured outputs
Conclusion: Effective for business efficiency but needs human oversight for quality
Recipe Creation and Personalization
Methodology: Personal cooking experiments with AI-generated recipes
Conclusion: Useful cooking partner but requires human judgment and cultural awareness
Cognitive Effects and Educational Solutions
Methodology: Research synthesis and school-based observations
Conclusion: AI can enhance learning if designed to promote thinking rather than provide answers
Recruitment Automation and Bias Detection
Methodology: Testing ChatGPT and Claude with real and AI-generated resumes
Conclusion: Useful for initial screening but requires human oversight to ensure fairness
Custom GPTs for Educational Framework Creation
Methodology: Custom GPT development with pedagogical frameworks
Conclusion: Effective tool for learning designers when combined with expert knowledge
Multiple presentations demonstrated how prompt design significantly affects AI output quality
Students identified various forms of bias across different applications
AI systems consistently showed high confidence in potentially incorrect outputs
Most effective implementations involved humans in expert or oversight roles
Several students compared AI tools (Ethan: ChatGPT vs. DataWise, Larry: ChatGPT vs. Claude)
Students used actual scenarios rather than hypothetical examples
Presentations assessed accuracy, bias, usability, and ethical implications
Multiple students refined their prompts based on initial results
"It seems like these chatbots can do pretty sophisticated mathematics, but they fail miserably on very simple things... Why is there some data, is there like a lot more data out there for the sophisticated problems than the simpler ones?"
"Where is the fine line between creativity and hallucination? Hallucination is something that does not exist, created by the machine, and creativity is, by definition, you want to create something that others have not thought about."
"AI technology encourages all of us to think more creatively about how to use it. We may end up saying the human system is doing the best. That actually is not a bad outcome. It's a confirmation."
"We're always looking for something unique, extraordinary. But I cannot tell you what is unique, because if I can tell you, that's not unique. How do you recognize the pattern, which is you don't have a pattern?"
This session marked the midpoint of the course. The first half focused on "How can we use GAI to solve problems?" while identifying problems generated by GAI itself.
The second half will focus on "How do we solve problems posed by GAI?" - addressing data ethics, bias, transparency, and the complex challenges revealed through these student investigations.
Professor Andrew Lo from MIT will discuss "Can you really use ChatGPT for investment?" - continuing the practical exploration with expert academic perspective.