Practical Applications of Generative AI

Course Description

Generative AI has moved in less than three years from research curiosity to economic and cultural force. In Korea — home to Samsung, Naver, Kakao, LG AI Research, and a thriving startup ecosystem — generative AI is being deployed at industrial scale across manufacturing, healthcare, finance, media, education, and government. This course gives students a working understanding of generative AI: how it works, where it comes from, what it can and cannot do, and how to use it productively, responsibly, and critically across their own academic and professional work.

The course is hands-on. Students will engage directly with leading generative AI platforms — large language models, image generators, code assistants — building practical fluency through workshops, prompts, and projects. They will also engage with the conceptual frameworks needed to think clearly about generative AI: what it means for work, education, creativity, intellectual property, and the politics of technology. Korea's distinctive position — as both a major AI producer and a society debating AI's social impact — provides the case material throughout.

Aligned with the UN Sustainable Development Goals

Generative AI both accelerates and challenges progress on the SDGs. This course engages with SDG 4 (Quality Education), SDG 8 (Decent Work), SDG 9 (Industry, Innovation and Infrastructure), SDG 10 (Reduced Inequalities), and SDG 16 (Peace, Justice and Strong Institutions) — examining AI's positive applications and the governance challenges it surfaces.

Key Topics

Learning Objectives

By the end of this course, students will be able to:

1. Explain in plain language how generative AI systems are built, trained, and deployed — and identify where they are and aren't reliable.

   Assessment: Assessment: Short individual explainer (1,000 words) written for a non-technical audience.

2. Demonstrate practical prompt-engineering fluency across at least three generative AI platforms (text, image, code), producing higher-quality output than untrained users.

   Assessment: Assessment: Portfolio of prompted outputs with iteration documentation.

3. Design a generative AI workflow that solves a real problem in a specified domain — academic research, marketing, customer service, journalism, or another.

   Assessment: Assessment: Team project: build and document a working AI workflow.

4. Critically evaluate generative AI deployments — identifying potential harms, ethical risks, governance gaps, and mitigations.

   Assessment: Assessment: Case-study analysis of a real generative AI deployment in Korea or globally.

5. Articulate a personal point of view on what generative AI means for the student's own discipline, intended career, and intellectual life.

   Assessment: Assessment: Final reflection essay (1,500 words).

Course Format and Assessment Methods

Total grade is composed of the following weighted components:

Course Outline

The course is organized into the following sessions, which may be combined or expanded depending on summer vs. semester format.

1. What Generative AI Actually Is
   Demystifying generative AI: tokens, training, transformers — at the level of intuition, not mathematics.
2. A Short History of Generative AI
   From early neural networks through GANs, GPT, multimodal models, and the agentic frontier.
3. The Generative AI Toolkit
   Hands-on workshops with leading platforms: ChatGPT/Claude/Gemini, Midjourney/DALL·E, GitHub Copilot, and Korean platforms (HyperCLOVA X, Solar).
4. Prompt Engineering as a Craft
   Practical prompting techniques: few-shot, chain-of-thought, role-playing, system prompts, and the structural patterns that produce reliable output.
5. Retrieval-Augmented Generation
   How and why to ground AI outputs in custom knowledge — and the workflow design implications.
6. AI Agents and Agentic Workflows
   From single prompts to multi-step agentic systems — and the new affordances and risks they introduce.
7. AI in Korean Industry
   Case studies: Samsung's on-device AI, Naver's HyperCLOVA, LG AI Research, Kakao's enterprise deployments.
8. AI in Education and Research
   Generative AI in higher education — academic integrity, learning, and the new pedagogical questions.
9. AI Ethics: The Hard Problems
   Bias, hallucination, deepfakes, copyright, labor displacement, and the politics of training data.
10. AI Safety and Alignment
    What "AI safety" means at the frontier — and why it matters for everyone, not just specialists.
11. AI Governance
    EU AI Act, the Korean AI Basic Act, US executive orders, and the emerging global governance landscape.
12. Building With AI: A Workshop Day
    Team workflow projects: hands-on build day with instructor support.
13. The Future of Work
    What generative AI means for the work students are preparing to enter — across disciplines, with a critical eye.
14. Capstone Presentations
    Team workflow projects presented to the class.

Field Visits and Guest Speakers

Seoul is the city as classroom. Course-related field components vary by term and availability, but examples include:

• Visit to Naver's headquarters or LG AI Research center — meet practicing AI researchers and product teams.
• Tour of a Korean AI startup in the Seongsu or Pangyo tech districts.
• Workshop at Korea's National Institute for Lifelong Education or a leading EdTech firm on AI in education.
• Guest lecture from a Korean AI policy specialist or KAIST researcher.
• Visit to Samsung Innovation Museum or KICTI (Korea Information Society Development Institute).

Readings & Resources

Selected readings and resources for this course. Full syllabus and reading list provided at enrollment.

Books

Brynjolfsson, Erik, and Andrew McAfee. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton, 2014.

Crawford, Kate. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press, 2021.

Marcus, Gary, and Ernest Davis. Rebooting AI: Building Artificial Intelligence We Can Trust. New York: Pantheon, 2019.

Mollick, Ethan. Co-Intelligence: Living and Working with AI. New York: Portfolio, 2024.

O'Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown, 2016.

Suleyman, Mustafa. The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma. New York: Crown, 2023.

Films and Recordings

Andreessen, Marc, and Ben Horowitz. 2024. The AI Stack. Podcast series, a16z.

Lex Fridman Podcast. 2024. Conversations with Demis Hassabis, Yann LeCun, and Ilya Sutskever.

The AI Podcast (NVIDIA). 2024. Generative AI in Industry. Selected episodes.

Articles and Reports

Bommasani, Rishi, et al. 2021. "On the Opportunities and Risks of Foundation Models." Stanford CRFM.

Brynjolfsson, Erik, Danielle Li, and Lindsey R. Raymond. 2023. "Generative AI at Work." NBER Working Paper.

Korean Ministry of Science and ICT. 2024. National AI Strategy 2024–2027.

McKinsey Global Institute. 2024. The Economic Potential of Generative AI.

OECD. 2024. AI, Data Governance and Privacy: Synergies and Areas of International Cooperation.

Wei, Jason, et al. 2022. "Chain of Thought Prompting Elicits Reasoning in Large Language Models." NeurIPS.