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5.15. Applied AI (Mandatory)

5.15.1. Justification ↑ Back to top

This course provides a practical introduction to Artificial Intelligence (AI) for students from all scientific and engineering disciplines. Focused on developing AI literacy and practical skills, it covers fundamental concepts, modern AI tools (including Western and Chinese platforms), and responsible usage. Students will learn to effectively interact with diverse AI systems, write quality prompts, and apply AI solutions to problems across various domains while understanding ethical implications and cultural contexts of AI deployment.

5.15.2. Generales Goals ↑ Back to top

1. Develop comprehensive AI literacy by understanding fundamental concepts, capabilities, and limitations of modern AI systems across different platforms and cultures.
2. Master effective prompt engineering techniques and interaction patterns with various AI tools (Western: ChatGPT, Claude, Gemini; Chinese: DeepSeek, Kimi, ERNIE).
3. Apply diverse AI tools to solve practical problems in scientific, engineering, and academic contexts while maintaining critical evaluation of outputs across platforms.
4. Understand ethical considerations, biases, cultural contexts, and responsible usage of AI technologies in global professional and academic settings.
5. Develop workflows that integrate multiple AI tools to enhance productivity and problem-solving capabilities while understanding regional strengths and specializations.

5.15.3. Contribution to Outcomes ↑ Back to top

AG-C11) Use of Tools: Applies modern computing tools in problem solving. (Usage)

AG-C01) The Professional and the World: Analyzes and evaluates the impact of solutions to complex computing problems on the sustainable development of society. (Familiarity)

5.15.4. Content ↑ Back to top

5.15.4.1. AI Fundamentals and Global Landscape (16 hours) [Skills AG-C01,AG-C11] ↑ Back to top

Bibliography: (Ng, 2019; Mollick and Mollick, 2024; UNESCO, 2023; Institute, 2024)

Topics

1. What is AI? Definitions, history, and current global landscape.
2. Types of AI systems: chatbots, image generators, research assistants across regions.
3. Western AI ecosystems: OpenAI, Anthropic, Google, Microsoft.
4. Chinese AI ecosystems: DeepSeek, Kimi, ERNIE, Zhipu AI, Baidu.
5. AI capabilities and limitations: comparative analysis across platforms.
6. Digital literacy in the AI era: critical thinking about diverse AI outputs.

Learning Outcomes

1. Apply fundamental AI knowledge to identify system types and their capabilities [Usage].
2. Analyze global intelligent systems and their distinctive characteristics [Assessment].
3. Critically evaluate AI outputs across different cultural contexts [Assessment].

5.15.4.2. Effective AI Interaction and Cross-Platform Prompt Engineering (20 hours) [Skills AG-C01,AG-C11] ↑ Back to top

Bibliography: (White et al., 2023; Prompt Engineering Institute, 2024; Microsoft, 2024; DeepSeek, 2024)

Topics

1. Principles of effective prompt writing: clarity, context, constraints across platforms.
2. Prompt patterns: persona, template, chain-of-thought, few-shot for different AI systems.
3. Western tools: specific techniques for ChatGPT, Claude, Gemini, Copilot.
4. Chinese tools: specific features and best practices for DeepSeek, Kimi, ERNIE.
5. Cross-platform strategies: leveraging different AI strengths for complex tasks.
6. Iterative refinement: how to improve prompts based on outputs from diverse systems.
7. Practical sessions: comparative prompt writing workshops across platforms.

Learning Outcomes

1. Design effective prompt solutions for diverse AI systems [Assessment].
2. Develop cross-platform workflows that integrate different AI tools [Usage].
3. Implement iterative refinement strategies to optimize AI outputs [Usage].

5.15.4.3. AI Applications Across Disciplines and Platforms (16 hours) [Skills AG-C01,AG-C11] ↑ Back to top

Bibliography: (Mollick, 2023; Google, 2024; Cloud, 2024; OpenAI, 2024)

Topics

1. AI for research: literature review, data analysis using multiple AI assistants.
2. AI for writing: academic papers, reports with cross-platform verification.
3. AI for problem-solving: scientific calculations, engineering design with specialized tools.
4. AI for creativity: brainstorming, concept development across cultural contexts.
5. Platform-specific strengths: when to use Western vs. Chinese AI tools.
6. Discipline-specific workshops: tailored applications using diverse AI ecosystems.
7. Case studies: real-world applications in scientific research across regions.

Learning Outcomes

1. Apply AI fundamentals to solve discipline-specific problems [Usage].
2. Design solutions that integrate multiple intelligent systems for complex tasks [Assessment].
3. Develop practical applications using Western and Chinese AI tools [Usage].
4. Implement workflows that leverage specific strengths of each platform [Assessment].

5.15.4.4. Global AI Ethics and Responsible Usage (12 hours) [Skills AG-C01,AG-C11] ↑ Back to top

Bibliography: (UNESCO, 2023; Union, 2024; Bender et al., 2021; of Cyberspace Studies, 2024; Tencent AI Lab, 2023)

Topics

1. Understanding AI biases: Western and Eastern cultural perspectives.
2. Ethical frameworks: comparing EU AI Act, Chinese regulations, and global standards.
3. Academic integrity: proper citation and AI usage in coursework across platforms.
4. Privacy and data security: regional differences in AI data handling.
5. Cultural sensitivity: navigating AI outputs in global contexts.
6. Environmental impact: sustainability considerations of different AI systems.
7. Developing personal guidelines for ethical AI usage in international settings.
8. Case studies: ethical dilemmas in AI deployment across regions.

Learning Outcomes

1. Demonstrate professional responsibility in using AI technologies [Assessment].
2. Apply professional ethical principles in global AI contexts [Usage].
3. Develop ethical guidelines to mitigate biases in AI systems [Assessment].
4. Evaluate ethical implications of AI deployment across different cultures [Usage].

5.15.5. Bibliography ↑ Back to top

Ng, A. (2019). AI for everyone. Technical report, DeepLearning.AI. Continuously updated online course, available on Coursera.

Mollick, E. and Mollick, L. (2024). Co-Intelligence: Living and Working with AI. Penguin Random House. Practical guide to human-AI collaboration.

UNESCO (2023). AI and education: Guidance for policy-makers. Technical report, UNESCO. Latest guidance on AI literacy and ethical implementation in education.

Institute, T. R. (2024). Ai development in china: Current status and future trends. Technical report, Tencent Research Institute. Comprehensive analysis of Chinese AI ecosystem development.

White, J. et al. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. Peer-reviewed research on prompt engineering patterns.

Prompt Engineering Institute (2024). Prompt engineering guide. https://www.promptingguide.ai/. Recurso integral de ingeniería de prompts actualizado continuamente. Accedido: 15 de enero de 2024.

Microsoft (2024). Prompt crafting for ai systems. Official Microsoft prompt engineering guidance updated for 2024.

DeepSeek (2024). Deepseek model documentation and best practices. Official documentation for DeepSeek AI models and usage guidelines.

Mollick, E. (2023). Chatgpt and how ai disrupts industries. Harvard Business Review. Analysis of AI's practical impact across global sectors.

Google (2024). Introduction to responsible ai. Google's updated framework for responsible AI development and use.

Cloud, A. (2024). AI Ethics and best practices in chinese context. Chinese perspective on AI ethics and implementation guidelines.

OpenAI (2024). Best practices for prompt engineering. Updated official prompt engineering guidelines from OpenAI.

Union, E. (2024). AI Act: Regulatory framework. Technical report, European Commission. Comprehensive AI regulation framework effective 2024.

Bender, E. M., Gebru, T., et al. (2021). On the dangers of stochastic parrots: Can language models be too big? FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. Foundational paper on AI ethics with ongoing relevance.

of Cyberspace Studies, C. A. (2024). Ai governance and ethics in china. Technical report, Cyberspace Administration of China. Official Chinese perspective on AI governance and ethical standards.

Tencent AI Lab (2023). Responsible ai practices in chinese tech industry. Technical report, Tencent AI Lab. Industry perspective on AI ethics from leading Chinese tech company.