HSUHK | CCCO

COM1012 How to Master GenAI

Common Core Curriculum Office (CCCO) > COM1012 How to Master GenAI
Department
Computer Science
Cluster
3 (Science and Technology), C (Creativity, Technoscience, and Digital Competence)
Pedagogical Method
Computer Software Application

Prerequisite

GEN1000 for students admitted to Year 1 before AY2025/26, Year 2 before AY2026/27, or Year 3 before AY2027/28, except with the permission of the Module Coordinator

Exclusion

N/A

Module Description

This module provides a comprehensive introduction to Generative AI tools and their practical applications in academic and professional settings. Students will learn to effectively utilize various Generative AI platforms for content creation, data analysis, and problem-solving through hands-on projects and real-world case studies. The course emphasizes practical skills development, enabling students to integrate Generative AI tools into their academic work and future careers while understanding both the capabilities and limitations of current AI technologies.

Module Intended Learning Outcomes (MILO)

Upon completion of this module, students should be able to:

  1. Understand the fundamental principles and mechanisms behind Generative AI, including its capabilities and limitations.
  2. Master prompt engineering techniques and effectively interact with various AI platforms for content creation and task automation.
  3. Apply Generative AI tools to create high-quality content, including text, visuals, and multimedia, for professional and academic purposes.
  4. Critically evaluate the outputs of Generative AI, addressing issues of accuracy, bias, and ethical considerations.

Module Content

1. Introduction to Generative AI

1.1. Overview of AI development
1.2. Types of Generative AI applications
1.3. Popular Generative AI tools and platforms

2. Principles behind Generative AI

2.1. Basic concepts of neural networks
2.2. Large Language models
2.3. Training and fine-tuning processes

3. Prompt Engineering

3.1. Fundamentals of prompt design
3.2. Advanced prompt strategies (e.g. Few-shot, Chain of Thought (CoT) prompting)

4. Generative AI for Content Creation

4.1. Text and script generation
4.2. Visual content creation

5. Generative AI for Task Automation

5.1. Data analysis and visualization
5.2. Routine task automation

6. Generative AI for Professional Development

6.1. Personalized professional content
6.2. Interactive learning scenarios

7. Generative AI Ethics and Guidelines

7.1. Intellectual property considerations
7.2. Bias and fairness in Generative AI outputs
7.3. Privacy and data security
7.4. Responsible Generative AI usage and social impact

Assessment Methods

  1. Class participation (20%)
  2. Assignment (40%)
  3. Group project: Report, Presentation (40%)