AILT9021 AI and the Future Economy | Undergraduate - FBE

(3-Credit Microcredential Artificial Intelligence Literacy Course)

Course Instructor(s)

Professor Michael CHAU

Dr. Vera YUEN

Course Description

This course aims to equip students with foundational literacy on the relationship between AI and the economy. Students will examine how AI technologies are reshaping industries, labor markets, and economic policies. Topics include the economic fundamentals of AI, its role in automation and productivity, implications for income inequality, and the potential for AI-driven innovation to foster sustainable growth. Through interactive lectures, case studies, tutorials, and discussions, students will understand the opportunities and challenges AI presents to modern economies.

Prerequisite

AILT1001 Artificial Intelligence Literacy I

Course Objectives

1. Provide students with the opportunity to investigate how AI technologies are shaping the future economy.
2. Help students understand the impact of AI on the labor markets and their future.
3. Develop critical thinking to evaluate ethical, societal, and regulatory implications of AI.

Course Learning Outcomes

1. Demonstrate an understanding of how AI technologies are changing industries and the economy through automation and productivity enhancement.
2. Analyze the impact of AI on the labor markets.
3. Analyze AI’s role in societal issues such as income inequality and sustainability.
4. Critically assess the ethical, societal, and regulatory implications of AI in different economies, considering global impacts.

Course Teaching and Learning Activities

Activities	No. of Study Hours
In-class and Online Lectures	18
Tutorials	8
Group Project	16
Self-study and Readings	18
Total	60

Course Assessment Methods

Assessment Method	Type of Assessment	Weighting (%)	Aligned Course Learning Outcome(s)
Participation	Participation in case studies and discussions.	20%	CLOs 1,2,3,4
Group Project	Students need to write a proposal and a final report and make a verbal presentation.	30%	CLOs 1,2,3,4
Tutorials	Four two-hour sessions of AI tools and case studies.	20%	CLOs 1,2,3,4
Final Test	A test to assess students’ achievement of the learning objectives.	30%	CLOs 1,2,3,4
Total		100%

Course Grade Descriptor

Grade	Description
Distinction	Participation: Actively contributes to discussions with insightful and original ideas. Demonstrates excellent preparation and a deep understanding of the material. Completes all online activities with accuracy and demonstrates thorough understanding. Responses are thoughtful and reflect critical thinking. Group Project: Project is highly innovative, original, and demonstrates creative thinking. Thorough research with critical analysis. Effectively integrates relevant theories and concepts. Strong alignment with course content and learning outcomes. Ideas are communicated clearly and professionally with excellent organization and depth. Tutorials: Attends all tutorial sessions. Actively contributes to discussions with insightful and original ideas. Demonstrates excellent preparation and a deep understanding of the material. Final Test: Demonstrates exceptional understanding of AI concepts. Almost all answers are correct, showing depth of knowledge.
Pass	Participation: Regularly contributes to discussions with relevant points. Shows good preparation and understanding of the material. Completes most online activities satisfactorily. Responses are generally accurate but may lack depth. Group Project: Project meets basic requirements with some original elements. Adequate research with commendable analysis. Basic integration of theories and concepts. Generally aligns with course content. Ideas are clear but may lack sophistication. Good organization. Tutorials: Regularly contributes to discussions with relevant points. Shows good preparation and understanding of the material. Final Test: Demonstrates solid understanding of concepts with minor errors. Majority of answers are correct.
Fail	Participation: Rarely participates or contributions lack relevance. Shows little to no preparation. Fails to complete a significant portion of online activities. Responses are inaccurate or incomplete. Group Project: Lacks originality; minimal effort evident. Inadequate research; superficial analysis. Poor alignment with course content. Ideas are unclear; poor organization and depth. Tutorials: Rarely participates or contributions lack relevance. Shows little to no preparation. Final Test: Significant gaps in understanding; misconceptions evident. Majority of answers are incorrect.

Course Content and Topics

Session 1: AI and the Future Economy

Session 1 will provide an introduction to the course. It will explore how artificial intelligence is transforming industries, redefining labor markets, and driving unprecedented economic growth. As AI technologies advance, they enable automation of complex tasks, enhance decision-making processes, and foster innovation across sectors such as healthcare, finance, manufacturing, and logistics. This shift promises increased productivity and new business opportunities, but also raises concerns about job displacement, income inequality, and ethical considerations. The first session will give an overview of these issues and stimulate students’ intellectual inquiry on these topics.

Session 2: AI’s Impact on Economic Productivity and Growth I

This session will delve into how AI-driven innovations are reshaping industries, enhancing efficiency, and creating new opportunities for businesses and economies worldwide. We will analyze current trends and technological advancements, and study how AI can be harnessed to boost productivity and stimulate innovation, resulting in economic growth.

Session 3: AI’s Impact on Economic Productivity and Growth II

Continuing from the previous session, the session will look at specific case studies showing AI’s impact on economic productivity and growth. Example AI technologies, companies, and economic bodies will be discussed.

Session 4: AI’s Impact on the Labor Market I

This session will examine how the rapid advancement of artificial intelligence technologies is reshaping employment patterns, workforce dynamics, and job roles across various industries. We will explore the potential for AI to automate routine and repetitive tasks, leading to increased efficiency and productivity, while also raising concerns about job displacement and shifting skill requirements. Students will analyze case studies of industries heavily affected by AI, such as manufacturing, customer service, and data analysis, to understand both the opportunities and challenges presented by automation.

Session 5: AI’s Impact on the Labor Market II

Continuing from the previous session, we explore the broader socio-economic implications of AI-driven changes in employment, including the need for reskilling and lifelong learning, the emergence of new job categories, and the importance of policies that promote equitable growth. Students will engage with current research, debate ethical considerations, and develop strategies for navigating a labor market in transition. Students will have an understanding of how AI is transforming work environments and the measures needed to ensure a resilient and inclusive future workforce.

Session 6: AI and Sustainability

This session explores the potential of artificial intelligence in addressing some of the most pressing environmental and social challenges, such as how AI technologies can enhance resource management, optimize renewable energy systems, and support sustainable development goals.

Session 7: Policy, Regulation, and Ethical Considerations

In this sessions we still investigate time governance of AI in economic systems. Topics such as intellectual property, data ownership, and privacy will be discussed. Students will also be introduced to the challenges on addressing bias, inequality, and access.

Session 8: Global Perspectives and Inequality

This session will study AI's role in emerging versus developed economies. Is AI bridging or widening economic gaps? We will examine how AI impacts different regions and communities worldwide, highlighting both its potential to drive development and its role in exacerbating existing inequalities. It explores issues such as the digital divide, unequal access to AI technologies, and the ethical considerations of deploying AI in diverse socio-economic contexts.

Session 9: Future Scenarios

This session will look at some potential future trajectories of AI in the economy. What are the best case and worst case scenarios? We will look at how strategic planning and proactive governance can help steer the future toward the most beneficial outcomes while mitigating risks.

Session 10: Getting Prepared

This session focuses on the essential steps individuals, organizations, and policymakers can take to adapt to the rapidly evolving landscape of artificial intelligence. It explores the importance for individuals to acquire knowledge of AI and related tools and be aware of the related social and ethical issues in order to navigate AI's opportunities and challenges responsibly. The session also highlights the need for proactive policy frameworks, ethical guidelines, and collaborative efforts to ensure a fair, inclusive, and innovative transition into an AI-driven future, empowering society to harness AI's potential while mitigating risks.

Sessions 11 and 12: Project Presentation

Students will present their project to the instructors and peers and receive feedback.

Session 13: Final Test

Tutorials

Tutorial 1: Analyzing data related to the impact of AI on productivity

Tutorial 2: Analyzing data related to the impact of AI on labor markets

Tutorial 3: Case studies on AI-driven economical innovation

Tutorial 4: Case studies on policy regulations and ethical issues

Required / Recommended Readings and Online Materials

Recommended Readings will be available on Moodle.

Course Policy

An orderly learning environment is extremely important for this course. Disruptive behaviors are inconsiderate to other students as well as to the instructor and are absolutely unacceptable. Talking during lectures, arriving to class late, and any other disruptions of mobile devices are not allowed; students who are responsible for any of these actions will be subject to academic penalty and will be asked to leave the classroom.

Any dishonesty—such as cheating, false representation, plagiarism, etc.—that comes to my attention will result in an F in the course. Academic dishonesty includes cheating, plagiarism, unauthorized collaboration, falsifying academic records, and any act designed to avoid participating honestly in the learning process. Scholastic dishonesty also includes, but is not limited to, providing false or misleading information to receive a postponement or an extension on an exam or other assignment. The responsibilities of both students and faculty with regard to scholastic dishonesty are described in detail in the Disciplinary Committee Regulations. By teaching this course, I have agreed to observe all of the faculty responsibilities described in that document. By enrolling in this class, you have agreed to observe all of the student responsibilities described in that document. If the application of that policy statement to this class and its assignments is unclear in any way, it is your responsibility to ask me for clarification.

Other Additional Course Information

Additional information will be available on Moodle.