(3-Credit Microcredential Artificial Intelligence Literacy Course)
Course Instructor(s)
Professor Chao DING (First Semester)
TBA (Second Semester)
Course Description
This course aims to equip students with foundational AI literacy to navigate an AI-driven economy. Students will explore AI’s transformative role across business functions, including marketing, economics, finance, human resources, operations, sales, product development, consulting, and information systems. Through interactive lectures, case studies, and discussions, students will understand AI applications, tackle ethical and practical challenges, and devise strategic solutions for real-world business scenarios.
Prerequisite
AILT1001 Artificial Intelligence Literacy I
Course Objectives
1. Introduce core AI concepts and their relevance to diverse business functions.
2. Enable students to analyze AI-driven business opportunities and challenges.
3. Develop critical thinking to evaluate ethical, societal, and regulatory implications of AI in business.
4. Equip students with practical skills to apply user-friendly AI tools for business innovation.
5. Foster adaptability for future careers in an AI-integrated business landscape.
Course Learning Outcomes
1. Identify and explain key AI concepts and tools, applying them to business contexts.
2. Analyze AI’s impact on business functions, evaluating challenges and solutions.
3. Apply AI strategies to develop practical business solutions.
4. Critically assess the ethical, societal, and regulatory implications of AI adoption across business functions, considering global impacts.
Course Teaching and Learning Activities
Activities | No. of Study Hours |
In-class and Online Lectures | 14 |
Tutorials | 4 |
Case Studies and Discussions | 10 |
Group Project | 14 |
Self-study and Readings | 18 |
Total | 60 |
Course Assessment Methods
Assessment Method | Type of Assessment | Weighting (%) | Aligned Course Learning Outcome(s) |
|---|---|---|---|
Case Studies | Interaction in case studies and discussions during lectures. | 40% | CLOs 1,2,4 |
Group Project | Students need to write a proposal and a final report and make a verbal presentation. | 40% | CLOs 1,2,3,4 |
Tutorial Participation | Participation in four one-hour sessions of hands-on training with user-friendly AI tools tailored to business functions. | 20% | CLOs 1,3,4 |
Total | 100% |
| |
Grade Descriptor
Case Studies (40%)
This component focuses specifically on student interaction in case studies and discussions during lectures. It assesses engagement, quality of contributions, and relevance to AI applications in business functions.
Distinction:
• Contributes substantively in ≥90% of case studies/discussions.
• Contributions are consistently insightful, demonstrating deep understanding of AI applications and challenges.
• Consistently ties contributions to specific case studies, referencing business functions and AI contexts.
• Actively fosters collaborative discussion by responding to peers, building on their ideas, or encouraging participation.
Pass:
• Contributes in ≥2/3 of case studies/discussions.
• Contributions show basic understanding of AI applications and challenges.
• Contributions are relevant to case studies but may lack specificity or depth.
• Engages with peers’ ideas occasionally but focuses primarily on own contributions
Fail:
• Contributes in <1/3 of case studies/discussions.
• Contributions lack depth, relevance, or understanding.
• Contributions are rarely or never tied to case studies or AI applications.
• Rarely or never engages with peers’ ideas.
2. Group Project (40%)
The project requires students to apply AI strategies to propose practical business solutions, analyze challenges, and assess societal or regulatory implications. It includes a proposal, a group presentation and a final report.
Distinction:
• Proposes innovative, practical AI solutions tailored to the scenario.
• Solutions are feasible, specific, and address challenges.
• Demonstrates exceptional collaboration: all group members contribute visibly and equitably
• Delivery is polished, engaging, and professional. Visual aids are high-quality, visually appealing, and enhance content.
• Report is well-written, structured, and polished. Demonstrates deep analysis.
Pass:
• Proposes functional AI solutions for the scenario.
• Solutions are feasible but may not fully address challenges.
• Shows adequate collaboration: most group members contribute, but roles may be uneven.
• Delivery is clear but may lack polish. Visual aids are functional but basic.
• Report is clear and adequately structured. Shows basic analysis.
Fail:
• Solutions are impractical, vague, or absent.
• Little to no understanding of AI applications.
• Lacks collaboration: one or few members dominate, or some contribute minimally.
• Delivery is unclear, unprofessional, or disengaging. Visual aids are poor, confusing, or absent.
• Report is incomplete, poorly structured, or missing key sections. Lacks analysis or relevance.
3. Tutorial Participation (20%)
These tutorials focus on hands-on training with user-friendly AI tools tailored to business functions. Students engage in practical exercises and develop skills in applying these tools to business contexts.
Distinction:
• Attends all tutorial sessions.
• Consistently leads or enhances activities across all tutorials.
• Produces high-quality, innovative outputs demonstrating advanced tool mastery and business insight.
Pass:
• Attends at least 3 tutorial sessions.
• Contributes to discussions but may not lead.
• Produces functional outputs with basic proficiency in AI tools.
Fail:
• Attends less than 2 tutorial sessions.
• Minimal or no contribution to discussions.
• Fails to produce usable outputs or demonstrates little to no understanding of AI tools.
Course Content and Topics
Session 1: Technological Landscape
Overview of AI’s core concepts and their relevance to business functions
Session 2: AI in Marketing and Sales
Introduction to AI tools for targeted advertising, recommendation engines, and CRM analytics
Session 3: AI in Economics and Finance
Forecasting trends and fraud detection using AI
Session 4: AI in Human Resources
AI for talent management and recruitment
Session 5: AI in Operations and Supply Chain
AI for workflow optimization and logistics
Session 6: AI in Product Development
Generative AI for ideation and prototyping
Session 7: AI in Consulting and Information Systems
AI for strategy and automation
Session 8: AI in Accounting and Law
AI for financial reporting, auditing, and compliance
Session 9: The Future of Work
AI’s impact on jobs and upskilling
Session 10: Integrating AI Across Business
Holistic studies of AI implementation in business. Bias, fairness, and accountability in AI
Session 11: Project Presentation
Session 12: Project Presentation
Session 13: Course Wrap-up and Project Feedback
Tutorials
• Tutorial 1: Intro to AI Tools for Business
• Tutorial 2: AI Tools for Finance and Analytics
• Tutorial 3: AI Tools for Operations and Supply Chain
• Tutorial 4: AI Tools for Accounting and Entrepreneurship
Required / Recommended Readings and Online Materials
Recommended Readings:
1. Power and Prediction: The Disruptive Economics of Artificial Intelligence, by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, 2022, Ascent Audio. https://www.amazon.com/dp/B0BM4Z9TFG
2. AI and Innovation: How to Transform Your Business and Outpace the Competition with Generative AI, by Michael Lewrick and Omar Hatamleh, 2024, Wiley. https://www.amazon.com/AI-Innovation-Transform-Competition-Generative/dp/1394254970
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.