Course Syllabus
Contact Information
Course Details
For a list of course activities, scroll to the bottom of this page.
Course Format
- Synchronous Online: Learning occurs online and in real time, through interactions on Zoom. Live Sessions occur weekly and attendance is expected.
Course Materials
- ChatGPT Plus, 3 month subscription ($20/month) -- March 1 to May 31
- Additional information can be found on the RESOURCES: Course Materials page
Program Learning Outcomes (PLO)
- Demonstrate competency in the application of technology to business.
- Demonstrate decision-making and problem-solving skills in specific domains.
- Exhibit global awareness and strategic perspectives of solutions.
- Understand ethical issues in business.
- Demonstrate leadership and teamwork skills.
Course Learning Outcomes (CLO) Alignment
| CLO | Description | PLO |
|---|---|---|
| A |
Understand Generative AI concepts and capabilities by equipping them with a foundational understanding of what Generative AI is, including key concepts, types of models and the underlying principles of how they work. |
1, 2, 3 |
| B |
Learn how Generative AI can be applied in business settings, covering a range of functions such as marketing, product development, content creation and customer service. |
1, 2, 3, 5 |
| C |
Critically assess the ethical, legal and societal implications of deploying Generative AI in business, including issues like data privacy, bias and misinformation. |
3, 4 |
Grades
Breakdown
| Activity | Description | Points/Percentage |
|---|---|---|
| Participation | Participation in weekly Live Sessions and completion of in-class activities. | 16% |
| Discussion Board Posts | Weekly, individual posts providing insightful questions/comments on the posted resources and module's topic. | 14% |
| Application Reports | Weekly, individual reports demonstrating the application of generative AI to a novel use case. | 30% |
| Final Report | Summative, individual report demonstrating the application of generative AI to a synthesis of weekly topics or new novel use case. | 30% |
| Peer Review | Evaluation assignment to assess micro group members' collaboration and accountability | 10% |
| Total | 100% |
Scale
| Percentage | Letter Grade |
|---|---|
| 90–100% | A |
| 80–89% | B |
| 70–79% | C |
| 60–69% | D |
| Below 60% | F |
Course Components
Course and Live Session Participation
This is a course in which we will learn by doing. Due to the experiential-learning nature of this course, it is impossible to be a passive participant and achieve the course learning objectives. Active participation will be required each week during Live Sessions. Full participation includes having cameras on, microphones muted, and using the available Zoom functions (raised hand) to speak. This includes engagement during hands-on demonstrations, micro- and macro-groups, and concept presentations. This course component will help you achieve Course Learning Outcomes (CLOs) A, B, and C.
Discussion Board
Each week, you will actively participate in the module's discussion board by sharing your reflections on the module's assigned materials. Posts should demonstrate thoughtful connections to the current or preceding module's topic. Posts may include a comment, question, or issue to be considered and must be posted before class (Live Sessions on Wednesdays). While responding to your peers is not required, you are encouraged to read through your peers' posts and are welcome to reply in order to facilitate an exchange of ideas or present a new perspective. This course component will help you achieve Course Learning Outcomes (CLOs) A, B, and C.
Micro Group Demonstration
Students will be organized into micro groups to work together on all AI use case demonstrations presented during Live Sessions. During each Live Session, you will work with this group in breakout sessions to practice applying the concepts learned from the instructor-led demonstration to a related, but new, use case. Following the breakout session, your micro group will be responsible for presenting your use case, highlighting lessons learned and challenges faced to the whole class (macro group) each week. This course component will help you achieve Course Learning Outcomes (CLOs) A and B.
Application Report - Individual
To extend your understanding of the concepts and tools presented in each module (two through seven), you will complete a weekly, individual application report (of no more than 200 words, double-spaced, 12 pt. font--about 2 pages, excluding appendices). The aim of this assignment is to bridge the gap between theoretical knowledge and practical application of business applications of generative AI. This report will succintly communicate your efforts to apply generative AI to solving a business problem. This course component will help you achieve Course Learning Outcomes (CLOs) A and B.
Final Report
The Final Report is an opportunity for you to show how the concepts learned in this course might be applied to problems or opportunities that are part of your career, interests or life. Your Final Report may extend work demonstrated in a previous AI Application Report, or it may involve a new use case, but should focus on synthesizing concepts/tools from multiple weeks.
The Final Report will be due during the last week of the course and submitted via Canvas assignment. This component will help you achieve Course Learning Outcomes (CLO) A, B, and C.
Micro Group Peer Evaluation
Since weekly preparedness and collaboration are essential to the success of your micro-group, you will provide an assessment of the participation and contribution of your team members during Live Sessions. These honest evaluations are meant to ensure that each group member participates meaningfully during the Live Sessions and is accountable to their group.
The Micro Group Peer Evaluation activity will be completed during the last week of the course using the Feedback Fruits application embedded within this Canvas course. This course component will help you achieve Course Learning Outcomes (CLOs) B and C.
Course Policies
Communication
The easiest way to contact me is through email. Under normal circumstances, I strive to respond to email messages within 24 hours during weekdays. If I am traveling, dealing with unforseen events or during the weekend, responses may be delayed.
You can call me Dr. Laku or Laku (if you feel comfortable).
Feedback
Under normal circumstances, I strive to provide feedback on assignments within a week. If I am traveling or dealing with unforseen events, my feedback may be delayed.
Late Policy
You can be late by two days on one (of eight) Discussion Board Posts and one (of six) Application Reports without any penalty. Beyond these, every late assignment will incur a late penalty of 20%, and after five days will not recieve a grade. You can also miss one Micro-group in-class session without penalty. Additional absences will result in a 20% reduction for each absence in the Micro-group in-class assignment. The Final Report must be submitted on time to earn a grade. No late submissions will be accepted.
Plagiarism
While we will be using Generative AI, which was trained on output created by others, to develop solutions, it is your responsibility to ensure that the final product you turn in has been vetted carefully. Ensure that the content is accurate, unbiased and captures your authentic voice. Often you will need to read and review AI-generated output very carefully to make sure it meets all these criteria and addresses the business problem correctly.
For more information about plagiarism, watch this video.
University Academic Policies and Student Support
Access the University Academic Policies Document.
Course Summary:
| Date | Details | Due |
|---|---|---|