MSIT3101
INDUSTRY/CREATIVE APP GEN AI
- Department
- Master of Science Info. Tech.
- Terms offered
- 3
- Data refreshed
- Jun 27, 2026
Description
This course provides an in-depth exploration of how Generative AI is utilized in creative fields such as digital art, music, and design, as well as its applications to enhance productivity and performance in business settings. Through a blend of theoretical discussions and hands-on projects, students will examine innovative ways in which Generative AI algorithms transform traditional creative processes, enabling artists, musicians, and designers to push the boundaries of their craft. Additionally, students will explore case studies and real-world examples highlighting the integration of Generative AI in various industries, from marketing and advertising to finance and healthcare, gaining insights into how these technologies can improve efficiency, streamline workflows, and unlock new opportunities for product innovation and growth. By the end of the course, students will emerge with a deep understanding of the diverse applications of Generative AI and the skills to leverage its potential to revolutionize both creative expression and business operations.
Syllabi
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Offering history & forecast 2025–2026
| Year | Fall | Spring | Summer | Intersession |
|---|---|---|---|---|
| 2030 | 2030 Fall: not enough history to forecast. | 2030 Spring: not enough history to forecast. | 2030 Summer: 7% chance, medium confidence. Offered in 0 of 9 summer terms since 2018. | 2030 Intersession: 8% chance, medium confidence. Offered in 0 of 6 intersession terms since 2020. |
| 2029 | 2029 Fall: not enough history to forecast. | 2029 Spring: not enough history to forecast. | 2029 Summer: 6% chance, medium confidence. Offered in 0 of 10 summer terms since 2017. | 2029 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2028 | 2028 Fall: not enough history to forecast. | 2028 Spring: not enough history to forecast. | 2028 Summer: 5% chance, medium confidence. Offered in 0 of 11 summer terms since 2016. | 2028 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2027 | 2027 Fall: not enough history to forecast. | 2027 Spring: not enough history to forecast. | 2027 Summer: 5% chance, high confidence. Offered in 0 of 12 summer terms since 2015. | 2027 Intersession: 6% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2026 | 1 section | Not offered | Not offered | No data for this term |
| 2025 | 1 section | 2 sections | Not offered | Not offered |
Future estimates combine recent offering frequency with detected multi-year cycles. Hover a cell for confidence and evidence; low-confidence estimates are marked with ~.
Meeting times
Most recent first- Tue, 6:30 PM–9:30 PM JC118 20262025
- Wed, 6:30 PM–9:30 PM ONLINE 2025-1
- Thu, 6:30 PM–9:30 PM ONLINE 2025-2
| Term | Professor | Type | Credits | Enrollment | CRN |
|---|---|---|---|---|---|
| Fall 2026 | Shivansh Awasthi (He/Him) | Online | 1 | 1 of 30 enrolled | 24047 |
| Fall 2025 | Kevin C. Keller | Online | 1 | 10 of 30 enrolled | 24047 |
| Spring 2025 | Kevin C. Keller | Online | 1 | 31 of 30 enrolled | 34009 |
| Spring 2025 | Kevin C. Keller | Online | 1 | 13 of 30 enrolled | 34103 |