CSC-280 AI Integration & Professional Practice

AI Integration & Professional Practice serves as the culminating course in the AI Specialist Microcredential series, preparing learners to implement AI solutions in real-world professional contexts. Learners explore domain-specific AI applications across industries including marketing, education, software development, data analysis, and customer service, learning from diverse use cases and peer experiences. The course emphasizes critical organizational considerations including data governance, privacy policies, change management, and measuring AI impact. Learners gain deeper experience in operational decisions like balancing cost, privacy, and capability trade-offs. Through a comprehensive final project, students design and implement an AI solution for an actual problem in their field, demonstrating appropriate tool selection, ethical considerations, security measures, and sustainable workflow design. By the end of the course, students can serve as AI champions in their organizations, implementing solutions responsibly while navigating the technical, ethical, and human dimensions of AI adoption.

Credits

1

Lecture Contact Hours

1

Lab Contact Hours

0

Other Contact Hours

0

Department

• Computer Science

Grading Scheme

• Letter

SUNY Gen Ed Credit

• No

Semesters Course Will Be Offered

• Fall
• Spring
• Summer
• Winter

Course Learning Outcomes

1. Implement domain-specific AI solutions that address real professional challenges while incorporating appropriate privacy, security, and ethical safeguards based on organizational context and data sensitivity.
2. Evaluate and deploy self-hosted AI systems using tools like LM Studio, making informed decisions about when local deployment is necessary and managing the technical and cost trade-offs involved.
3. Design organizational AI adoption strategies that include governance policies, change management approaches, training plans, and methods for measuring return on investment and impact.
4. Synthesize AI knowledge into professional practice through a culminating project that demonstrates end-to-end implementation of an AI solution, from problem identification through deployment and documentation.

View Course Outline

CSC 280: AI Integration & Professional Practice

Department

Computer Science

Course Description

AI Integration & Professional Practice serves as the culminating course in the AI Specialist Microcredential series, preparing learners to implement AI solutions in real-world professional contexts. Learners explore domain-specific AI applications across industries including marketing, education, software development, data analysis, and customer service, learning from diverse use cases and peer experiences. The course emphasizes critical organizational considerations including data governance, privacy policies, change management, and measuring AI impact. Learners gain deeper experience in operational decisions like balancing cost, privacy, and capability trade-offs. Through a comprehensive final project, students design and implement an AI solution for an actual problem in their field, demonstrating appropriate tool selection, ethical considerations, security measures, and sustainable workflow design. By the end of the course, students can serve as AI champions in their organizations, implementing solutions responsibly while navigating the technical, ethical, and human dimensions of AI adoption.

Credit Hours

1

Contact Hours

Lecture	1
Lab	0
Other	0

Grading Scheme

Letter

Semester(s) Course Will Be Offered

Fall, Spring, Summer, Winter

First Year Experience Course

No

Capstone Course

No

Course Learning Outcomes

1. Implement domain-specific AI solutions that address real professional challenges while incorporating appropriate privacy, security, and ethical safeguards based on organizational context and data sensitivity.
2. Evaluate and deploy self-hosted AI systems using tools like LM Studio, making informed decisions about when local deployment is necessary and managing the technical and cost trade-offs involved.
3. Design organizational AI adoption strategies that include governance policies, change management approaches, training plans, and methods for measuring return on investment and impact.
4. Synthesize AI knowledge into professional practice through a culminating project that demonstrates end-to-end implementation of an AI solution, from problem identification through deployment and documentation.

Topic Outline

1. Privacy, security, and self-hosting - Deep dive into LM Studio for running models locally; understanding data sensitivity and when cloud AI is inappropriate; organizational data policies and governance; data lakes

2.

3. Domain-specific applications - AI in marketing, education, software development, data analysis, customer service, and content creation; learning from diverse industry case studies and peer use cases

4.

5. Organizational integration - Change management strategies for AI adoption; creating policies and guidelines; training and supporting teams; measuring ROI and impact

6.

7. Building your AI stack - Combining tools for maximum effectiveness; creating sustainable workflows; troubleshooting and maintenance; cost-benefit analysis of free versus paid tools

8.

9. Professional AI leadership - Serving as an AI champion in your organization; communicating AI capabilities and limitations to stakeholders; balancing innovation with risk management

10.

11. Culminating project - Designing and implementing an AI solution for a real problem from your work or field; demonstrating tool selection, ethics, privacy, and sustainable workflow design