CIS 288 - Artificial Intelligence Capstone

Description
The AI Capstone Project is the culminating experience for students enrolled in the AI Certificate Program at Grand Rapids Community College. This project-based course is designed to provide students with the opportunity to apply their knowledge of artificial intelligence to a comprehensive and practical project.

Credit Hours: 3
Contact Hours: 3

School: School STEM
Department: Computer Information Systems
Discipline: CIS

Course Review & Revision Year: 2028-2029

Course Type:
Program Requirement- Offering designed to meet the learning needs of students in a specific GRCC program.
Course Format:
Lecture - 1:1

General Education Requirement: None
General Education Learner Outcomes (GELO):
NA

Course Learning Outcomes:
Upon completion of the AI Capstone Project, students will be able to:

Demonstrate a comprehensive understanding of AI concepts and methodologies.
Apply AI techniques to address real-world problems.
Critically analyze and solve complex AI-related issues.
Communicate and defend their AI project effectively.
Gain proficiency in collaborative industry-standard tools and platforms.
Develop applied industry experience and relationship building.

Course Outline:

AI Capstone Course

I. Weeks 1-2: Orientation and Project Selection

a. Introduction to the AI Capstone Project, overview of objectives, and expectations
b. Guidance on forming teams or choosing individual projects
c. Ideation and selection of AI project topics
d. Initial project proposals and discussions with faculty for approval

II. Weeks 3-4: Project Planning and Proposal Refinement

a. Detailed project planning, including scope, objectives, and methodologies.
b. Refinement of project proposals based on initial feedback.
c. Discussion and development of project timelines and milestones.

III. Weeks 5-8: Implementation and Development

a. Hands-on work on the AI project, applying various AI techniques and methodologies
b. Regular check-ins and consultations with faculty for guidance and support
c. Data acquisition, preprocessing, model development, and iterative improvements

IV. Weeks 9-10: Ethical Considerations in AI

a. Exploration and discussion of ethical considerations in AI project development
b. Examination of ethical frameworks, bias, fairness, and transparency in AI systems
c. Integration of ethical principles within the ongoing projects

V. Weeks 11-12: Progress Evaluation and Adjustment

a. Evaluation of project progress against predefined milestones
b. Identification of potential challenges and necessary adjustments
c. Fine-tuning of project implementations based on feedback and interim assessment

VI. Weeks 13-14: Presentation Preparation

a. Preparing and refining materials for the final presentation
b. Practice sessions for effective communication and presentation skills
c. Revisions and finalization of project documentation

VII. Week 15: Final Presentations and Evaluation

a. Presentation of AI projects to a panel of experts or stakeholders
b. Defense and explanation of project methodologies, outcomes, and ethical considerations
c. Evaluation and feedback from the panel and faculty

VIII. Week 16: Reflection and Conclusion

a. Reflective discussions on the overall project experience and lessons learned
b. Course conclusion, wrap-up, and submission of final project documentation
c. Individual assessment and feedback sessions

Mandatory CLO Competency Assessment Measures:
None

Name of Industry Recognize Credentials: None

Instructional Strategies:
Lecture 40%-50%

Facilitated Discussion 20%-30%

Facilitated Group Work 40%-50%

Mandatory Course Components:
None

Academic Program Prerequisite: None
Prerequisites/Other Requirements: CIS 240 and CIS 270 (C or better)
English Prerequisite(s): None
Math Prerequisite(s): Eligible for Math 105 or Higher; SAT Math Score of 24.5 or Higher
Course Corerequisite(s): None
Course-Specific Placement Test: None
Course Aligned with IRW: NA
Consent to Enroll in Course: No Department Consent Required

Total Lecture Hours Per Week: 3
Faculty Credential Requirements:
Master’s Degree (GRCC general requirement)
Faculty Credential Requirement Details: 18 graduate credit hours in discipline being taught (HLC requirement) - The instructor must possess a minimum of a Master of Computer Science or a Master in Computer Information Systems with demonstrated studies/work with Artificial Intelligence, Machine Learning, or Advanced Algorithms. Professionally qualified through work experience in field - Two years work experience in tech industry directly related to AI, Machine Learning, or Data Science. Program Accreditation Requirement - The instructor must have a Lead Facilitator Certificate in AI for Workforce Program or equivalent.

General Room Request: AI Incubator

Maximum Course Enrollment:
Equivalent Courses: None
Dual Enrollment Allowed?: Yes
AP Min. Score:
Number of Times Course can be taken for credit: 1
First Term Valid: Fall 2024 (8/1/2024)
Programs Where This Courses is a Requirement:
Pathway Degree with Computer Information Systems Concentration, A.A.
1st Catalog Year: 2024-2025
People Soft Course ID Number: 105264
Course CIP Code: 11.9999

Name of Course Author:
Jonnathan Resendiz