Artificial Intelligence Fundamentals, Minor

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The Minor in Artificial Intelligence Fundamentals equips students from all majors with foundational knowledge, ethical grounding, and practical skills in the responsible application of artificial intelligence within and across disciplines, preparing graduates to contribute meaningfully to an AI-enabled workforce. To meet the rising demand for AI-literate professionals, this interdisciplinary minor introduces students to the foundations, tools, and ethical considerations of AI, giving them a broad understanding of a range of AI applications while enabling them to explore its potential in specific contexts.

Admission Requirements

Current UNC Charlotte Undergraduate Students

See University Admission Requirements
Open to all undergraduate students.
No prerequisites are required.

Minor Requirements

The Minor in Artificial Intelligence Fundamentals requires 15 credit hours. Students take two (2) core required courses. In addition to these two courses, students take three (3) elective courses from the list of courses offered across participating departments or programs. Some electives may require completion of one or both of the core courses in the minor or completion of specific prerequisites from other programs. (For example, if a student wishes to take MKTG 3219 as an elective, this may require completion of a requisite MKTG course to be eligible to enroll in MKTG 3219 elective).

Faculty teaching the elective will determine specific prerequisite requirements. This program recognizes both the breadth of AI literacy and depth of AI fluency, ensuring that students across arts, business, health, humanities, education, and STEM develop skills in AI principles and tools relevant to their disciplines.

Required Core Courses (6 Credit Hours)

Elective Courses (9 Credit Hours)

Students select nine (9) credit hours from the following list of elective courses.

ARCH 4050 - Architecture Topics (3)
Any Artificial Intelligence section topic of ARCH 4050 (attribute code ARIN)
ARTM 3005 - Topics in Digital Media (3)
BUSA 3000 - Topics in Business Analytics (3)
CMET 4135 - Construction Technologies and Innovation (3)
COSC 3000 - Topics in Science (1 to 4)
ECGR 4103 - Machine Vision, AI and Image Processing (3)
ECGR 4105 - Introduction to Machine Learning (3)
ECGR 4106 - Foundations of Deep Learning (3)
ECGR 4115 - Convex Optimization and AI Applications (3)
ECGR 4116 - Artificial Intelligence for Biomedical Applications (3)
ECGR 4117 - AI for Robotics and Automation (3)
ECGR 4127 - Machine Learning for the Internet of Things (3)
ECON 3090 - Topics in Economics (3)
ETEM 4131 - Intelligent Industrial Systems (3)
FINN 3000 - Topics in Finance (3)
HCAI 3111 - HCAI Social Science Applied (3)
HCAI 3112 - Applied Humanities in Artificial Intelligence Studies (3)
ITIS 4360 - Human-Centered Artificial Intelligence (3)
ITIS 4353 - Social Technology Design (3)
MKTG 3219 - AI in Marketing (3)
PHIL 3272 - Philosophy of Technology (3)
PHIL 3224 - Data Ethics (3)
PHIL 3253 - Science, Knowledge, and Values (3)
PHIL 3255 - Philosophy of Artificial Intelligence (3)
POLS 3030 - Topics in Comparative or International Politics (1 to 4)
Any Artificial Intelligence section topic of POLS 3030 (attribute code ARIN)
PSYC 2316 - Introduction to Cognitive Processes (3)
SEGR 2110 - Systems Engineering Concepts (3)
SEGR 3103 - Human System Interface (3)
SEGR 3105 - Computational Methods for Systems Engineering II (3)
SEGR 3110 - System Design and Deployment (3)
SEGR 4090 - Special Topics (1 to 6)
SEGR 4952 - Engineering System Optimization (3)

Minor Total = 15 Credit Hours

Progression Requirements

Students must attain an overall GPA of 2.0 in all coursework within the minor. No courses counted toward the minor may be taken Pass/No Credit.