This introductory course explores the interdisciplinary field of artificial intelligence. Students develop AI literacy, examine technical and social dimensions, and analyze societal impacts. Through activities, case studies, and projects, they learn how AI functions, its limitations, and its influence across industries.

Introduction of Python programming for management of textual, tabular, and image data, used in conjunction with JSON, NumPy, Pandas, and LangChain for data management, cleaning, wrangling, aggregation, and interfacing. Credit cannot be given for both IAI 110/ASE110 and CIS 151.

Introductory concepts in set theory, probability theory, linear algebra, and the fundamentals of how they are applied to artificial intelligence. IAI302/ASE302 cannot be taken for credit after receiving a grade of C or higher in MAT331, MAT485, or CIS375.

Foundational concepts in artificial intelligence, including agents, knowledge representation and reasoning, machine learning, neural networks, reinforcement learning, and large language models, accompanied by programming projects and exercises.

This course provides a comprehensive understanding of Artificial Intelligence to students from different backgrounds and disciplines, including key concepts, techniques, and technical infrastructure, along with their social, environmental, economic, legal, and cultural context of emergence.

This class uses a cross-disciplinary approach to study how ethical principles, public policy, and private governance could guide the development and deployment of artificial intelligence in the public interest.

Understand, analyze, and critique scientific and practical perspectives on what counts as interactivity and intelligence when AI-driven agents interact with humans

Examines human-AI systems through individual behavior theories (rational choice, game theory, social choice) and dynamic system modeling approaches (agent-based models, complex systems, adaptive landscapes).

Designing AI interventions in sociotechnical systems and evaluating their effectiveness using experimental and quasi-experimental methods, from stakeholder analysis to impact measurement and recommendation formulation.

This one-semester capstone is the culminating experience for the Integrative AI major, where students synthesize technical and societal knowledge through a self-directed project research paper, functional prototype, or portfolio guided by faculty and presented to diverse audiences.