Skip to header Skip to Content Skip to Footer

AI in Quantitative STEM Learning Community

The AI in Quantitative STEM (Q-STEM) Learning Community supports students transitioning from foundational coursework in mathematics-intensive STEM degree programs into applied learning in artificial intelligence (AI) and quantitative research. Designed for beyond-the-first-year students, this non-residential learning community brings together students from mathematics-intensive fields—such as mathematics, statistics, physics, engineering, computer science and others—to develop AI literacy, engage in interdisciplinary research, and explore the ethical, technical and societal dimensions of AI in science and engineering. Students need to complete MAT 161 prior to starting the Learning Community.

The AI in Q-STEM is anchored in the Certificate in Artificial Intelligence Literacy, providing a structured academic and co-curricular pathway that connects coursework, research and professional preparation while fostering a strong sense of belonging among quantitatively focused peers.

What to Expect

In the AI in Q-STEM Learning Community, you will connect with students who share your interests in STEM majors and artificial intelligence while building relationships that support both your academic and professional goals. This Learning Community helps you build your network early by connecting you with faculty mentors, peer leaders and campus partners who support your growth in quantitative STEM fields.

You will also gain exposure to how AI is used beyond the classroom through interactive experiences such as guest talks, industry-focused presentations and field trips that highlight real-world applications of AI in science, engineering and data-intensive careers. These experiences are designed to help you see possible career paths, ask questions of professionals in the field, and connect what you are learning to meaningful, high-impact work.

You will:

  • Learn how artificial intelligence and data analysis are used across Q-STEM disciplines
  • Collaborate with peers on research and applied projects connected to real-world problems
  • Engage with faculty mentors and professionals through talks, site visits and career-focused events
  • Explore research, internship and career opportunities that build AI and data literacy
  • Develop skills in ethical reasoning, communication and interdisciplinary teamwork
  • Gain confidence applying calculus, statistics and computational tools beyond the classroom

This shared experience supports your transition from foundational coursework to applied learning while helping you build a strong sense of community, purpose and direction in quantitative STEM.

 

Courses

The Learning Community supports completion of the Certificate in Artificial Intelligence Literacy (9 credits) through a coordinated sequence of required and elective courses. The Certificate requires PAR 312, CSC 202 or CSC 302, and an elective course from an approved list of courses.

  • PAR 312 – Ethics of Artificial Intelligence or CSC 202 – AI Essentials: Understanding Artificial Intelligence or CSC 302 – Introduction to Artificial Intelligence (based on prerequisites)
  • MAT 162 – Calculus with Analytical Geometry II or MAT 261 Multivariant Calculus
  • PHY 201 – General Physics I or PHY 202 – General Physics II
  • PAR 312 – Ethics of Artificial Intelligence or CSC 202 – AI Essentials: Understanding Artificial Intelligence or CSC 302 – Introduction to Artificial Intelligence (based on prerequisites, do not take both CSC courses)
  • CSC/DSC/PHY/EGN/ISE/MAT 291 or 491 (or another approved course by the learning community director and department chair)
  • One of the following:
    • ART 370 – 3D Printing and Laser Cutting
    • CSC 322 – Data & Machine Learning
    • ECN 325 – Environmental Economics
    • ENG 309 – Technical Editing
    • ENG 312 – Writing Strategies for Business and Innovation
    • ENG 314 – Digital Composing
    • ENG 319 – Document Design
    • ENG 326 – Writing with Artificial Intelligence
    • ENG 414 – Writing for the Web
    • EVS 325 – Environmental Economics
    • EVS 428 – Environmental Interpretation in Protected Areas
    • EVS 325 – Environmental Economics
    • EVS 428 – Environmental Interpretation in Protected Areas
    • EVS 455 – Geospatial Analysis for Coastal Management
    • HST 329 – History of Science and Technology in the Modern World
    • MAT 275 – Axiomatic Systems
    • MIS 370 – Problem-Solving with Artificial Intelligence
    • PAR 105 – Critical Reasoning with Artificial Intelligence
    • PAR 230 – Judaism, Christianity, and Islam
    • PAR 320 – Philosophy of Mind
    • PAR 344 – Religion and Popular Culture
    • PAR 390 – Religion and Technology
    • SOC 340 – Organizations in Modern Society
    • STT 215 – Introduction to Statistics