The University of Texas at San Antonio is offering an eight-week summer workshop on integrating AI tools into undergraduate mathematics instruction. The workshop is part of a new NSF-funded project (Award #2518973) focused on improving student learning and success in College Algebra, Precalculus, and Calculus.

No prior programming experience is required. The workshop is open nationally via webcast, and UTSA participants receive a $2,000 stipend.

What the Project Is About

We’re developing an AI-assisted adaptive learning platform called ALICE (Adaptive Learning for Interdisciplinary Collaborative Environments) that personalizes learning pathways for students, identifies gaps in understanding, and gives faculty actionable data to inform their teaching. The project includes rigorous evaluation of student outcomes across multiple pilot classrooms.

But the technology is only half the story. What matters is people. The graphic below shows how working with people resulted in a substantial improvement in student outcomes. This was achieved with a group effort to promote horizontal and vertical alignment of curricula at the Department of Mathematics at UT San Antonio. While serving ~12k registrations per year, we were able to reduce DFWs (drops, fails and withdrawals) from 34% to 24% just by introducing course coordination.

The big question now is : Can AI help further? We want to help faculty become confident, thoughtful users of AI in their own courses. That’s where the workshop comes in.

What the Workshop Covers

The eight sessions move from foundations to classroom application:

The first two weeks focus on practical skills. You’ll build a simple AI agent from scratch using tools like GPT and Claude, then learn how these tools can help with lecture preparation, grading, and individualized tutoring. These sessions assume you’re starting from zero with AI development… that’s the point.

Weeks three and four address specific instructional challenges: incorporating AI into writing assignments in mathematics courses (yes, writing in math), and using AI to streamline data analysis workflows. You’ll work with actual student data collected at UTSA.

The remaining weeks turn toward your own teaching. You’ll evaluate curricular alignment in a course you plan to pilot in the fall, explore how to guide students in using AI as an inquiry tool, design AI-integrated active learning activities, and learn to assess whether these approaches are actually helping your students.

What You Get

20 UTSA participants who complete the workshop receive a $2,000 stipend. Five participants will also be selected as workshop facilitators, with an additional stipend for that role.

Beyond compensation, you’ll leave with a concrete plan for running an AI-enhanced pilot in your own classroom, supported by our project team. Your pilot will contribute to the project’s evaluation, meaning your work becomes part of a larger, peer-reviewed effort to understand what actually works in AI-assisted education.

The workshop is broadcast nationally, so you can participate from anywhere. This also means you’ll be learning alongside faculty from other institutions, which we’ve found enriches the conversation considerably.

Who This Is For

The workshop is designed for faculty who teach early undergraduate mathematics (College Algebra, Precalculus, Calculus I, Calculus II, etc) though the skills transfer broadly. We especially welcome participants who are curious about AI but unsure where to start. The first session exists precisely because many faculty haven’t had a reason to engage with these tools yet, and we want to change that in a structured, low-pressure way.

If you’re skeptical about AI in education, you’re also welcome. Healthy skepticism makes for better pedagogy, and we’d rather have critical voices in the room than outside it.

How to Participate

Details on applications and deadlines will be posted soon. If you’d like to be notified when registration opens, or if you have questions about the project, please contact juan.gutierrez3@utsa.edu.

Workshops:

Week 1: Step-by-Step Creation of an AI Agent: This introductory session will guide participants through the process of building AI agents using APIs like GPT and Claude. Designed for individuals with little to no programming experience, the seminar will provide a hands-on, step-by-step framework to create a basic AI agent. The primary objective will be to demystify AI development and empower attendees to harness these tools with confidence.

Week 2: AI Agents in Instructional Activities: This seminar will demonstrate how LLMs can transform instructional tasks. Faculty will learn to use AI for generating lecture materials, grading assignments, and creating AI-powered tutoring systems tailored to specific lessons. By automating routine tasks, these tools will allow educators to focus on meaningful interactions with students, marking a significant advancement in teaching methodologies.

Week 3: Writing Assignments with AI: This session will explore effective strategies for incorporating AI into writing assignments within mathematics courses. Faculty will learn methods to leverage AI tools to support students in structuring clear, logical arguments, defining concepts precisely, and articulating reasoning in solving mathematical problems. Practical techniques will be demonstrated to maintain academic integrity while using AI to enhance student productivity and encourage deeper conceptual understanding through reflective writing activities.

Week 4: AI in Data Analysis: This seminar will highlight the application of LLMs in automating complex data analysis workflows. Participants will study student data collected at UTSA during the period 2022-2025. These tools will showcase their potential to simplify intricate coding tasks, enabling researchers to focus on interpretation and discovery.

Week 5: Evaluating Curricular Alignment with LLMs: This session will explore how LLMs can be used to assess and optimize curricular alignment across courses. Participants will evaluate curricular alignment in the course in which they will run the pilot in the fall semester, and will learn how AI can strengthen, validate, and/or identify inconsistencies in course objectives, instructional materials, and assessment strategies.

Week 6: Using LLMs to Support Student Inquiry: This seminar will focus on empowering students to use LLMs as tools for inquiry and exploration. Participants will examine strategies for integrating LLMs into inquiry-based learning models for the course in which they will run the pilot in the fall semester, enabling students to formulate questions, explore answers, and deepen their understanding of complex topics. Hands-on activities will highlight best practices for promoting effective student engagement with AI.

Week 7: Supporting Active Learning with AI: Participants will explore how AI technologies can enhance active learning practices. The seminar will cover ways to incorporate LLMs into activities for the course in which they will run the pilot in the fall semester like group problemsolving, peer teaching, and interactive simulations. Faculty will gain insights into designing AIintegrated
lessons that promote critical thinking and collaboration.

Week 8: Evaluating How LLM Ensembles Support Students: This session will examine the potential of LLM ensembles—combinations of multiple AI agents—for supporting diverse learning needs. Faculty will learn how to analyze the effectiveness of ensembles when deployed in their classrooms. Practical evaluation methods will be shared to assess their impact on student learning outcomes.

---

This project is supported by the National Science Foundation under Award #2518973, “Supporting Student Learning and Success in Early Undergraduate Mathematics Courses via AI-Enhanced Education.” The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students.