ELEVATE Academy: Learn to Use AI in Your Teaching
The University of Texas at San Antonio is offering an open-access eight-session summer workshop plus one prerequisite session on integrating AI tools with instruction, from July 9 through August 11, 2026.
The ELEVATE Academy (Evidence-Led Evaluation & AI Tools for Education) is part of an NSF-funded project (Award #2518973) focused on improving student learning and success in College Algebra, Precalculus, and Calculus. Although the focus of the sponsored project is mathematics instruction, the workshop is open to instructors in any area.
No prior programming experience is required. ELEVATE is open globally via webcast.
What the Project Is About
Artificial intelligence has become an essential tool for nearly everyone across most activities. Whether it is for answering simple questions or developing sophisticated intellectual products, technical and/or creative. It has certainly permeated education.
The biggest questions among educators today are: How to use AI effectively to improve learning? How to prevent outsourcing of reasoning and learning to the machine? ELEVATE addresses these questions through a structured workshop in which proven and novel techniques, both technological and pedagogical, are presented.
We will use a multi-agent strategy (e.g., tutoring, feedback, and assessment agents working together). Participants will learn how to develop and deploy AI agents via Python code and/or through a learning platform called ALICE (Adaptive Learning for Interdisciplinary Collaborative Environments, supported by NSF Award 1645325, 2016-2019) that personalizes learning pathways for students, guides students, identifies gaps in understanding, and gives faculty actionable data to inform their teaching.
ELEVATE participants will be eligible to participate in pilot classrooms including rigorous evaluation of outcomes of students exposed to the pedagogical use of AI. Evaluation will include common assessments and measures of reasoning/transfer.
Two conditions must be met for a pilot: (i) the instructor must teach at least two sections of the same course in the same semester: one with AI and one without (this allows us to quantify the effect of AI), and (ii) there must be at least another instructor teaching the same course (this allows us to quantify instructor effects).
Privacy, Security, and Responsible AI use
The use of ALICE is required to make outcome of pilots comparable. ALICE is hosted at UTSA, with student data encrypted; encryption keys are set and owned by designated institutional administrators. Only vetted personnel with institutional permissions can access corresponding student records.
For pilots that involve multiple institutions, we will use the appropriate inter-institution agreements and review processes (e.g., data-use/data-sharing agreements, as needed) to support FERPA-aligned handling of student information.
When large language models (LLMs) are used in ELEVATE activities, we use (i) locally hosted LLMs and (ii) commercial LLMs configured with zero-data-retention policies.
Eligibility
You must be an instructor at an accredited university. You will be asked to use your institutional email and URL during registration.
How to Participate
ELEVATE will take place from July 9 through August 11, 2026. 10AM-12PM Wednesday & Friday in English and 10AM-12PM Tuesday & Thursday in Spanish.
Register here (free of charge): ELEVATE Academy – Fill out form By early May you will receive instructions to prepare for the workshop.
What ELEVATE Covers
Session 0 covers the ethics of AI. Participants must complete this session before starting the workshop. The other eight sessions move from foundations to classroom application:
The first two sessions focus on practical skills. You’ll build a simple AI agent from scratch using API tools, 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.
Sessions three and four address specific instructional challenges: incorporating AI into writing assignments in mathematics courses (yes, writing in math) and other subjects, and using AI to streamline data analysis workflows. You’ll work with actual student data collected at UTSA.
The remaining sessions 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.
Workshops: July 8-August 7, 2026
The descriptions below are subject to refinement.
Session 0: Ethics of AI Agents in Education (To be completed before the workshop; topics from this session will be used throughout all other sessions of the workshop): This foundational session will establish the ethical framework for responsible use of AI agents in educational settings. Participants will learn to distinguish invalidation (the breach of factual, logical, normative, or structural constraints) from the narrower concept of “hallucination.” The session will examine seven categories of ethical risk: epistemic propagation (how errors circulate through research and teaching), accountability and authorship, provenance and reproducibility, confidentiality, bias and fairness, security, and sustainability. Faculty will study concrete cases involving protected student data, multi-agent workflows, and the boundaries of human responsibility when AI assists in grading, feedback, and content generation. Particular attention will be given to federal guidance prohibiting AI use in peer review contexts and to FERPA-aligned handling of student information. Participants will leave with a governance checklist and disclosure templates suitable for immediate adoption in their courses and research.
Session 1: Step-by-Step Creation of an AI Agent: This introductory session will guide participants through the process of building AI agents using APIs. 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.
Session 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.
Session 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.
Session 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.
Session 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.
Session 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.
Session 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.
Session 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.
ELEVATE 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.