| 10:30 AM | Student Panel A Comparative Analysis of Multimodal LLMs for Grading Handwritten Fractions Assignments
This project develops an AI-assisted grading pipeline to assess mathematical accuracy and classify errors in elementary students’ handwritten fraction assignments. Using a teacher-in-the-loop approach, the system aims to provide personalized feedback that supports student learning. We evaluated three multimodal large language models (Claude, ChatGPT, and Gemini) on a dataset of 320 problems. Claude performed best overall, achieving 86.9% agreement with human graders and the most accurate error-family classifications, although it required longer processing times. Its reliable parsing and strong grading performance suggest that multimodal LLMs can support automated mathematics grading and provide educators with actionable insights into students’ learning gaps. However, the models occasionally flagged correct answers as errors, underscoring the importance of teacher oversight. A Label-Free Framework for Evaluating RAG Chatbots with a 16-Month Production Case Study
Retrieval-augmented generation (RAG) chatbots answer user questions using relevant passages retrieved from a knowledge base. They are commonly evaluated using curated benchmarks with labeled data. In real-world deployments, however, chatbot interactions are typically unlabeled, and ground-truth answers are often unavailable for evaluation. To support real-world evaluation, we propose a label-free framework that organizes existing evaluation methods into a reusable workflow without requiring labeled data or ground-truth answers. We demonstrate the framework through a production case study analyzing 112,474 real user interactions collected over 16 months from AskCody, a RAG-based chatbot deployed at CSUSB. The proposed workflow uses embedding-based cosine similarity to measure how well a response addresses the user’s question (relevance) and how closely it aligns with the available retrieved evidence (groundedness). It then examines the distributions of these scores, their relationship at the interaction level, and how they change over time. It also examines reward-model scores, fallback behavior, and interaction patterns to provide additional context for interpreting the automatic scores. Together, the findings demonstrate how the framework combines relevance, groundedness, reward-model scores, and system context to provide complementary screening signals for evaluating large, unlabeled production datasets. Supercharge Your Learning and Productivity with AI: A Student's Guide AI tools can do much more than answer quick questions, but most people never get past typing a sentence into a chat box. In this talk, students who use AI every day in their coursework and projects share what actually works. We start with the fundamentals of effective prompting and introduce a simple, reusable prompt template: give the AI a role, context, a clear task, constraints and the output format you want. This one habit turns vague answers into useful ones. Next, we show how to use Gemini notebooks to turn your own materials, such as lecture notes, readings and documentation, into a personal study assistant. It can summarize, quiz you and answer questions grounded in your sources rather than the open web. Finally, we introduce AI agents, tools that can plan and carry out multi-step tasks on your behalf. We also cover how to direct them, check their work and know when to keep a human in the loop. Attendees will leave with a prompt template they can use right away and practical workflows for studying, research and project work. They will also have a clear sense of how to use AI as a partner in learning without giving up their own thinking |
| 12:00 PM- 1:00 PM | Gen AI for Teaching and Learning
How are CSUSB faculty responding to student use of AI for coursework? What pedagogical practices help address AI concerns or create opportunities to promote student learning outcomes and student engagement? Moderator TRC Director Mihaela Popescu will facilitate a discussion with scheduled panelists. Faculty Panel:
Dr. Jesus Canelon, Associate Professor, School of Cyber & Decision Sciences
Dr. Katy Wimberly, Assistant Professor, Physics and Astronomy
Dr. Mahbuba Hammad, Lecturer, College of Arts & Letters
Dr. Sailesh Maharjan, Lecturer, Psychology
Dr. Samantha Juan, Assistant Professor, Nursing
Dr. Vincent Nestler, Professor, School of Cyber & Decision Sciences |
| 1:00 PM- 2:00 PM | AI and Faculty Research
How are CSUSB faculty using AI in their research? What do they see as the opportunities and concerns? Moderator TRC Director Mihaela Popescu will facilitate a discussion with scheduled panelists. Faculty Panel:
Dr. Samia Amin, Assistant Professor, Health Science and Human Ecology
Dr. Ahmed Elnoshokaty, Assistant Professor, School of Cyber & Decision Sciences
Dr. Daniel MacDonald, Professor, Economics
Dr. Jeremy Murray, Professor, History
Justin Swanson, Lecturer, Kinesiology
Dr. Sishi Wu, Assistant Professor, Criminal Justice
Dr. Stephen Bronack, Professor, Education Leadership and Technology |
| 2:30 PM | Embodied AI Systems: Building Intelligent Humanoid Robots (Yote)
Student Demo (IST 5930 - Embodied AI Systems) Researchers—Intelligence and Interaction:
Explore how Yotie can understand questions, communicate with students, and support classroom learning.
Engineers—Movement and Integration:
Connect the robot’s hardware, sensors, software, and movement so Yotie can interact with its physical environment.
Security Officers—Security and Privacy:
Investigate how to protect the robot’s network connections, manage access, and handle classroom data responsibly. |