2026 ASEE Annual Conference & Exposition

Engineering laboratories often risk being perceived by students as prescriptive exercises rather than opportunities for authentic inquiry. To address this challenge, we are piloting an AI-powered Project-Based Learning (AI-PBL) framework that integrates artificial intelligence tools with Gold Standard Project-Based Learning (PBL) in three intersegmental undergraduate laboratory courses. This initiative is funded through the California Education Learning Lab AI FAST Challenge and is being developed as an intersegmental collaboration between engineering and computer science faculty at Folsom Lake College, California State University, Sacramento, and University of California, Davis. Together, these institutions represent a community college, a comprehensive R2 university, and an R1 research university, creating a diverse testing ground to evaluate how AI can scaffold student inquiry, close equity gaps, and enhance engagement across contexts.

The AI-PBL framework uses a two-tiered approach to integrate artificial intelligence into laboratory instruction. First, class bots scaffold learning by guiding students through troubleshooting, prompting them to ask higher-level questions, and encouraging reflection on experimental design choices. These bots are trained on curated open educational resources (OER) and course materials, ensuring equitable access to knowledge while supporting students in connecting experimental data to theory. Second, the ADE (AI-Driven Education) bot functions exclusively as an assessment tool. Rather than providing instructional support, the ADE bot analyzes the questions students ask the class bot. It evaluates these questions by mapping them to Bloom’s taxonomy levels and scoring them against modified AAC&U VALUE rubrics, which are aligned with ABET outcomes. This dual framework enables instructors to track student progression from recall and comprehension to higher-order reasoning, including analysis, evaluation, and creation, while also assessing competencies such as inquiry, problem-solving, and communication.

To ensure authenticity and relevance, external industry advisors propose or support projects that students compete to solve. At Sacramento State, the EEE 161 Applied Electromagnetics Lab is themed around “Antennas for Smart World”; at UC Davis, the ENGR 104L Mechanics of Materials Lab emphasizes “My Home”; and at Folsom Lake College, the CISP 430: Data Structures course centers on “Stories Hidden in Data.” These authentic project themes address societal issues while mirroring professional practice. Students must not only demonstrate technical mastery but also justify design decisions and present their solutions at college-wide showcases and student research conferences.

This study seeks to examine whether student-driven, AI-enhanced PBL leads to improved learning outcomes and reduced equity gaps, particularly for URM and female students; whether AI integration fosters deeper engagement and higher metacognitive abilities as students progress through the term; and how the combination of Bloom’s taxonomy and VALUE/ABET-aligned rubric assessments via ADE bot can provide faculty with scalable, evidence-based measures of inquiry depth, metacognition and critical thinking.

This work-in-progress paper will report on early implementation and cross-institutional collaboration, contributing to the growing conversation on how artificial intelligence, combined with project-based pedagogy and industry engagement, can transform laboratory instruction into a space for inquiry, innovation, and equity in engineering education.