2026 ASEE Annual Conference & Exposition

Abstract - An Introduction to Applied Neural Networks, first introduced at the University of Houston in the 1990s, was the first graduate-level course in artificial intelligence (AI) within the Engineering Technology curriculum. Although the course content has been periodically updated, the rapid advancement of AI and machine learning (ML) in recent years necessitated a comprehensive redesign to align with current technologies and industry practices. Supported by a University of Houston Teaching Innovation Program (TIP - 2025) grant, the course has been revitalized by incorporating the latest advanced topics including Generative AI, Reinforcement Learning, Transformers, Convolutional Neural Networks, and Deep Learning - while embedding experiential learning throughout the course. The redesigned graduate-level neural networks course integrates industry-standard tools, including Amazon Web Services (AWS) and NVIDIA Deep Learning Institute (DLI) lab kits, to support authentic, data-driven project work. A hybrid delivery model combines in-person instruction with both asynchronous and synchronous online laboratory sessions, enhancing accessibility, collaboration, and instructional flexibility. Preliminary implementation of selected AWS and NVIDIA modules in Spring 2025 yielded positive student feedback and descriptive gains in self-reported familiarity with key AI concepts and tools. Full implementation in Fall 2025 expanded these efforts through project-based laboratories and real-world examples using MATLAB and Python, leveraging AWS and NVIDIA resources throughout the semester. Course learning outcomes were evaluated using a combination of student surveys and performance indicators aligned with course objectives. The findings suggest increased student-reported confidence and perceived understanding of industry-relevant AI competencies. The redesigned curriculum provides a practical framework for integrating experiential, industry-aligned tools into AI courses and engineering education.