Integrating AI into Engineering Education
Integrating AI into Engineering Education
Minhaj Alam
Course: Applied AI and AI for Biomedical Applications
At UNC Charlotte ECE, two courses—AI for Biomedical Engineering Applications and Applied AI—have been designed to provide students with hands-on experience, technical expertise, and real-world applications of AI. Through project-based learning, journal presentations, and industry-relevant tools like GitHub, these courses ensure that students are not just consumers of AI knowledge but active contributors to the field.
Final Projects: AI in Action
One of the most impactful elements of these courses is the final project, where each student selects and develops an AI-related application. This open-ended approach allows students to explore areas that align with their interests and career aspirations while applying AI techniques in meaningful ways.
Some standout projects have included:
- Large Language Models (LLMs): Students fine-tuned LLMs for domain-specific tasks, such as biomedical text summarization and automated report generation.
- Biomedical Classification with Optical Coherence Tomography (OCTs): AI models were trained to classify medical images, demonstrating the potential of deep learning in early disease detection.
- Gait Prediction Using EMG and Video Data: This interdisciplinary project integrated AI with Raspberry Pi and a TENS unit, allowing for real-time gait movement response, a concept with applications in rehabilitation and prosthetics.
Journal Demos: Expanding AI Horizons
To expose students to a broad spectrum of AI advancements, each student participated in a journal demo, where they presented a five-minute summary of an AI-related research paper of their choice. This activity encouraged students to explore cutting-edge topics beyond the course curriculum, including reinforcement learning, generative AI, and AI ethics.
This approach not only enhanced their research skills but also improved their ability to distill complex AI concepts and communicate them effectively—a crucial skill in both academia and industry. The open discussion on the papers during the class time really improved student engagement.
GitHub Integration: Preparing for Real-World AI Careers
In today’s AI and machine learning (ML) landscape, version control and collaborative coding are essential skills. To prepare students for real-world careers, both courses incorporated GitHub integration, teaching students best practices for code management, collaboration, and open-source contributions.
By working with GitHub, students gained experience with essential tools used by AI practitioners, reinforcing software engineering principles alongside AI development.
Impact on Student Learning and Career Readiness
By combining hands-on projects, research exploration, and industry-standard tools, these courses provided a comprehensive learning experience that goes beyond traditional lectures.
Students gained:
- Practical AI skills through real-world projects.
- Exposure to diverse AI topics through journal presentations.
- Industry preparedness through GitHub integration and project-based collaboration.
This holistic approach ensures that graduates are well-equipped to contribute to AI-driven innovations in biomedical engineering, applied sciences, and beyond.
Conclusion
The integration of AI into engineering education at UNC Charlotte reflects a commitment to interdisciplinary learning and real-world application.