2025 ASEE Annual Conference & Exposition

In today's fast-paced and ever-evolving job market, students often struggle with course selection, finding it difficult to align their academic paths with the skills required by industries. This gap between education and employment readiness poses a significant challenge, particularly for students aiming to enter the workforce in industry or national laboratories. To address this, we propose developing an AI-driven course recommendation tool focused on the Electrical and Computer Engineering (ECE) domain, designed to bridge this gap by empowering students with data-driven guidance that improves career alignment.

The tool leverages machine learning (ML) algorithms to analyze job postings from industry careers pages, extracting key trends and skills currently in demand. Using keywords associated with various ECE subfields—such as controls, power electronics, and power systems—, the system will provide students with a curated list of recommended courses corresponding to these market demands. Additionally, the tool will incorporate student input, allowing them to tailor recommendations based on their interests and career aspirations. This dual input system ensures that course recommendations are aligned with real-world opportunities and personalized to individual student goals.

This is a work in progress, henceforth, as the tool evolves, it could also include more specializations and certifications offered by the university, allowing students and professionals at different stages of their careers to receive tailored guidance. Furthermore, the tool can adapt to the unique strengths and specializations of individual institutions, ensuring that the recommendations are market-driven and reflective of the best academic resources available at the student's university. By integrating university-specific course catalogs, the tool enhances its relevance to students while maintaining adaptability to changing industry demands and local job markets.

By combining market analysis with personalized learning paths, the tool is designed to enhance students' employability by preparing them for the specific demands of their desired industries. The use of AI/ML not only allows for scalable, real-time data processing but also provides an adaptive framework that evolves alongside changing market needs. This innovation addresses a critical need in engineering education by helping students make more strategic, informed decisions about their academic preparation. Furthermore, by fostering closer alignment between education and employment, this tool supports universities' mission to produce career-ready graduates, ultimately contributing to a more responsive and adaptable educational system.