Hyacinth macaw: a project-based learning program to develop talents in Software Engineering for Artificial Intelligence

Abstract

Software Engineering for Artificial Intelligence (SE4A) uses SE principles to design and maintain AI systems, requiring analytical thinking for software complexity, while AI demands mathematical knowledge and algorithm adjustment. The IEEE Curriculum Guidelines for Undergraduate Degree Programs in Software Engineering states that extracurricular elements impact students’ preparation. This study focuses on the first module of a project-based learning talent development program involving undergraduate students, two expert professors (in AI and SE), and mentors from sponsoring companies. An exploratory case study with 39 students from four courses was conducted, challenging them to deliver an MVP in machine learning within 1.5 months. Results showed high agreement (87.5%) in applying learned skills to future projects, recognizing SE’s benefits (96.9%) in AI, and acknowledging the connection between SE and AI (78.1%). Participants applied relevant knowledge in ML performance, data analysis, and software architecture for AI. We share strategies used by students to enhance developer experience.