Teaching Software Engineering for AI-Enabled Systems

TL;DR

This paper presents a new course designed to teach software engineering principles tailored for AI-enabled systems, emphasizing real-world challenges like data variability, robustness, and ethical considerations.

Contribution

It introduces a novel curriculum that integrates software engineering practices with AI and ML, focusing on practical, realistic system development and ethical issues.

Findings

Successful first offering of the course with positive student feedback

Course materials and infrastructure shared for wider adoption

Enhanced understanding of engineering challenges in AI systems

Abstract

Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, responsive and robust, even when built on unreliable components. Systems with artificial-intelligence or machine-learning (ML) components raise new challenges and require careful engineering. We designed a new course to teach software-engineering skills to students with a background in ML. We specifically go beyond traditional ML courses that teach modeling techniques under artificial conditions and focus, in lecture and assignments, on realism with large and changing datasets, robust and evolvable infrastructure, and purposeful requirements engineering that considers ethics and fairness as well. We describe the course and our infrastructure and share experience and all material from teaching the course for the first time.