Teaching Machine Learning to Software Engineers

2026-06-12

AI Summary

This study addresses the widespread absence of systematic instruction on building, testing, deploying, and maintaining AI/ML systems in current undergraduate software engineering (SE) curricula. It presents the first comprehensive delineation of core AI/ML topics essential for SE practice, integrating curriculum mapping analysis, instructor needs surveys, and structured modeling to identify critical content gaps in existing programs. Grounded in empirical evidence, the work proposes actionable pathways for embedding high-priority AI/ML themes into established SE courses. The resulting framework offers a practical, implementable guide to enhance SE education’s capacity to support the development of intelligent software systems.

Abstract

Machine learning (ML) and Artificial Intelligence (AI) components are increasingly embedded in software products, yet undergraduate software engineering (SE) curricula rarely provide systematic preparation for building, testing, deploying, and maintaining AI/ML-based software systems. This paper aims to provide evidence-based guidance for integrating AI/MLrelevant content into core SE education. We compile and define a structured inventory of topics relevant to SE practice in AI/MLbased software, then map these topics against required courses in a set of representative SE curricula to identify coverage gaps. To assess educational priorities and feasibility, we survey SE instructors on topic importance and integration constraints. Based on the crosswalk between topic definitions, curriculum coverage, and instructor prioritization, we derive a guideline that recommends where and how high-priority topics can be embedded within existing SE courses.

Problem

• Machine Learning
• Software Engineering Education
• AI/ML-based Software
• Curriculum Integration
• Undergraduate Education

Innovation

• AI/ML integration
• software engineering education
• curriculum mapping
• topic inventory
• evidence-based guideline