[2606.14618] Teaching Machine Learning to Software Engineers

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Computer Science > Software Engineering

arXiv:2606.14618 (cs)

[Submitted on 12 Jun 2026]

Title:Teaching Machine Learning to Software Engineers

Authors:Nafiseh Kahani, Jason Jaskolka<br>View a PDF of the paper titled Teaching Machine Learning to Software Engineers, by Nafiseh Kahani and Jason Jaskolka

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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.

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Software Engineering (cs.SE)

Cite as:<br>arXiv:2606.14618 [cs.SE]

(or<br>arXiv:2606.14618v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2606.14618

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Nafiseh Kahani [view email]<br>[v1]<br>Fri, 12 Jun 2026 16:42:34 UTC (2,419 KB)

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