Dataset of 23 publicly available U.S. AI-assisted software engineering course syllabi and materials¶
Product or Programme · All products and programmes
Evidence · 3 claims (3 for) · 1 study (1 qualitative),q2· 0 of 1 report an effect size · 3 claims rest on one study
Description¶
A dataset compiled by Francis Geng et al. containing publicly available syllabi and course materials from 23 U.S.-based upper-division software engineering courses with explicit, required generative-AI use in graded tasks.
Components¶
Claims¶
- Among AI-assisted software engineering courses with public objectives, human-AI collaboration, tool development, and evaluation are the most common learning-objective themes [+W]
- Only 9 of 23 courses publicly named AI tools, with Claude Code, Cursor, and GitHub Copilot most frequent and a fragmented long tail [+W]
- AI/ML fundamentals, agentic development, prompting, and testing are the most commonly documented topics in AI-assisted software engineering courses [+W]
Related Products and Programmes¶
Key Sources¶
- Francis Geng, Anshul Shah, Mia Chen, Paul Denny, Juho Leinonen, Bill Griswold, Gerald Soosai Raj, and Leo Porter. (2026). Mapping the Emerging Curriculum for AI-Assisted Software Engineering via Syllabus Analysis. https://arxiv.org/abs/2608.05898v1
Page metadata
| Field | Value |
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type |
product |
id |
dataset-of-23-publicly-available-us-ai-assisted-software-engineering-course-syllabi-and-ma |
title |
Dataset of 23 publicly available U.S. AI-assisted software engineering course syllabi and materials |
description |
A dataset compiled by Francis Geng et al. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
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