Only 9 of 23 courses publicly named AI tools, with Claude Code, Cursor, and GitHub Copilot most frequent and a fragmented long tail¶

Claim · All claims
Evidence · 1 study · qualitative r3 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Of 23 courses, 9 explicitly named one or more AI tools in public materials; 16 distinct tools were identified, with Claude Code appearing most frequently, followed by Cursor and GitHub Copilot. → Francis Geng 2026

Evidence¶

Francis Geng 2026¶

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/2608.05898 doi:10.48550/arxiv.2608.05898

q2 · i? · qualitative · r3

Direct extraction of tools explicitly named in public course materials across the 23-course dataset (Figure 1). Claude Code appeared in 6 courses, Cursor and GitHub Copilot in 3 each; most remaining tools appeared in only one course.

"Of the 23 courses, 9 explicitly named one or more AI tools in public course materials; across these courses, we identified 16 distinct tools (Figure 1)."

Discussion¶

Related Claims¶

• Among AI-assisted software engineering courses with public objectives, human-AI collaboration, tool development, and evaluation are the most common learning-objective themes — related
• Course syllabi name specific GenAI tools — ChatGPT in 90 of 98 — while institutional guidelines contain no code for specific tools — related
• In a narrative educator assessment, coding assistants (Cursor and Claude Code) produced the most accurate, complete, and pedagogically sound slides from textbook chapters — related