Scholar4AI Updated 2026-11-02 12:20 UTC+09:00

SIGCSE: ACM Special Interest Group on Computer Science Education Conference — papers and institutions, 2024–2026

Papers of SIGCSE, ACM Special Interest Group on Computer Science Education Conference (CORE rank A; field: Human-centred computing), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02. Also as Markdown.

Share: X LinkedIn Bluesky Threads Facebook Reddit Hacker News Hatena LINE Email
Copy:

← All conferences

Key findings#

  • SIGCSE (ACM Special Interest Group on Computer Science Education Conference) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,550 of its papers from 2024–2026 (524 in 2024, 496 in 2025, 530 in 2026).
  • By the countries of the authors' institutions, the most papers come from the United States (1,322), Canada (88) and Australia (30).
  • The institutions with the most papers are University of Illinois Urbana-Champaign (78), University of California, Berkeley (78) and Georgia Institute of Technology (55).
  • Its most-cited paper of these years is “Prompt Problems: A New Programming Exercise for the Generative AI Era” (2024), with 188 citations.

Papers per year#

SIGCSE papers per publication year in OpenAlex (–: not in OpenAlex yet)
YearPapersWith affiliations
2024524519
2025496493
2026530525

Sources#

Where OpenAlex lists the papers
OpenAlex sourceWhich papers
S4363608709The conference's proceedings

Who publishes there#

Institutions with the most SIGCSE papers, 2024–2026
#InstitutionPapers
1University of Illinois Urbana-Champaign78
2University of California, Berkeley78
3Georgia Institute of Technology55
4North Carolina State University53
5University of Washington47
6University of North Carolina at Charlotte44
7University of Florida40
8University of California San Diego38
9Northeastern University37
10Rochester Institute of Technology35
Countries with the most SIGCSE papers, 2024–2026
#CountryPapers
1United States1,322
2Canada88
3Australia30
4Germany25
5New Zealand23
6Finland22
7India20
8United Kingdom17
9Brazil14
10Japan14
Keywords with the most SIGCSE papers, 2024–2026
#KeywordPapers
1computer science education337
2large language models170
3programming education170
4computing education111
5student engagement97
6introductory programming86
7introductory computer science79
8computational thinking78
9CS169
10curriculum development65

Most-cited papers#

Most-cited SIGCSE papers of 2024–2026 (citations counted by OpenAlex)
#CitationsTitlePublishedAuthors
1188Prompt Problems: A New Programming Exercise for the Generative AI Era2024-03-07Paul C. Denny, Juho Leinonen, James E. Prather et al.
2186Teaching CS50 with AI: Leveraging Generative Artificial Intelligence in Computer Science Education2024-03-07Rongxin Liu, Carter Zenke, C. Liu et al.
3144Trust in Generative AI among Students: An exploratory study2024-03-07Matin Amoozadeh, David Daniels, Daye Nam et al.
496Large Language Models in Computer Science Education: A Systematic Literature Review2025-02-12Md Nishat Raihan, Mohammed Latif Siddiq, Joanna C. S. Santos et al.
581Teaching AI to K-12 Learners: Lessons, Issues, and Guidance2024-03-07Shuchi Grover
670Software Engineering Education Must Adapt and Evolve for an LLM Environment2024-03-07Vassilka D. Kirova, Cyril S. Ku, Joseph R. Laracy et al.
762Use of AI-driven Code Generation Models in Teaching and Learning Programming: a Systematic Literature Review2024-03-07Doga Cambaz, Xiaoling Zhang
861ChatGPT in the Classroom: An Analysis of Its Strengths and Weaknesses for Solving Undergraduate Computer Science Questions2024-03-07Ishika Joshi, Ritvik Budhiraja, Harshal Dev et al.
960Detecting ChatGPT-Generated Code Submissions in a CS1 Course Using Machine Learning Models2024-03-07Muntasir Hoq, Yang Shi, Juho Leinonen et al.
1059Beyond Traditional Teaching: Large Language Models as Simulated Teaching Assistants in Computer Science2024-03-07Mengqi Liu, Faten Mhiri

Conferences in the same field#

CHI · HRI · VR · UIST · SIGGRAPH Asia · ISMAR · SIGGRAPH · PerCom · DIS · CSCW · AIED · ASSETS · IEEE VIS · IUI · UMAP · LAK · ICER

Cite this page

Scholar4AI. (2026). SIGCSE: ACM Special Interest Group on Computer Science Education Conference — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/sigcse

@misc{scholar4ai-proceedings-sigcse,
  author = {{Scholar4AI}},
  title = {SIGCSE: ACM Special Interest Group on Computer Science Education Conference — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/sigcse}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).