---
title: "SIGCSE (A): papers and universities"
description: "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)."
url: https://scholar4ai.com/proceedings/sigcse
markdown_url: https://scholar4ai.com/proceedings/sigcse.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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… (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# 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.

All conferences: https://scholar4ai.com/proceedings.md

## 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)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 524 | 519 |
| 2025 | 496 | 493 |
| 2026 | 530 | 525 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4363608709](https://openalex.org/S4363608709) | The conference's proceedings |

## Who publishes there

*Institutions with the most SIGCSE papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | University of Illinois Urbana-Champaign | 78 |
| 2 | University of California, Berkeley | 78 |
| 3 | Georgia Institute of Technology | 55 |
| 4 | North Carolina State University | 53 |
| 5 | University of Washington | 47 |
| 6 | University of North Carolina at Charlotte | 44 |
| 7 | University of Florida | 40 |
| 8 | University of California San Diego | 38 |
| 9 | Northeastern University | 37 |
| 10 | Rochester Institute of Technology | 35 |

*Countries with the most SIGCSE papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | United States | 1,322 |
| 2 | Canada | 88 |
| 3 | Australia | 30 |
| 4 | Germany | 25 |
| 5 | New Zealand | 23 |
| 6 | Finland | 22 |
| 7 | India | 20 |
| 8 | United Kingdom | 17 |
| 9 | Brazil | 14 |
| 10 | Japan | 14 |

*Keywords with the most SIGCSE papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | computer science education | 337 |
| 2 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 170 |
| 3 | programming education | 170 |
| 4 | computing education | 111 |
| 5 | student engagement | 97 |
| 6 | introductory programming | 86 |
| 7 | introductory computer science | 79 |
| 8 | computational thinking | 78 |
| 9 | CS1 | 69 |
| 10 | curriculum development | 65 |

## Most-cited papers

*Most-cited SIGCSE papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 188 | [Prompt Problems: A New Programming Exercise for the Generative AI Era](https://doi.org/10.1145/3626252.3630909) | 2024-03-07 | Paul C. Denny, Juho Leinonen, James E. Prather et al. |
| 2 | 186 | [Teaching CS50 with AI: Leveraging Generative Artificial Intelligence in Computer Science Education](https://doi.org/10.1145/3626252.3630938) | 2024-03-07 | Rongxin Liu, Carter Zenke, C. Liu et al. |
| 3 | 144 | [Trust in Generative AI among Students: An exploratory study](https://doi.org/10.1145/3626252.3630842) | 2024-03-07 | Matin Amoozadeh, David Daniels, Daye Nam et al. |
| 4 | 96 | [Large Language Models in Computer Science Education: A Systematic Literature Review](https://doi.org/10.1145/3641554.3701863) | 2025-02-12 | Md Nishat Raihan, Mohammed Latif Siddiq, Joanna C. S. Santos et al. |
| 5 | 81 | [Teaching AI to K-12 Learners: Lessons, Issues, and Guidance](https://doi.org/10.1145/3626252.3630937) | 2024-03-07 | Shuchi Grover |
| 6 | 70 | [Software Engineering Education Must Adapt and Evolve for an LLM Environment](https://doi.org/10.1145/3626252.3630927) | 2024-03-07 | Vassilka D. Kirova, Cyril S. Ku, Joseph R. Laracy et al. |
| 7 | 62 | [Use of AI-driven Code Generation Models in Teaching and Learning Programming: a Systematic Literature Review](https://doi.org/10.1145/3626252.3630958) | 2024-03-07 | Doga Cambaz, Xiaoling Zhang |
| 8 | 61 | [ChatGPT in the Classroom: An Analysis of Its Strengths and Weaknesses for Solving Undergraduate Computer Science Questions](https://doi.org/10.1145/3626252.3630803) | 2024-03-07 | Ishika Joshi, Ritvik Budhiraja, Harshal Dev et al. |
| 9 | 60 | [Detecting ChatGPT-Generated Code Submissions in a CS1 Course Using Machine Learning Models](https://doi.org/10.1145/3626252.3630826) | 2024-03-07 | Muntasir Hoq, Yang Shi, Juho Leinonen et al. |
| 10 | 59 | [Beyond Traditional Teaching: Large Language Models as Simulated Teaching Assistants in Computer Science](https://doi.org/10.1145/3626252.3630789) | 2024-03-07 | Mengqi Liu, Faten Mhiri |

## 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

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).