@inproceedings{pitts-etal-2025-survey,
title = "A Survey of {LLM}-Based Applications in Programming Education: Balancing Automation and Human Oversight",
author = "Pitts, Griffin and
Hridi, Anurata Prabha and
Lekshmi Narayanan, Arun Balajiee",
editor = "Blodgett, Su Lin and
Curry, Amanda Cercas and
Dev, Sunipa and
Li, Siyan and
Madaio, Michael and
Wang, Jack and
Wu, Sherry Tongshuang and
Xiao, Ziang and
Yang, Diyi",
booktitle = "Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP)",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.hcinlp-1.21/",
pages = "255--262",
ISBN = "979-8-89176-353-1",
abstract = "Novice programmers benefit from timely, personalized support that addresses individual learning gaps, yet the availability of instructors and teaching assistants is inherently limited. Large language models (LLMs) present opportunities to scale such support, though their effectiveness depends on how well technical capabilities are aligned with pedagogical goals. This survey synthesizes recent work on LLM applications in programming education across three focal areas: formative code feedback, assessment, and knowledge modeling. We identify recurring design patterns in how these tools are applied and find that interventions are most effective when educator expertise complements model output through human-in-the-loop oversight, scaffolding, and evaluation. Fully automated approaches are often constrained in capturing the pedagogical nuances of programming education, although human-in-the-loop designs and course-specific adaptation offer promising directions for future improvement. Future research should focus on improving transparency, strengthening alignment with pedagogy, and developing systems that flexibly adapt to the needs of varied learning contexts."
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<abstract>Novice programmers benefit from timely, personalized support that addresses individual learning gaps, yet the availability of instructors and teaching assistants is inherently limited. Large language models (LLMs) present opportunities to scale such support, though their effectiveness depends on how well technical capabilities are aligned with pedagogical goals. This survey synthesizes recent work on LLM applications in programming education across three focal areas: formative code feedback, assessment, and knowledge modeling. We identify recurring design patterns in how these tools are applied and find that interventions are most effective when educator expertise complements model output through human-in-the-loop oversight, scaffolding, and evaluation. Fully automated approaches are often constrained in capturing the pedagogical nuances of programming education, although human-in-the-loop designs and course-specific adaptation offer promising directions for future improvement. Future research should focus on improving transparency, strengthening alignment with pedagogy, and developing systems that flexibly adapt to the needs of varied learning contexts.</abstract>
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%0 Conference Proceedings
%T A Survey of LLM-Based Applications in Programming Education: Balancing Automation and Human Oversight
%A Pitts, Griffin
%A Hridi, Anurata Prabha
%A Lekshmi Narayanan, Arun Balajiee
%Y Blodgett, Su Lin
%Y Curry, Amanda Cercas
%Y Dev, Sunipa
%Y Li, Siyan
%Y Madaio, Michael
%Y Wang, Jack
%Y Wu, Sherry Tongshuang
%Y Xiao, Ziang
%Y Yang, Diyi
%S Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-353-1
%F pitts-etal-2025-survey
%X Novice programmers benefit from timely, personalized support that addresses individual learning gaps, yet the availability of instructors and teaching assistants is inherently limited. Large language models (LLMs) present opportunities to scale such support, though their effectiveness depends on how well technical capabilities are aligned with pedagogical goals. This survey synthesizes recent work on LLM applications in programming education across three focal areas: formative code feedback, assessment, and knowledge modeling. We identify recurring design patterns in how these tools are applied and find that interventions are most effective when educator expertise complements model output through human-in-the-loop oversight, scaffolding, and evaluation. Fully automated approaches are often constrained in capturing the pedagogical nuances of programming education, although human-in-the-loop designs and course-specific adaptation offer promising directions for future improvement. Future research should focus on improving transparency, strengthening alignment with pedagogy, and developing systems that flexibly adapt to the needs of varied learning contexts.
%U https://aclanthology.org/2025.hcinlp-1.21/
%P 255-262
Markdown (Informal)
[A Survey of LLM-Based Applications in Programming Education: Balancing Automation and Human Oversight](https://aclanthology.org/2025.hcinlp-1.21/) (Pitts et al., HCINLP 2025)
ACL