mlsanityData to 7 Oct 2026

How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Course

arXiv 2407.20792 ↗First posted 30 Jul 2024cs.AIcited by 11

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Abstract (arXiv)

This research paper contributes to the computing education research community's understanding of Generative AI (GenAI) in the context of introductory programming, and specifically, how students utilize related tools, such as ChatGPT. An increased understanding of students' use is mandatory for educators and higher education institutions, as GenAI is here to stay, and its performance is likely to improve rapidly in the near future. Learning about students' use patterns is not only crucial to support their learning, but to develop adequate forms of instruction and assessment. With the rapid advancement of AI, its broad availability, and ubiquitous presence in educational environments, elaborating how AI can enhance learning experiences, especially in courses such as introductory programming is important. To date, most studies have focused on the educator's perspective on GenAI, its performance, characteristics, and limitations. However, the student perspective, and how they actually use GenAI tools in course contexts, has not been subject to a great number of studies. Therefore, this study is guided by the following research questions: (1) What do students report on their use pattern of ChatGPT in the context of introductory programming exercises? and (2) How do students perceive ChatGPT in the context of introductory programming exercises? To address these questions, computing students at a large German university were asked to solve programming tasks with the assistance of ChatGPT as part of their introductory programming course. Students (n=298) provided information regarding the use of ChatGPT, and their evaluation of the tool via an online survey. This research provides a comprehensive evaluation of ChatGPT-3.5's application by novice programmers in a higher education context...

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 27 of its 61 references that are in the map. First posted on arXiv 30 Jul 2024.

Builds on

27 of 61 references.

References · 27

  1. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
  2. 2022Automatic Generation of Programming Exercises and Code Explanations using Large Language Modelscited by 124
  3. 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 85
  4. 2022Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Bookcited by 84
  5. 2024CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needscited by 81
  6. 2022Using Large Language Models to Enhance Programming Error Messagescited by 67
  7. 2023Comparing Code Explanations Created by Students and Large Language Modelscited by 63
  8. 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmerscited by 62
  9. 2023Generating High-Precision Feedback for Programming Syntax Errors using Large Language Modelscited by 34
  10. 2023Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Codecited by 31
  11. 2022Repairing Bugs in Python Assignments Using Large Language Modelscited by 28
  12. 2023Exploring the Potential of Large Language Models to Generate Formative Programming Feedbackcited by 28
15 more
  1. 2023Next-Step Hint Generation for Introductory Programming Using Large Language Modelscited by 20
  2. 2023Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbotscited by 18
  3. 2023Generative AI in Computing Education: Perspectives of Students and Instructorscited by 17
  4. 2023Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generatorscited by 16
  5. 2023Trust in Generative AI among students: An Exploratory Studycited by 16
  6. 2023Can Language Models Employ the Socratic Method? Experiments with Code Debuggingcited by 14
  7. 2024Feedback-Generation for Programming Exercises With GPT-4cited by 14
  8. 2023ChatGPT in the Classroom: An Analysis of Its Strengths and Weaknesses for Solving Undergraduate Computer Science Questionscited by 13
  9. 2023Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Modelscited by 11
  10. 2023Large Language Models in Introductory Programming Education: ChatGPT's Performance and Implications for Assessmentscited by 10
  11. 2023Can ChatGPT Pass An Introductory Level Functional Language Programming Course?cited by 9
  12. 2023AI-enhanced Auto-correction of Programming Exercises: How Effective is GPT-3.5?cited by 8
  13. 2024Analyzing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPTcited by 8
  14. 2023Generative AI Perceptions: A Survey to Measure the Perceptions of Faculty, Staff, and Students on Generative AI Tools in Academiacited by 6
  15. 2024Student Perspectives on Using a Large Language Model (LLM) for an Assignment on Professional Ethicscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 3.

11 papers.