mlsanityData to 7 Oct 2026

"ChatGPT Is Here to Help, Not to Replace Anybody" -- An Evaluation of Students' Opinions On Integrating ChatGPT In CS Courses

arXiv 2404.17443 ↗First posted 26 Apr 2024cs.ET cs.AI cs.HCcited by 6

Not on the map. Why?

Abstract (arXiv)

Large Language Models (LLMs) like GPT and Bard are capable of producing code based on textual descriptions, with remarkable efficacy. Such technology will have profound implications for computing education, raising concerns about cheating, excessive dependence, and a decline in computational thinking skills, among others. There has been extensive research on how teachers should handle this challenge but it is also important to understand how students feel about this paradigm shift. In this research, 52 first-year CS students were surveyed in order to assess their views on technologies with code-generation capabilities, both from academic and professional perspectives. Our findings indicate that while students generally favor the academic use of GPT, they don't over rely on it, only mildly asking for its help. Although most students benefit from GPT, some struggle to use it effectively, urging the need for specific GPT training. Opinions on GPT's impact on their professional lives vary, but there is a consensus on its importance in academic practice.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 33 references that are in the map. First posted on arXiv 26 Apr 2024.

Builds on

17 of 33 references.

References · 17

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022A Systematic Evaluation of Large Language Models of Codecited by 360
  3. 2023How is ChatGPT's behavior changing over time?cited by 265
  4. 2023Better Zero-Shot Reasoning with Role-Play Promptingcited by 220
  5. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
  6. 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 85
  7. 2023CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classescited by 71
  8. 2023Comparing Code Explanations Created by Students and Large Language Modelscited by 63
  9. 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmerscited by 62
  10. 2023Exploring the Responses of Large Language Models to Beginner Programmers' Help Requestscited by 44
  11. 2023Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Coursescited by 38
  12. 2023Prompt Problems: A New Programming Exercise for the Generative AI Eracited by 31
5 more
  1. 2023Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?cited by 30
  2. 2023Generative AI in Computing Education: Perspectives of Students and Instructorscited by 17
  3. 2023Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generatorscited by 16
  4. 2023A Preliminary Analysis on the Code Generation Capabilities of GPT-3.5 and Bard AI Models for Java Functionscited by 8
  5. 2024LLMs Still Can't Avoid Instanceof: An Investigation Into GPT-3.5, GPT-4 and Bard's Capacity to Handle Object-Oriented Programming Assignmentscited by 6

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Cited by year

Citing papers per year: 2024: 4; 2025: 2.

6 papers.