ChatGPT in Introductory Programming: Counterbalanced Evaluation of Code Quality, Conceptual Learning, and Student Perceptions
- URL: http://arxiv.org/abs/2510.00946v1
- Date: Wed, 01 Oct 2025 14:19:24 GMT
- Title: ChatGPT in Introductory Programming: Counterbalanced Evaluation of Code Quality, Conceptual Learning, and Student Perceptions
- Authors: Shiza Andleeb, Brandon Kantorski, Jeffrey C. Carver,
- Abstract summary: Large language models (LLMs) such as ChatGPT are increasingly used in introductory programming courses.<n>We investigated how ChatGPT access affects code quality, conceptual understanding, task completion times, and student perceptions in a CS1 course.
- Score: 0.5844783557050257
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Background: Large language models (LLMs) such as ChatGPT are increasingly used in introductory programming courses to provide real-time code generation, debugging, and explanations. While these tools can boost productivity and code quality, concerns remain about over-reliance and potential impacts on conceptual learning. Objective: To investigate how ChatGPT access affects code quality, conceptual understanding, task completion times, and student perceptions in a CS1 course. Methods: We conducted a counterbalanced, quasi-experimental study in which students alternated between ChatGPT and non-ChatGPT conditions across two programming assignments in C (functions and structures). We evaluated their code submissions using multidimensional rubrics, conceptual post-surveys, and task completion time. Results: Students who had access to ChatGPT produced significantly higher rubric scores for code quality and completed tasks in less time compared to those without access. However, gains in conceptual understanding were mixed, lower for the functions topic but higher for the structures topic. Students reported positive experiences with ChatGPT, citing its value for debugging and practice, while expressing concerns about accuracy and long-term skill development. Conclusions: ChatGPT can enhance code quality and efficiency for novice programmers, but may not uniformly improve conceptual understanding. Structured integration and complementary instructional strategies are recommended to foster independent problem-solving skills.
Related papers
- Can Large Language Models Help Students Prove Software Correctness? An Experimental Study with Dafny [75.55915044740566]
Students in computing education increasingly use large language models (LLMs) such as ChatGPT.<n>This paper investigates how students interact with an LLM when solving formal verification exercises in Dafny.
arXiv Detail & Related papers (2025-06-27T16:34:13Z) - Enhancing Programming Education with ChatGPT: A Case Study on Student Perceptions and Interactions in a Python Course [7.182952031323369]
This paper explores ChatGPT's impact on learning in a Python programming course tailored for first-year students over eight weeks.
By analyzing responses from surveys, open-ended questions, and student-ChatGPT dialog data, we aim to provide a comprehensive view of ChatGPT's utility.
Our study uncovers a generally positive reception toward ChatGPT and offers insights into its role in enhancing the programming education experience.
arXiv Detail & Related papers (2024-03-20T15:47:28Z) - Can ChatGPT Play the Role of a Teaching Assistant in an Introductory
Programming Course? [1.8197265299982013]
This paper explores the potential of using ChatGPT, an LLM, as a virtual Teaching Assistant (TA) in an introductory programming course.
We evaluate ChatGPT's capabilities by comparing its performance with that of human TAs in some of the important TA functions.
arXiv Detail & Related papers (2023-12-12T15:06:44Z) - Exploring ChatGPT's Capabilities on Vulnerability Management [56.4403395100589]
We explore ChatGPT's capabilities on 6 tasks involving the complete vulnerability management process with a large-scale dataset containing 70,346 samples.
One notable example is ChatGPT's proficiency in tasks like generating titles for software bug reports.
Our findings reveal the difficulties encountered by ChatGPT and shed light on promising future directions.
arXiv Detail & Related papers (2023-11-11T11:01:13Z) - Exploring the Potential of ChatGPT in Automated Code Refinement: An
Empirical Study [0.0]
ChatGPT, a cutting-edge language model, has demonstrated impressive performance in various natural language processing tasks.
We conduct the first empirical study to understand the capabilities of ChatGPT in code review tasks.
Our results show that ChatGPT achieves higher EM and BLEU scores of 22.78 and 76.44 respectively, while the state-of-the-art method achieves only 15.50 and 62.88 on a high-quality code review dataset.
arXiv Detail & Related papers (2023-09-15T07:41:33Z) - Can ChatGPT Pass An Introductory Level Functional Language Programming
Course? [2.3456295046913405]
This paper aims to explore how well ChatGPT can perform in an introductory-level functional language programming course.
Our comprehensive evaluation provides valuable insights into ChatGPT's impact from both student and instructor perspectives.
arXiv Detail & Related papers (2023-04-29T20:30:32Z) - ChatLog: Carefully Evaluating the Evolution of ChatGPT Across Time [54.18651663847874]
ChatGPT has achieved great success and can be considered to have acquired an infrastructural status.
Existing benchmarks encounter two challenges: (1) Disregard for periodical evaluation and (2) Lack of fine-grained features.
We construct ChatLog, an ever-updating dataset with large-scale records of diverse long-form ChatGPT responses for 21 NLP benchmarks from March, 2023 to now.
arXiv Detail & Related papers (2023-04-27T11:33:48Z) - Is ChatGPT the Ultimate Programming Assistant -- How far is it? [11.943927095071105]
ChatGPT has received great attention: it can be used as a bot for discussing source code.
We present an empirical study of ChatGPT's potential as a fully automated programming assistant.
arXiv Detail & Related papers (2023-04-24T09:20:13Z) - ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large
Language Models in Multilingual Learning [70.57126720079971]
Large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP)
This paper evaluates ChatGPT on 7 different tasks, covering 37 diverse languages with high, medium, low, and extremely low resources.
Compared to the performance of previous models, our extensive experimental results demonstrate a worse performance of ChatGPT for different NLP tasks and languages.
arXiv Detail & Related papers (2023-04-12T05:08:52Z) - Is ChatGPT a General-Purpose Natural Language Processing Task Solver? [113.22611481694825]
Large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot.
Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community.
It is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot.
arXiv Detail & Related papers (2023-02-08T09:44:51Z) - ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
In this paper, we frame the problem of providing feedback as few-shot classification.
A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors.
Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
arXiv Detail & Related papers (2021-07-23T22:41:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.