Toward Artificial Intelligence-Human Paired Programming: A Review of the Educational Applications and Research on Artificial Intelligence Code-Generation Tools

IF 4 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Educational Computing Research Pub Date : 2024-04-04 DOI:10.1177/07356331241240460

Jiangyue Liu, Siran Li

{"title":"Toward Artificial Intelligence-Human Paired Programming: A Review of the Educational Applications and Research on Artificial Intelligence Code-Generation Tools","authors":"Jiangyue Liu, Siran Li","doi":"10.1177/07356331241240460","DOIUrl":null,"url":null,"abstract":"Pair Programming is considered an effective approach to programming education, but the synchronous collaboration of two programmers involves complex coordination, making this method difficult to be widely adopted in educational settings. Artificial Intelligence (AI) code-generation tools have outstanding capabilities in program generation and natural language understanding, creating conducive conditions for pairing with humans in programming. Now some more mature tools are gradually being implemented. This review summarizes the current status of educational applications and research on AI-assisted programming technology. Through thematic coding of literature, existing research focuses on five aspects: underlying technology and tool introduction, performance evaluation, the potential impacts and coping strategies, exploration of behavioral patterns in technological application, and ethical and safety issues. A systematic analysis of current literature provides the following insights for future academic research related to the practice of “human-machine pairing” in programming: (1) Affirming the value of AI code-generation tools while also clearly defining their technical limitations and ethical risks; (2) Developing adaptive teaching ecosystems and educational models, conducting comprehensive empirical research to explore the efficiency mechanisms of AI-human paired programming; (3) Further enriching the application of research methods by integrating speculative research with empirical research, combining traditional methods with emerging technologies.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"24 1","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Educational Computing Research","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1177/07356331241240460","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}

引用次数: 0

Abstract

Pair Programming is considered an effective approach to programming education, but the synchronous collaboration of two programmers involves complex coordination, making this method difficult to be widely adopted in educational settings. Artificial Intelligence (AI) code-generation tools have outstanding capabilities in program generation and natural language understanding, creating conducive conditions for pairing with humans in programming. Now some more mature tools are gradually being implemented. This review summarizes the current status of educational applications and research on AI-assisted programming technology. Through thematic coding of literature, existing research focuses on five aspects: underlying technology and tool introduction, performance evaluation, the potential impacts and coping strategies, exploration of behavioral patterns in technological application, and ethical and safety issues. A systematic analysis of current literature provides the following insights for future academic research related to the practice of “human-machine pairing” in programming: (1) Affirming the value of AI code-generation tools while also clearly defining their technical limitations and ethical risks; (2) Developing adaptive teaching ecosystems and educational models, conducting comprehensive empirical research to explore the efficiency mechanisms of AI-human paired programming; (3) Further enriching the application of research methods by integrating speculative research with empirical research, combining traditional methods with emerging technologies.

查看原文本刊更多论文

迈向人工智能-人类配对编程：人工智能代码生成工具的教育应用与研究综述

结对编程被认为是一种有效的编程教育方法，但两名程序员的同步协作涉及复杂的协调工作，因此这种方法难以在教育环境中广泛采用。人工智能（AI）代码生成工具在程序生成和自然语言理解方面能力突出，为与人类结对编程创造了有利条件。目前，一些较为成熟的工具正在逐步实现。本综述总结了人工智能辅助编程技术的教育应用和研究现状。通过对文献进行主题编码，现有研究主要集中在五个方面：底层技术和工具介绍、性能评估、潜在影响和应对策略、技术应用中的行为模式探索以及伦理和安全问题。通过对现有文献的系统分析，为未来编程 "人机配对 "实践的相关学术研究提供了以下启示：（1）在肯定人工智能代码生成工具价值的同时，明确其技术局限性和伦理风险；（2）开发适应性教学生态系统和教育模式，开展综合实证研究，探索人工智能与人机配对编程的效率机制；（3）进一步丰富研究方法的应用，将推测研究与实证研究相结合，将传统方法与新兴技术相结合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Educational Computing Research EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

11.90

自引率

6.20%

发文量

期刊介绍： The goal of this Journal is to provide an international scholarly publication forum for peer-reviewed interdisciplinary research into the applications, effects, and implications of computer-based education. The Journal features articles useful for practitioners and theorists alike. The terms "education" and "computing" are viewed broadly. “Education” refers to the use of computer-based technologies at all levels of the formal education system, business and industry, home-schooling, lifelong learning, and unintentional learning environments. “Computing” refers to all forms of computer applications and innovations - both hardware and software. For example, this could range from mobile and ubiquitous computing to immersive 3D simulations and games to computing-enhanced virtual learning environments.