Generative AI and future education: a review, theoretical validation, and authors' perspective on challenges and solutions.

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE PeerJ Computer Science Pub Date : 2024-12-03 eCollection Date: 2024-01-01 DOI:10.7717/peerj-cs.2105
Wali Khan Monib, Atika Qazi, Rosyzie Anna Apong, Mohammad Tazli Azizan, Liyanage De Silva, Hayati Yassin
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Abstract

Generative AI (Gen AI), exemplified by ChatGPT, has witnessed a remarkable surge in popularity recently. This cutting-edge technology demonstrates an exceptional ability to produce human-like responses and engage in natural language conversations guided by context-appropriate prompts. However, its integration into education has become a subject of ongoing debate. This review examines the challenges of using Gen AI like ChatGPT in education and offers effective strategies. To retrieve relevant literature, a search of reputable databases was conducted, resulting in the inclusion of twenty-two publications. Using Atlas.ti, the analysis reflected six primary challenges with plagiarism as the most prevalent issue, closely followed by responsibility and accountability challenges. Concerns were also raised about privacy, data protection, safety, and security risks, as well as discrimination and bias. Additionally, there were challenges about the loss of soft skills and the risks of the digital divide. To address these challenges, a number of strategies were identified and subjected to critical evaluation to assess their practicality. Most of them were practical and align with the ethical and pedagogical theories. Within the prevalent concepts, "ChatGPT" emerged as the most frequent one, followed by "AI," "student," "research," and "education," highlighting a growing trend in educational discourse. Moreover, close collaboration was evident among the leading countries, all forming a single cluster, led by the United States. This comprehensive review provides implications, recommendations, and future prospects concerning the use of generative AI in education.

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生成式人工智能与未来教育:综述、理论验证以及作者对挑战和解决方案的看法。
以ChatGPT为代表的生成式人工智能(Gen AI)最近受到了极大的欢迎。这项尖端技术展示了一种特殊的能力,可以产生类似人类的反应,并在上下文适当的提示引导下进行自然语言对话。然而,它与教育的融合已经成为一个持续争论的话题。本文探讨了在教育中使用像ChatGPT这样的Gen AI所面临的挑战,并提供了有效的策略。为了检索相关文献,对知名数据库进行了搜索,结果纳入了22份出版物。使用阿特拉斯。该分析反映了六大主要挑战,其中抄袭是最普遍的问题,紧随其后的是责任和问责制挑战。人们还对隐私、数据保护、安全和安保风险以及歧视和偏见表示担忧。此外,软技能的流失和数字鸿沟的风险也带来了挑战。为了应对这些挑战,我们确定了一些战略,并对其进行了严格的评估,以评估其实用性。其中大多数是实用的,符合伦理和教学理论。在流行的概念中,“ChatGPT”是最常见的一个,其次是“AI”,“学生”,“研究”和“教育”,突显了教育话语的增长趋势。此外,主要国家之间的密切合作是显而易见的,它们都形成了一个以美国为首的集群。这篇全面的综述提供了关于在教育中使用生成式人工智能的影响、建议和未来前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
期刊最新文献
Design of a 3D emotion mapping model for visual feature analysis using improved Gaussian mixture models. Enhancing task execution: a dual-layer approach with multi-queue adaptive priority scheduling. LOGIC: LLM-originated guidance for internal cognitive improvement of small language models in stance detection. Generative AI and future education: a review, theoretical validation, and authors' perspective on challenges and solutions. MSR-UNet: enhancing multi-scale and long-range dependencies in medical image segmentation.
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