Collaborative learning and skill development for educational growth of artificial intelligence: A systematic review

IF 2.4 Q1 EDUCATION & EDUCATIONAL RESEARCH Contemporary Educational Technology Pub Date : 2023-01-01 DOI:10.30935/cedtech/13123
Andrés F. Mena-Guacas, Jairo Alonso Urueña Rodríguez, David Mauricio Santana Trujillo, José Gómez-Galán, Eloy López-Meneses
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引用次数: 1

Abstract

The diversity of topics in education makes it difficult for artificial intelligence (AI) to address them all in depth. Therefore, guiding to focus efforts on specific issues is essential. The analysis of competency development by fostering collaboration should be one of them because competencies are the way to validate that the educational exercise has been successful and because collaboration has proven to be one of the most effective strategies to improve performance outcomes. This systematic review analyzes the relationship between AI, competency development, and collaborative learning (CL). PRISMA methodology is used with data from the SCOPUS database. A total of 1,233 articles were found, and 30 passed the inclusion and exclusion criteria. The analysis of the selected articles identified three categories that deserve attention: the objects of study, the way of analyzing the results, and the types of AI that could be used. In this way, it has been possible to determine the relationship offered by the studies between skill development and CL and ideas about AI’s contributions to this field. Overall, however, the data from this systematic review suggest that, although AI has great potential to improve education, it should be approached with caution. More research is needed to fully understand its impact and how best to apply this technology in the classroom, minimizing its drawbacks, which may be relevant, and making truly effective and productive use of it.
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人工智能教育成长中的协作学习和技能发展:系统综述
教育主题的多样性使得人工智能(AI)很难深入解决所有这些问题。因此,引导工作集中在具体问题上是必要的。通过促进合作来分析能力发展应该是其中之一,因为能力是验证教育实践是否成功的方法,因为合作已被证明是提高绩效结果的最有效策略之一。本文系统分析了人工智能、能力发展和协作学习之间的关系。PRISMA方法与SCOPUS数据库中的数据一起使用。共发现1233篇文章,其中30篇通过了纳入和排除标准。对所选文章的分析确定了值得注意的三个类别:研究对象、分析结果的方式和可以使用的人工智能类型。这样,就有可能确定技能发展与CL之间的关系,以及关于AI对这一领域的贡献的想法。然而,总的来说,这项系统综述的数据表明,尽管人工智能在改善教育方面有很大的潜力,但我们应该谨慎对待。需要更多的研究来充分了解它的影响,以及如何最好地在课堂上应用这项技术,最大限度地减少它的缺点,这可能是相关的,并真正有效和富有成效地利用它。
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来源期刊
Contemporary Educational Technology
Contemporary Educational Technology Social Sciences-Education
CiteScore
6.20
自引率
0.00%
发文量
55
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