{"title":"Levering AI to enhance students' conceptual understanding and confidence in mathematics","authors":"Allan Mesa Canonigo","doi":"10.1111/jcal.13065","DOIUrl":null,"url":null,"abstract":"MotivationThis research investigates the transformative impact of integrating AI into mathematics education, aiming to enhance students' conceptual understanding and self‐efficacy. It addresses the crucial need for innovative teaching methods in response to contemporary challenges in education and aims to fill gaps in understanding the potential drawbacks and benefits of AI implementation.ObjectivesThe primary goal of this study is to investigate the effects of AI tools, such as GeoGebra and ChatGPT, on students' conceptual understanding and self‐efficacy in a mathematics classroom setting, with a focus on collaborative learning, teacher‐led discussions, and problem‐solving.MethodsEmploying a mixed‐methods approach, the research involves pre‐ and post‐implementation surveys, supplemented by qualitative data from interviews, focus groups, and classroom observations. The study emphasises the utilisation of AI in collaborative learning environments and explores the challenges and opportunities associated with its integration.Results and ConclusionsThe study reveals a noteworthy enhancement in students' conceptual understanding and self‐efficacy belief when AI tools are incorporated into mathematics education. However, it highlights concerns such as technological challenges, potential teacher replacement fears, biases in AI systems, and difficulties related to pacing and social interaction. To optimise the use of AI, the paper suggests strategies like adaptive learning platforms, real‐world simulations, intelligent tutoring systems, and data‐driven instruction, positioning AI as a complement rather than a substitute for traditional teaching methods. The study recommends addressing challenges through additional support resources and transparent communication, emphasising the need for a thoughtful integration of AI in education.","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Assisted Learning","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1111/jcal.13065","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
MotivationThis research investigates the transformative impact of integrating AI into mathematics education, aiming to enhance students' conceptual understanding and self‐efficacy. It addresses the crucial need for innovative teaching methods in response to contemporary challenges in education and aims to fill gaps in understanding the potential drawbacks and benefits of AI implementation.ObjectivesThe primary goal of this study is to investigate the effects of AI tools, such as GeoGebra and ChatGPT, on students' conceptual understanding and self‐efficacy in a mathematics classroom setting, with a focus on collaborative learning, teacher‐led discussions, and problem‐solving.MethodsEmploying a mixed‐methods approach, the research involves pre‐ and post‐implementation surveys, supplemented by qualitative data from interviews, focus groups, and classroom observations. The study emphasises the utilisation of AI in collaborative learning environments and explores the challenges and opportunities associated with its integration.Results and ConclusionsThe study reveals a noteworthy enhancement in students' conceptual understanding and self‐efficacy belief when AI tools are incorporated into mathematics education. However, it highlights concerns such as technological challenges, potential teacher replacement fears, biases in AI systems, and difficulties related to pacing and social interaction. To optimise the use of AI, the paper suggests strategies like adaptive learning platforms, real‐world simulations, intelligent tutoring systems, and data‐driven instruction, positioning AI as a complement rather than a substitute for traditional teaching methods. The study recommends addressing challenges through additional support resources and transparent communication, emphasising the need for a thoughtful integration of AI in education.
期刊介绍：
The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope
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