用于教育反馈的生成式人工智能和多模态数据:来自具身数学学习的见解

IF 8.1 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Giulia Cosentino, Jacqueline Anton, Kshitij Sharma, Mirko Gelsomini, Michail Giannakos, Dor Abrahamson
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引用次数: 0

摘要

本研究探讨了生成式人工智能(GenAI)在儿童数字学习体验中提供形成性反馈的作用,特别是在数学教育的背景下。该研究使用多模态数据,将人工智能生成的反馈与人类教师的反馈进行比较,重点关注其对儿童学习成果的影响。儿童利用数字体尺数轴,通过具身互动学习正负整数的加减法。这项研究采用了组间设计,一组从人类教练那里得到反馈,另一组从GenAI那里得到反馈。使用眼动追踪数据和系统日志来评估学生的信息处理行为和认知负荷。结果表明,尽管任务型表现在两种条件之间没有显著差异,但GenAI反馈条件表现出较低的认知负荷,并且在两种条件下学生表现出不同的视觉信息加工策略。这些发现为GenAI的潜力提供了实证支持,它可以通过提供支持高效学习的结构化和适应性反馈来补充传统教学。该研究强调了混合智能方法的重要性,这种方法将人类和人工智能的反馈结合起来,通过协同反馈来增强学习。这项研究为旨在设计促进有效学习成果的人工智能-人类混合教育环境的教育工作者、开发人员和研究人员提供了有价值的见解。关于这个主题我们已经知道了什么?具身学习方法已被证明通过让学生接触学习材料来促进更深层次的认知加工,这对数学等抽象学科尤其有益。GenAI有可能通过个性化反馈来增强教育体验,这对于培养学生的理解和参与至关重要。先前的研究表明,将人工智能与人类教师相结合的混合智能有助于改善教育成果。这篇文章补充了什么?本研究实证检验了在多感官环境(MSE)下,基因人工智能生成的反馈与人类教师反馈在数学学习中的有效性。系统日志和眼动追踪分析的结果表明,GenAI反馈可以有效地支持学习,特别是帮助学生管理他们的认知负荷。研究发现,基因人工智能和教师反馈导致不同的信息加工策略。这些发现为反馈模式如何影响认知参与提供了可行的见解。将GenAI整合到教育环境中,为加强传统教学方法提供了机会,实现了利用人工智能和人类反馈优势的适应性学习环境。未来的教育实践应探索结合人工智能和人类反馈的混合模式,以创造包容和有效的学习体验,适应学习者的多样化需求。决策者应该建立指导方针和框架,以促进伦理和公平地采用基因人工智能技术进行学习。这包括解决信任、透明度和可及性问题,以确保GenAI系统有效地支持而不是取代人类教员。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Generative AI and multimodal data for educational feedback: Insights from embodied math learning

Generative AI and multimodal data for educational feedback: Insights from embodied math learning

Generative AI and multimodal data for educational feedback: Insights from embodied math learning

Generative AI and multimodal data for educational feedback: Insights from embodied math learning

This study explores the role of generative AI (GenAI) in providing formative feedback in children's digital learning experiences, specifically in the context of mathematics education. Using multimodal data, the research compares AI-generated feedback with feedback from human instructors, focusing on its impact on children's learning outcomes. Children engaged with a digital body-scale number line to learn addition and subtraction of positive and negative integers through embodied interaction. The study followed a between-group design, with one group receiving feedback from a human instructor and the other from GenAI. Eye-tracking data and system logs were used to evaluate student's information processing behaviour and cognitive load. The results revealed that while task-based performance did not differ significantly between conditions, the GenAI feedback condition demonstrated lower cognitive load and students show different visual information processing strategies among the two conditions. The findings provide empirical support for the potential of GenAI to complement traditional teaching by providing structured and adaptive feedback that supports efficient learning. The study underscores the importance of hybrid intelligence approaches that integrate human and AI feedback to enhance learning through synergistic feedback. This research offers valuable insights for educators, developers and researchers aiming to design hybrid AI-human educational environments that promote effective learning outcomes.

Practitioner notes

What is already known about this topic?

  • Embodied learning approaches have been shown to facilitate deeper cognitive processing by engaging students physically with learning materials, which is especially beneficial in abstract subjects like mathematics.
  • GenAI has the potential to enhance educational experiences through personalized feedback, making it crucial for fostering student understanding and engagement.
  • Previous research indicates that hybrid intelligence that combines AI with human instructors can contribute to improved educational outcomes.

What this paper adds?

  • This study empirically examines the effectiveness of GenAI-generated feedback when compared to human instructor feedback in the context of a multisensory environment (MSE) for math learning.
  • Findings from system logs and eye-tracking analysis reveal that GenAI feedback can support learning effectively, particularly in helping students manage their cognitive load.
  • The research uncovers that GenAI and teacher feedback lead to different information processing strategies. These findings provide actionable insights into how feedback modality influences cognitive engagement.

Implications for practice and/or policy

  • The integration of GenAI into educational settings presents an opportunity to enhance traditional teaching methods, enabling an adaptive learning environment that leverages the strengths of both AI and human feedback.
  • Future educational practices should explore hybrid models that incorporate both AI and human feedback to create inclusive and effective learning experiences, adapting to the diverse needs of learners.
  • Policymakers should establish guidelines and frameworks to facilitate the ethical and equitable adoption of GenAI technologies for learning. This includes addressing issues of trust, transparency and accessibility to ensure that GenAI systems are effectively supporting, rather than replacing, human instructors.
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来源期刊
British Journal of Educational Technology
British Journal of Educational Technology EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
15.60
自引率
4.50%
发文量
111
期刊介绍: BJET is a primary source for academics and professionals in the fields of digital educational and training technology throughout the world. The Journal is published by Wiley on behalf of The British Educational Research Association (BERA). It publishes theoretical perspectives, methodological developments and high quality empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.
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