RICE AlgebraBot:从设计和开发负责任的对话式人工智能中获得的经验教训,使用归纳、具体化和举例来支持代数学习

IF 23.4 Q1 Social Sciences Computers and Education Artificial Intelligence Pub Date : 2025-06-01 Epub Date: 2024-12-06 DOI:10.1016/j.caeai.2024.100338
Chenglu Li , Wanli Xing , Yukyeong Song , Bailing Lyu
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引用次数: 0

摘要

代数学习的重要性和挑战得到了广泛的认可,美国各地的学生都面临着由于这门学科的复杂性而带来的困难。虽然广泛的研究集中在提高K-12教育中的代数学习,但所采用的策略(例如人工干预和数字辅导平台)的可重用性、可扩展性和有效性仍然有限。由于大型语言模型(llm)的进步,会话人工智能(ConvAI)成为了自动、个性化和有效的学生支持的潜在工具。然而,围绕与ConvAI相关的多样性、安全性、情感和刻板印象的伦理问题是突出的,并且检验其在教育中的应用的实证研究很少。本研究的目的是开发一个ConvAI系统,以减轻潜在的伦理问题,并实证评估这种系统对数学学习的影响。具体而言,我们首先研究了利用教育大数据(npretraining = 2,097,139)减轻教育环境中ConvAI伦理问题的计算策略,发现研究人员可以通过所研究的算法策略有效地增强ConvAI责任。然后,在学习科学原理的指导下,利用这些策略构建了一个ConvAI系统。最后,我们通过随机实验(n参与者= 151)检验了学生在使用该ConvAI系统学习代数时的眼动模式、接受度和学习过程。与对照组相比,使用开发的ConvAI的参与者普遍表现出更高的视觉注意力水平。此外,与会者对ConvAI技术表示积极接受。最后,参与者与ConvAI技术的互动模式影响了他们的代数学习。这些结果为教育研究人员和实践者在学习环境中整合ConvAI提供了见解。
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RICE AlgebraBot: Lessons learned from designing and developing responsible conversational AI using induction, concretization, and exemplification to support algebra learning
The importance and challenge of Algebra learning is widely recognized, with students across the U.S. facing difficulties due to the subject's complexity. While extensive research has focused on enhancing Algebra learning in K-12 education, the reusability, scalability, and effectiveness of the strategies employed (e.g., manual interventions and digital tutoring platforms) remain limited. Conversational AI (ConvAI), enabled by the advancement of large language models (LLMs), emerges as a potential tool for automatic, personalized, and effective student support. However, ethical concerns surrounding diversity, safety, sentiment, and stereotype associated with ConvAI are prominent, and empirical studies examining its application in education are scarce. The purpose of this study is to develop a ConvAI system that mitigates the potential ethical concerns and empirically evaluate the effect of such a system for math learning. Specifically, we first examined computational strategies to mitigate the ethical concerns of ConvAI in educational setting with educational big data (npretraining = 2,097,139) and found that researchers could effectively enhance ConvAI responsibility through the investigated algorithmic strategies. Then, a ConvAI system was constructed using these strategies, guided by learning sciences principles. Lastly, we examined students' eye-tracking patterns, acceptance, and learning processes when using this ConvAI system to learn Algebra through a random experiment (nparticipant = 151). Participants using the developed ConvAI demonstrated generally increased visual attention levels as compared to the control group. Moreover, participants expressed a positive acceptance towards the ConvAI technology. Finally, participants' interaction patterns with the ConvAI technology influenced their Algebra learning. These results provide insights for both educational researchers and practitioners to integrate ConvAI in learning environments.
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来源期刊
CiteScore
16.80
自引率
0.00%
发文量
66
审稿时长
50 days
期刊最新文献
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