Wijaya Tommy Tanu, Yu Qingchun, Cao Yiming, He Yahan, Leung Frederick K S
School of mathematical Sciences, Beijing Normal University, Beijing 100088, China.
National Research Institute for Mathematics Teaching Materials, Beijing 100190, China.
Behav Sci (Basel). 2024 Oct 30;14(11):1008. doi: 10.3390/bs14111008.
Artificial Intelligence (AI) technology, particularly generative AI, has positively impacted education by enhancing mathematics instruction with personalized learning experiences and improved data analysis. Nonetheless, variations in AI literacy, trust in AI, and dependency on these technologies among mathematics teachers can significantly influence their development of 21st-century skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. This study aims to identify distinct profiles of AI literacy, trust, and dependency among mathematics teachers and examines how these profiles correlate with variations in the aforementioned skills. Using a cross-sectional research design, the study collected data from 489 mathematics teachers in China. A robust three-step latent profile analysis method was utilized to analyze the data. The research revealed five distinct profiles of AI literacy and trust among the teachers: (1) Basic AI Engagement; (2) Developing AI Literacy, Skeptical of AI; (3) Balanced AI Competence; (4) Advanced AI Integration; and (5) AI Expertise and Confidence. The study found that an increase in AI literacy and trust directly correlates with an increase in AI dependency and a decrease in skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. The findings underscore the need for careful integration of AI technologies in educational settings. Excessive reliance on AI can lead to detrimental dependencies, which may hinder the development of essential 21st-century skills. The study contributes to the existing literature by providing empirical evidence on the impact of AI literacy and trust on the professional development of mathematics teachers. It also offers practical implications for educational policymakers and institutions to consider balanced approaches to AI integration, ensuring that AI enhances rather than replaces the critical thinking and problem-solving capacities of educators.
人工智能（AI）技术，尤其是生成式人工智能，通过提供个性化学习体验和改进数据分析，对数学教学产生了积极影响，从而提升了教育质量。尽管如此，数学教师在人工智能素养、对人工智能的信任以及对这些技术的依赖程度上存在差异，这可能会显著影响他们21世纪技能的发展，如自信心、解决问题的能力、批判性思维、创造性思维和协作能力。本研究旨在识别数学教师在人工智能素养、信任和依赖方面的不同特征，并探讨这些特征如何与上述技能的差异相关联。该研究采用横断面研究设计，收集了来自中国489名数学教师的数据。运用了一种强大的三步潜在特征分析方法来分析数据。研究揭示了教师在人工智能素养和信任方面的五种不同特征：（1）基础人工智能参与度；（2）发展人工智能素养，对人工智能持怀疑态度；（3）平衡的人工智能能力；（4）高级人工智能整合；（5）人工智能专业知识与信心。研究发现，人工智能素养和信任的提高直接与人工智能依赖的增加以及自信心、解决问题的能力、批判性思维、创造性思维和协作能力等技能的下降相关。研究结果强调了在教育环境中谨慎整合人工智能技术的必要性。过度依赖人工智能可能会导致有害的依赖，这可能会阻碍21世纪关键技能的发展。该研究通过提供关于人工智能素养和信任对数学教师专业发展影响的实证证据，为现有文献做出了贡献。它还为教育政策制定者和机构提供了实际启示，促使他们考虑平衡的人工智能整合方法，确保人工智能增强而不是取代教育工作者的批判性思维和解决问题的能力。
