[论文解读] Transforming Teacher Education in Developing Countries: The Role of Generative AI in Bridging Theory and Practice

Matthew Nyaaba|arXiv (Cornell University)|Nov 16, 2024

Online Learning and Analytics被引用 6

一句话总结

该论文认为生成式AI可以通过将焦点从内容交付转向教学建模和基于表现的评估，改变发展中国家的教师教育，以加纳为案例，并对潜在误用提出警示。

ABSTRACT

This study examines the transformative potential of Generative AI (GenAI) in teacher education within developing countries, focusing on Ghana, where challenges such as limited pedagogical modeling, performance-based assessments, and practitioner-expertise gaps hinder progress. GenAI has the capacity to address these issues by supporting content knowledge acquisition, a role that currently dominates teacher education programs. By taking on this foundational role, GenAI allows teacher educators to redirect their focus to other critical areas, including pedagogical modeling, authentic assessments, and fostering digital literacy and critical thinking. These roles are interconnected, creating a ripple effect where pre-service teachers (PSTs) are better equipped to enhance K-12 learning outcomes and align education with workforce needs. The study emphasizes that GenAI's roles are multifaceted, directly addressing resistance to change, improving resource accessibility, and supporting teacher professional development. However, it cautions against misuse, which could undermine critical thinking and creativity, essential skills nurtured through traditional teaching methods. To ensure responsible and effective integration, the study advocates a scaffolding approach to GenAI literacy. This includes educating PSTs on its supportive role, training them in ethical use and prompt engineering, and equipping them to critically assess AI-generated content for biases and validity. The study concludes by recommending empirical research to explore these roles further and develop practical steps for integrating GenAI into teacher education systems responsibly and effectively.

研究动机与目标

• 评估加纳初级教师教育改革中持续存在的挑战。
• 评估生成式AI如何支持内容知识、教学建模和基于表现的评估。
• 提出一种分层的、负责任的GenAI整合与专业发展框架方法。
• 识别对资源、课程保真度、评估实践与劳动力对齐等方面的潜在连锁效应。

提出的方法

• 对加纳教师教育改革及挑战进行综述。
• 综合在教师教育领域中生成式AI的发现以构建理论论证。
• 提出一个分层、因果的视角，使GenAI为教学建模和评估释放时间。
• 提出一个分层的实现路径及GenAI素养的伦理考量。
• 强调对政策、实践和未来实证研究的影响。

Figure 1: : GenAI Role in Transforming Teacher Education

实验结果

研究问题

• RQ1 生成式AI在变革加纳及类似发展中国家教师教育中可以扮演哪些角色？
• RQ2 GenAI如何减轻内容负担以促进教师教育中的教学建模和基于表现的评估？
• RQ3 将GenAI整合到教师教育中的潜在风险、伦理关切与保障措施是什么？
• RQ4 在教师教育与研究中负责任地实施GenAI需要哪些实际的分层步骤？

主要发现

• GenAI有潜力支持内容知识，同时使教师教育者更加专注于教学建模和基于表现的评估。
• GenAI可以提升资源获取、促进数字素养与批判性思维，并使教学与劳动力需求保持一致。
• 设想一个连锁反应效应：改进的教学建模提升准教师（PSTs），进而提升K-12学习成果和劳动力就绪度。
• 若未负责任地实施，关于GenAI误用的重大警告可能削弱批判性思维和创造力。
• 论文呼吁进行实证研究以验证GenAI的作用和负责任整合的实际步骤。
• 建议采用分层的GenAI素养方法，为准教师和教师教育者在伦理和高效使用方面做准备。