Abstract
The adoption of Artificial Intelligence in Education (AIED) holds the promise of revolutionizing educational practices by offering personalized learning experiences, automating administrative and pedagogical tasks, and reducing the cost of content creation. However, the lack of standardized practices in the development and deployment of AIED solutions has led to fragmented ecosystems, which presents challenges in interoperability, scalability, and ethical governance. This article aims to address the critical need to develop and implement industry standards in AIED, offering a comprehensive analysis of the current landscape, challenges, and strategic approaches to overcome these obstacles. We begin by examining the various applications of AIED in various educational settings and identify key areas lacking in standardization, including system interoperability, ontology mapping, data integration, evaluation, and ethical governance. Then, we propose a multi-tiered framework for establishing robust industry standards for AIED. In addition, we discuss methodologies for the iterative development and deployment of standards, incorporating feedback loops from real-world applications to refine and adapt standards over time. The paper also highlights the role of emerging technologies and pedagogical theories in shaping future standards for AIED. Finally, we outline a strategic roadmap for stakeholders to implement these standards, fostering a cohesive and ethical AIED ecosystem. By establishing comprehensive industry standards, such as those by IEEE Artificial Intelligence Standards Committee (AISC) and International Organization for Standardization (ISO), we can accelerate and scale AIED solutions to improve educational outcomes, ensuring that technological advances align with the principles of inclusivity, fairness, and educational excellence.
Abstract (translated)
人工智能在教育（AIED）的采用有望通过提供个性化的学习经历、自动化管理和教学任务以及减少内容创作成本来改革教育实践。然而，AIED解决方案开发和部署过程中缺乏标准化实践，导致碎片化生态系统，使得互操作性、可扩展性和伦理治理方面存在挑战。本文旨在解决开发和实施AIED行业标准的关键需求，全面分析当前格局、挑战和战略方法，克服这些障碍。我们首先检查AIED在各种教育环境中的应用，确定缺乏标准化实践的关键领域，包括系统互操作性、本体映射、数据集成、评估和伦理治理。然后，我们提出了一个多层框架来建立AIED行业标准。此外，我们讨论了包括来自实际应用的反馈循环在内，迭代开发和部署标准的和方法。最后，本文还强调了新兴技术和对AIED教育理论的影响，勾勒出AIED未来标准的战略路线图。通过建立全面的标准，如IEEE人工智能标准委员会（AISC）和国际标准化组织（ISO）的标准，我们可以加速并扩展AIED解决方案，提高教育成果，确保技术进步与包容性、公平和教育卓越的原则相符。
URL
https://arxiv.org/abs/2403.14689
