Date
Publisher
arXiv
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.
Study at a glance
- Question: How can industry standards be developed and implemented for artificial intelligence in education?
- Setting and data: Not stated in the abstract
- Method: Analysis of the current landscape and proposal of a standards framework
Why it matters
The lack of standardized practices in developing and deploying educational AI has led to fragmented ecosystems. This creates challenges in system interoperability, scalability, and ethical governance.
Key findings
- Across various educational settings, a lack of standardized practices for educational AI creates fragmented systems that struggle with tool compatibility, scaling, and ethical governance.
- Educational AI tools currently lack industry standards for connecting between systems, sharing data, defining concepts, evaluating performance, and maintaining ethical governance.
- Comprehensive industry standards can scale educational AI technologies to improve learning outcomes while ensuring they remain fair, inclusive, and educationally sound.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Why use AI?
Study design
