The original article emphasizes JSON as the preferred output format for AI tutors and graders, highlighting its ease of ingestion by gradebooks and other systems. This article would delve deeper into the practical aspects of structuring data effectively.
It would start by examining alternative structured data formats, such as YAML and Protocol Buffers, considering their respective strengths and weaknesses in the context of EdTech. It would also address the challenges of schema design, focusing on best practices for creating robust and flexible schemas that can accommodate evolving educational needs. Practical examples would be provided, demonstrating how to handle complex data types, nested structures, and versioning within JSON and alternative formats.
Furthermore, it would explore the process of validating the structured outputs, employing schema validation tools to ensure data integrity and compatibility. Finally, it would cover strategies for handling edge cases and error condition