$\mu$Ed API: Towards A Shared API for EdTech Microservices

2026-02-20

AI Summary

This study addresses the limitation of general-purpose learning platforms in supporting domain-specific automation—such as intelligent assessment and feedback—which hinders the advancement of teaching intelligence. To overcome this, the authors propose and design μEd, a platform-agnostic educational microservice standard API based on RESTful architecture. Drawing on deployment experiences from four universities, μEd establishes a unified interface specification that supports core functionalities including automated assessment, personalized feedback, and educational chatbots. This work represents the first effort to construct an interoperable and extensible ecosystem for educational automation services. The framework has already been implemented across multiple institutions, providing a reusable technical foundation for cross-platform, multidisciplinary teaching automation.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Intelligent User Interfaces

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Learning at scale often requires domain-specific automation such as assessment and feedback. An organization locked in to a general learning platform without these specialist automations limits its pedagogical offering. An ecosystem of interoperable, platform-agnostic microservices for domain-specific automation would solve this problem. To develop an effective eco-system, a standard interface (API) for education microservices is required. We propose an initial specification for a standard, platform-independent API for educational microservices, $\upmu$Ed. The API integrates functionality from existing systems in use at four institutions, which are adopting the new API. The API is initially specified for automation of feedback, assessment, and educational chatbots, with further service types envisaged in the future. The API specification provided here enables the development of an eco-system of education microservices that will facilitate automation in more domains, to more users, providing a richer learning experience in a wide range of disciplines.

Problem

• education microservices
• domain-specific automation
• learning platforms
• interoperability
• API standardization

Innovation

• educational microservices
• standard API
• platform-agnostic
• automated feedback
• interoperability