Standardization in artificial general intelligence model for education

Qiuxuan Xu, Yonghe Wu, Hao Zheng, Huan Yan, Huina Wu, Yu Qian, You Wu, Bowen Liu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The application of Artificial General Intelligence Models (AGIMs) in education has been identified as a promising emerging field. However, extensive research has revealed limitations in using AGIM in education, particularly in terms of controllability, trustworthiness, explainability, evaluation and feedback, security, and privacy. Therefore, standardization in AGIMs for Education (AGIME) is urgently required to provide normative guidance for developing artificial intelligence systems in education. This study first explores an AGIME standardization process with the methodology of use case collection and iterative research. We then propose the definition and attributes of AGIME and establish a standard system framework for the AGIME life cycle. This framework includes published specifications such as information model, data specification, evaluation specification, and application requirements on teaching and learning. We introduce standard application cases to validate the effectiveness of AGIME standard system framework. Finally, we present several specifications currently under development within this standard system, including interface, regulatory, operation and maintenance, and security, ethics, and privacy specifications. This study provides references for AGIME development and deployment, ensuring the technical stability, data credibility, evaluation accuracy, and pedagogical applicability of AGIME.

Original languageEnglish
Article number104006
JournalComputer Standards and Interfaces
Volume94
DOIs
StatePublished - Aug 2025

Keywords

  • Artificial general intelligence model for education
  • Artificial intelligence
  • Specifications
  • Standardization
  • Use cases

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