DMP_AI: An AI-Aided K-12 System for Teaching and Learning in Diverse Schools

Zhenqun Yang, Jiannong Cao, Xiaoyin Li, Kaile Wang, Xinzhe Zheng, Kai Cheung Franky Poon, Daniel Lai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

The use of Artificial Intelligence (AI) has gained momentum in education. However, the use of AI in K-12 education is still in its nascent stages, and further research and development is needed to realize its potential. Moreover, the creation of a comprehensive and cohesive system that effectively harnesses AI to support teaching and learning across a diverse range of primary and secondary schools presents substantial challenges that need to be addressed. To fill these gaps, especially in countries like China, we designed and implemented the DMP_AI (Data Management Platform Artificial Intelligence) system, an innovative AIaided educational system specifically designed for K-12 education. The system utilizes data mining, natural language processing, and machine learning, along with learning analytics, to offer a wide range of features, including student academic performance and behavior prediction, early warning system, analytics of Individualized Education Plan, talented students’ prediction and identification, and cross-school personalized electives recommendation. The development of this system has been meticulously carried out while prioritizing user privacy and addressing the challenges posed by data heterogeneity. We successfully implemented the DMP_AI system in real-world primary and secondary schools, allowing us to gain valuable insights into the potential and challenges of integrating AI into K-12 education in the real world. This system will serve as a valuable resource for supporting educators in providing effective and inclusive K-12 education.
Original languageEnglish
Title of host publicationBlended Learning. Intelligent Computing in Education - 17th International Conference on Blended Learning, ICBL 2024, Proceedings
PublisherSpringer Science and Business Media Deutschland GmbH
Pages117-130
Number of pages15
Volume14797
ISBN (Electronic)1611-3349
ISBN (Print)0302-9743
DOIs
Publication statusPublished - 2024
Event17th International Conference on Blended Learning, ICBL 2024 - Macao, China
Duration: 29 Jul 20241 Aug 2024

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer
Volume14797 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Blended Learning, ICBL 2024
Country/TerritoryChina
CityMacao
Period29/07/241/08/24

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