Development of an AI and Learning Analytics (LA) Integrated Teaching-Learning Model for STEM

This research presents the development of the STEM Teaching and Learning Model with AI & Learning Analytics (STELA), an integrated teaching-learning framework that combines Artificial Intelligence (AI) and Learning Analytics (LA) to enhance STEM education. STELA leverages AI’s ability to create personalized learning paths and provide real-time feedback, while LA utilizes student data to track progress, predict outcomes, and inform instructional strategies. By aligning these technologies, the model aims to optimize student engagement and improve learning outcomes in STEM subjects. The research identifies key challenges, including data privacy concerns, technological barriers, and the need for teacher training. By addressing these challenges, this study provides a scalable and adaptable framework for integrating AI and LA in diverse educational settings, offering practical solutions for enhancing STEM education through data-driven and AI-powered methodologies.

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Publication Details

Journal
International Journal on Research in STEM Education
Published
2025-11-09
DOI
https://doi.org/10.33830/ijrse.v7i2.1814
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
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article

Development of an AI and Learning Analytics (LA) Integrated Teaching-Learning Model for STEM

Tai Ki Kim
International Journal on Research in STEM Education
Online Learning and Analytics
article

Development of an AI and Learning Analytics (LA) Integrated Teaching-Learning Model for STEM

Tai Ki Kim
article en

Abstract

This research presents the development of the STEM Teaching and Learning Model with AI & Learning Analytics (STELA), an integrated teaching-learning framework that combines Artificial Intelligence (AI) and Learning Analytics (LA) to enhance STEM education. STELA leverages AI’s ability to create personalized learning paths and provide real-time feedback, while LA utilizes student data to track progress, predict outcomes, and inform instructional strategies. By aligning these technologies, the model aims to optimize student engagement and improve learning outcomes in STEM subjects. The research identifies key challenges, including data privacy concerns, technological barriers, and the need for teacher training. By addressing these challenges, this study provides a scalable and adaptable framework for integrating AI and LA in diverse educational settings, offering practical solutions for enhancing STEM education through data-driven and AI-powered methodologies.

International Journal on Research in STEM EducationVol. 7(2)
Yonsei University (KR)
Openalex Percentile: Top 63%
Online Learning and Analytics
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