review · Discover Sustainability
Large language models represent a major development in artificial intelligence, offering enhanced language understanding and generation capabilities that influence education despite bans and institutional resistance. An examination of their varieties, historical background, and training workflows demonstrates their expanding role across digital and higher education settings. A structured theoretical framework offers guidance for incorporating these models into educational environments, tackling critical hurdles related to personalisation, adaptability, and ethical considerations. In addition, practical case studies highlight solutions to persistent obstacles such as bias and data privacy concerns. By clarifying these integration pathways, the work outlines how such models can support the teaching and learning process while mitigating the primary technical and ethical challenges associated with their implementation.
Educational institutions face growing challenges as students and educators encounter artificial intelligence tools that are rapidly reshaping classroom practices. By mapping the mechanisms of large language models alongside practical strategies for data privacy, bias mitigation, and personalisation, this work helps educators and administrators navigate the integration of automated tools safely and effectively within modern learning environments.
The work addresses educational software developers, higher education institutions, and digital learning providers seeking to implement artificial intelligence responsibly. The proposed framework and case studies offer guidance on mitigating bias and data privacy issues during model deployment. Because the review focuses on theoretical frameworks and case analysis rather than a standalone commercial product, it functions as early-stage guidance to inform applied software development and institutional policy.
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A significant advancement in artificial intelligence is the development of large language models (LLMs). Despite opposition and explicit bans by some authorities, LLMs continue to play a transformative role, particularly in education, by improving language understanding and generation capabilities. This study explores LLMs’ types, history, and training processes, alongside their application in education, including digital and higher education settings. A novel theoretical framework is proposed to guide the integration of LLMs into education, addressing key challenges such as personalization, ethical concerns, and adaptability. Furthermore, the study presents practical case studies and solutions to barriers, such as data privacy and bias, offering insights into their role in enhancing the teaching–learning process. By providing a systematic analysis and proposing a structured framework, this study advances current knowledge and highlights the significant potential of LLMs in revolutionizing education.
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DOI: 10.1007/s43621-025-00815-8
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