Redesigning the Future of AI in Education: A Proposed Academic Large Language Model Framework for Institutions of Higher Learning

Abstract

Large Language Models (LLMs) like ChatGPT have quickly changed academic practices, sparking discussions about integrity, authorship, assessment, and the trustworthiness of AI-generated content in higher education. Critics say that Large Language Models produce hallucinations, unreliable citations, and inconsistent academic explanations, blaming these issues on the technology's fundamental flaws. However, this paper argues that such criticism is mistaken. The real problem comes not from the architecture of Large Language Models but from the poor-quality, diverse, and unverified open-internet data used for training. To overcome these issues, this study introduces Academic Large Language Models (A‑LLMs), a new type of AI trained mainly on high-quality, peer-reviewed academic sources like Scopus, Web of Science, JSTOR, PubMed, and IEEE Xplore. The paper claims that verified Academic Large Language Models can revolutionize teaching, assessment, research integrity, and educational fairness. A framework is proposed where AI developers, academic publishers, and universities work together to create academically aligned, citation-traceable AI systems. This study is conceptual and based on desk research. Ultimately, Academic Large Language Models aim to give universities a way to safely integrate AI into teaching, assessment, and research, ensuring greater reliability and adherence to academic standards.