ChatGPT Integration in Higher Education for Personalized Learning, Academic Writing, and Coding Tasks: A Systematic Review

Abstract

The emergence of ChatGPT in higher education has raised immense discussion due to its versatility in performing tasks, including coding, personalized learning, human-like conversations, and information retrieval. Despite the rapidly growing use of ChatGPT, a dire need still exists for an overarching view regarding its role and implications in educational settings. Following the PRISMA guidelines, this study represents a systematic review of 26 articles exploring the use of ChatGPT in academic writing, personalized learning, and code generation. The relevant literature was identified through electronic databases, including Scopus, ACM Digital Library, Education Research Complete, Computers & Applied Sciences, Web of Science, and IEEE Xplore. Key details from each article were extracted and synthesized narratively to provide insights into ChatGPT’s efficacy in academic writing, personalized learning, and coding. The findings indicate that ChatGPT enhances tailored learning by adapting delivery methods to individual needs, supports academic writing through error detection and content refinement, and assists in coding by offering clarifications and reusable code snippets. However, there are concerns over its ethical implications, including the impact on academic integrity, overreliance by students on AI, and privacy concerns about data use. Based on these insights, this study proposes recommendations for the ethical and responsible integration of ChatGPT into higher education, ensuring its utility while maintaining academic integrity. In addition, the results are discussed based on the relevant learning theories to understand how students engage with, learn through, and adapt to AI technologies such as ChatGPT in educational contexts.

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