[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116902-en":3,"doc-seo-116902-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116902,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","How Machine Learning (ML) is Transforming Higher Education - A Systematic Literature Review","In the last decade, artificial intelligence, machine learning, and learning data analytics have been introduced with significant impact in higher education, yet most institutions remain in early adoption stages. A systematic literature review was conducted on five years of work, following PRISMA guidelines, using SCOPUS-indexed publications screened via Rayyan QCRI. Of 1887 initial records, 171 articles were reviewed and analyzed with VOSViewer and Microsoft Excel.","Journal of Information Systems Engineering and Management  \n2023, 8(2), 21168  \ne-ISSN: 2468-4376  \n[https://www.jisem-journal.com/](https://www.jisem-journal.com/ Literature Review)[ Literature Review](https://www.jisem-journal.com/ Literature Review)  \n| How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review\u003Cbr>Agostinho Sousa Pinto 1, António Abreu 1, Eusébio Costa 2, Jerónimo Paiva 1\u003Cbr>1 CEOS.PP, ISCAP, Polytechnic University of Porto, Rua Jaime Lopes Amorim, 4465-004 S. Mamede de Infesta, Portugal\u003Cbr>2 European Institute of Superior Studies, Portugal\u003Cbr>*\u003Cbr>[Corresponding Author:](Corresponding Author: jeronimob12@gmail.com)[ ](Corresponding Author: jeronimob12@gmail.com)[jeronimob12@gmail.com](Corresponding Author: jeronimob12@gmail.com)\u003Cbr>Citation: Pinto, A. S., Abreu, A., Costa, E., and Paiva, J. (2023). How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review. Journal of Information Systems Engineering and Management, 8(2), 21168. [https://doi.org/10.55267/iadt.07.13227](https://doi.org/10.55267/iadt.07.13227) |  |  |\n| --- | --- | --- |\n| ARTICLE INFO\u003Cbr>Received: 14 Apr 2023\u003Cbr>Accepted: 27 Apr. 2023 | ABSTRACT\u003Cbr>In the last decade, artificial intelligence (AI), machine learning (ML) and learning data analytics have been introduced with great effect in the field of higher education. However, despite the potential benefits for higher education institutions (HIE´s) of these emerging technologies, most of them are still in the early stages of adoption of these technologies. Thus, a systematic literature review (SLR) on the literature published over the last 5 years on potential applications of machine learning in higher education is necessary. Following the PRISMA guidelines, out of the 1887 initially identified SCOPUS-indexed publications on the topic, 171 articles were selected for review. To screen the abstracts and titles of each citation, Rayyan QCRI was used. VOSViewer, a software tool for constructing and visualizing bibliometric networks, and Microsoft Excel were used to generate charts and figures. The findings show that the most widely researched application of ML in higher education is related to the prediction of academic performance and employability of students. The implications will be invaluable for researchers and practitioners to explore how ML and AI technologies ,in the era of ChatGPT, can be used in universities without jeopardizing academic integrity.\u003Cbr>Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Learning Analytics, Systematic Literature Review, ChatGPT, Higher Education, Digital Transformation, Industry 4.0. |  |\n| INTRODUCTION\u003Cbr>In the last decade, ML has been successfully implemented in a wide range of industries including medicine, hospitality, finance and e-commerce with profoundly disruptive effects. In this regard, the educational sector is no exception.\u003Cbr>According to McKinsey, data science and machine learning have the potential to add value for universities by unlocking significantly deeper insights into their student populations and identifying more nuanced risks than they could achieve through descriptive statistics greatly improving student retention and satisfaction (McKinsey, 2022) . In addition, learning analytics could be used across the entire student journey (prospective, current and former students) . For example, ML could identify the high schools and areas that tend to produce the best talent for an HIE, help identify what are the best measures to improve student satisfaction and better engage with the alumni by providing them with |  | continual learning opportunities after graduation.\u003Cbr>In addition, 2022 marked the release of ChatGPT-3, an AI chatbot fine-tuned using both supervised and reinforcement learning, that garnered extensive media attention for its humanlike responses. In response, several authors have expressed concern regarding the risk of ChatGPT being misused t","cbCaiaAs6amrlmfa","https://ap.wps.com/l/cbCaiaAs6amrlmfa","pdf",659362,1,9,"English","en",105,"# Abstract\n# Introduction\n## Background and rationale\n# Theoretical framework and related studies\n## Definitions: AI, ML, and DL","[{\"question\":\"What is the goal of the systematic literature review on machine learning in higher education?\",\"answer\":\"It evaluates the current state of the art in using machine learning in higher education by analyzing peer-reviewed literature from the last five years.\"},{\"question\":\"How were studies selected and screened in the review?\",\"answer\":\"The review followed PRISMA guidelines, starting from 1887 initially identified SCOPUS-indexed publications; abstracts and titles were screened using Rayyan QCRI.\"},{\"question\":\"What main application areas of machine learning in higher education were most studied?\",\"answer\":\"The most widely researched applications relate to predicting students’ academic performance and employability.\"}]","How Machine Learning (ML) is Transforming Higher Education - A Systematic Literature Review | PDF",1785672371,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"how-machine-learning-ml-is-transforming-higher-education-a-systematic-literature-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/how-machine-learning-ml-is-transforming-higher-education-a-systematic-literature-review/116902/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of the systematic literature review on machine learning in higher education?","Question",{"text":75,"@type":76},"It evaluates the current state of the art in using machine learning in higher education by analyzing peer-reviewed literature from the last five years.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were studies selected and screened in the review?",{"text":80,"@type":76},"The review followed PRISMA guidelines, starting from 1887 initially identified SCOPUS-indexed publications; 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