A. Marengo, Alessandro Pagano, Jenny Pange, K. A. Soomro
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Areas with empirical evidence supporting AI applications in academia are delineated.\n\n\nResearch limitations/implications\nThe prevalence of theoretical proposals may limit generalizability. Further research is encouraged to validate and expand upon the identified empirical applications of AI in higher education.\n\n\nPractical implications\nThis review outlines imperative implications for future research and the implementation of evidence-based AI interventions in higher education, facilitating informed decision-making for academia and stakeholders.\n\n\nOriginality/value\nThis paper contributes a comprehensive synthesis of empirical studies, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches.\n","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":"22 12","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The educational value of artificial intelligence in higher education: a 10-year systematic literature review\",\"authors\":\"A. Marengo, Alessandro Pagano, Jenny Pange, K. A. Soomro\",\"doi\":\"10.1108/itse-11-2023-0218\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nThis paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published research characteristics and provide insights into the promises and challenges of AI integration in academia.\\n\\n\\nDesign/methodology/approach\\nA systematic literature review was conducted, encompassing 44 empirical studies published as peer-reviewed journal papers. The review focused on identifying trends, categorizing research types and analysing the evidence-based applications of AI in higher education.\\n\\n\\nFindings\\nThe review indicates a recent surge in publications concerning AI in higher education. However, a significant proportion of these publications primarily propose theoretical and conceptual AI interventions. Areas with empirical evidence supporting AI applications in academia are delineated.\\n\\n\\nResearch limitations/implications\\nThe prevalence of theoretical proposals may limit generalizability. Further research is encouraged to validate and expand upon the identified empirical applications of AI in higher education.\\n\\n\\nPractical implications\\nThis review outlines imperative implications for future research and the implementation of evidence-based AI interventions in higher education, facilitating informed decision-making for academia and stakeholders.\\n\\n\\nOriginality/value\\nThis paper contributes a comprehensive synthesis of empirical studies, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches.\\n\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":\"22 12\",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/itse-11-2023-0218\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/itse-11-2023-0218","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
The educational value of artificial intelligence in higher education: a 10-year systematic literature review
Purpose
This paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published research characteristics and provide insights into the promises and challenges of AI integration in academia.
Design/methodology/approach
A systematic literature review was conducted, encompassing 44 empirical studies published as peer-reviewed journal papers. The review focused on identifying trends, categorizing research types and analysing the evidence-based applications of AI in higher education.
Findings
The review indicates a recent surge in publications concerning AI in higher education. However, a significant proportion of these publications primarily propose theoretical and conceptual AI interventions. Areas with empirical evidence supporting AI applications in academia are delineated.
Research limitations/implications
The prevalence of theoretical proposals may limit generalizability. Further research is encouraged to validate and expand upon the identified empirical applications of AI in higher education.
Practical implications
This review outlines imperative implications for future research and the implementation of evidence-based AI interventions in higher education, facilitating informed decision-making for academia and stakeholders.
Originality/value
This paper contributes a comprehensive synthesis of empirical studies, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches.
期刊介绍:
ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric.
Indexed/Abstracted:
Web of Science SCIE
Scopus
CAS
INSPEC
Portico