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AI-Driven Adaptive Learning Systems in Higher Education: A Systematic Review

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Title: AI-Driven Adaptive Learning Systems in Higher Education: A Systematic Review
Language: English
Authors: Thanet Yuensook, Thada Jantakoon, Potsirin Limpinan
Source: Journal of Education and Learning. 2026 15(2):117-132.
Availability: Canadian Center of Science and Education. 1595 Sixteenth Ave Suite 301, Richmond Hill, Ontario, L4B 3N9 Canada. Tel: 416-642-2606; Fax: 416-642-2608; e-mail: jel@ccsenet.org; Web site: http://www.ccsenet.org/journal/index.php/jel
Peer Reviewed: Y
Page Count: 16
Publication Date: 2026
Document Type: Journal Articles
Information Analyses
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Higher Education, Technology Uses in Education, Publications, Geographic Regions, Instructional Effectiveness, Barriers, Global Approach, Academic Achievement, Learner Engagement, Outcomes of Education, Influences, Technology Integration, Educational Strategies, Research Methodology, Individualized Instruction, Foreign Countries, Educational Research
Geographic Terms: Indonesia, United States, Spain, Australia, China, Cyprus, Mexico, Vietnam, Italy, Greece, Albania, Bulgaria, Peru, Pakistan, Saudi Arabia, India
ISSN: 1927-5250
1927-5269
Abstract: This systematic review investigates the implementation and impact of AI-driven adaptive learning systems in higher education, based on an analysis of 15 empirical studies published between 2020 and 2024. Following the PRISMA 2020 guidelines, the review addresses four core questions: (1) research trends and geographic distribution; (2) types of AI technologies and system characteristics; (3) implementation strategies in educational contexts; and (4) effectiveness and challenges encountered. The findings indicate a substantial increase in publications after 2022, with 73% of the studies published in 2023-2024. Geographically, research contributions span 15 countries, with the United States, China, and Europe as leading contributors. The predominant AI technologies identified include machine learning (40%), natural language processing (33%), and hybrid systems (27%), supporting real-time personalization and adaptive feedback mechanisms. Implementation strategies were observed primarily in STEM fields, language learning, and hybrid learning environments, with applications ranging from intelligent tutoring systems to LMS-integrated AI assistants. Effectiveness outcomes reported academic performance gains of 15-25% and improved learner engagement by up to 40%. However, challenges persist, including insufficient technical infrastructure, faculty readiness, ethical concerns (e.g., data privacy, algorithmic bias), and the underrepresentation of non-STEM disciplines. This review highlights critical considerations for successful integration of AI-enhanced adaptive systems and provides strategic guidance for institutions aiming to enhance personalization, equity, and scalability in higher education.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1507235
Database: ERIC
FullText Text:
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  Data: AI-Driven Adaptive Learning Systems in Higher Education: A Systematic Review
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  Data: <searchLink fieldCode="AR" term="%22Thanet+Yuensook%22">Thanet Yuensook</searchLink><br /><searchLink fieldCode="AR" term="%22Thada+Jantakoon%22">Thada Jantakoon</searchLink><br /><searchLink fieldCode="AR" term="%22Potsirin+Limpinan%22">Potsirin Limpinan</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Education+and+Learning%22"><i>Journal of Education and Learning</i></searchLink>. 2026 15(2):117-132.
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  Data: Canadian Center of Science and Education. 1595 Sixteenth Ave Suite 301, Richmond Hill, Ontario, L4B 3N9 Canada. Tel: 416-642-2606; Fax: 416-642-2608; e-mail: jel@ccsenet.org; Web site: http://www.ccsenet.org/journal/index.php/jel
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  Data: 16
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Label: Geographic Terms
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  Data: 1927-5250<br />1927-5269
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  Data: This systematic review investigates the implementation and impact of AI-driven adaptive learning systems in higher education, based on an analysis of 15 empirical studies published between 2020 and 2024. Following the PRISMA 2020 guidelines, the review addresses four core questions: (1) research trends and geographic distribution; (2) types of AI technologies and system characteristics; (3) implementation strategies in educational contexts; and (4) effectiveness and challenges encountered. The findings indicate a substantial increase in publications after 2022, with 73% of the studies published in 2023-2024. Geographically, research contributions span 15 countries, with the United States, China, and Europe as leading contributors. The predominant AI technologies identified include machine learning (40%), natural language processing (33%), and hybrid systems (27%), supporting real-time personalization and adaptive feedback mechanisms. Implementation strategies were observed primarily in STEM fields, language learning, and hybrid learning environments, with applications ranging from intelligent tutoring systems to LMS-integrated AI assistants. Effectiveness outcomes reported academic performance gains of 15-25% and improved learner engagement by up to 40%. However, challenges persist, including insufficient technical infrastructure, faculty readiness, ethical concerns (e.g., data privacy, algorithmic bias), and the underrepresentation of non-STEM disciplines. This review highlights critical considerations for successful integration of AI-enhanced adaptive systems and provides strategic guidance for institutions aiming to enhance personalization, equity, and scalability in higher education.
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  Data: 2026
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  Data: EJ1507235
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1507235
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 117
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Higher Education
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Publications
        Type: general
      – SubjectFull: Geographic Regions
        Type: general
      – SubjectFull: Instructional Effectiveness
        Type: general
      – SubjectFull: Barriers
        Type: general
      – SubjectFull: Global Approach
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Outcomes of Education
        Type: general
      – SubjectFull: Influences
        Type: general
      – SubjectFull: Technology Integration
        Type: general
      – SubjectFull: Educational Strategies
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      – SubjectFull: Research Methodology
        Type: general
      – SubjectFull: Individualized Instruction
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Educational Research
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      – SubjectFull: Indonesia
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      – SubjectFull: United States
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      – SubjectFull: Spain
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      – SubjectFull: Australia
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      – SubjectFull: China
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      – SubjectFull: Cyprus
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      – SubjectFull: Mexico
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      – SubjectFull: Vietnam
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      – SubjectFull: Bulgaria
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      – SubjectFull: Peru
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      – SubjectFull: Pakistan
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      – SubjectFull: Saudi Arabia
        Type: general
      – SubjectFull: India
        Type: general
    Titles:
      – TitleFull: AI-Driven Adaptive Learning Systems in Higher Education: A Systematic Review
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