Artificial Intelligence in Education: Mapping Adaptive Learning and Learning Analytics in K–12 Online, Virtual, and Distance Learning.
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| Title: | Artificial Intelligence in Education: Mapping Adaptive Learning and Learning Analytics in K–12 Online, Virtual, and Distance Learning. |
|---|---|
| Authors: | Boulhrir, Taoufik1, Ghreir, Hanan2, Hamash, Mahmoud3, Robert, Michael4 |
| Source: | International Review of Research in Open & Distributed Learning. May2026, Vol. 27 Issue 2, p122-148. 27p. |
| Subject Terms: | *Artificial intelligence, *Learning analytics, *Distance education, *Educational technology, *Educational intervention, *Instructional systems, *Secondary education, *Online education |
| Abstract: | This scoping review examines how artificial intelligence (AI) has been conceptualized and applied in adaptive learning and learning analytics in K–12 online and distance education between 2020 and 2025. Following Arksey and O’Malley’s framework and reported in accordance with PRISMA-ScR, we analyzed 21 empirical studies to explore thematic patterns, methodological trends, and research gaps. Most studies reported gains for learners in engagement, motivation, and self-regulation. However, reported benefits were unevenly distributed and often favored better-resourced learners, particularly in contexts where teacher mediation and institutional support were modest. AI was explicitly integrated in two-thirds of the studies, yet definitional inconsistencies blurred distinctions between genuine intelligence and automated adaptation. Quantitative designs were predominant, largely focusing on performance outcomes as derived from system logs and test data. While a small but growing number of mixed-methods studies have focused on learner experience and teacher mediation, the field remains constrained by methodological consistency and insufficient clarity regarding AI mechanisms. The findings highlight the importance of clearer conceptual frameworks, research designs that are participatory and context-sensitive, and ethical approaches that center teacher expertise and learner participation. This review argues that the transformative potential of AI for adaptive learning depends less on technological sophistication than on equitable, pedagogically informed integration between human judgment and automated systems. [ABSTRACT FROM AUTHOR] |
| Copyright of International Review of Research in Open & Distributed Learning is the property of Governors of Athabasca University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 193542272 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial Intelligence in Education: Mapping Adaptive Learning and Learning Analytics in K–12 Online, Virtual, and Distance Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Boulhrir%2C+Taoufik%22">Boulhrir, Taoufik</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ghreir%2C+Hanan%22">Ghreir, Hanan</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Hamash%2C+Mahmoud%22">Hamash, Mahmoud</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Robert%2C+Michael%22">Robert, Michael</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Review+of+Research+in+Open+%26+Distributed+Learning%22">International Review of Research in Open & Distributed Learning</searchLink>. May2026, Vol. 27 Issue 2, p122-148. 27p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br />*<searchLink fieldCode="DE" term="%22Distance+education%22">Distance education</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+intervention%22">Educational intervention</searchLink><br />*<searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Secondary+education%22">Secondary education</searchLink><br />*<searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This scoping review examines how artificial intelligence (AI) has been conceptualized and applied in adaptive learning and learning analytics in K–12 online and distance education between 2020 and 2025. Following Arksey and O’Malley’s framework and reported in accordance with PRISMA-ScR, we analyzed 21 empirical studies to explore thematic patterns, methodological trends, and research gaps. Most studies reported gains for learners in engagement, motivation, and self-regulation. However, reported benefits were unevenly distributed and often favored better-resourced learners, particularly in contexts where teacher mediation and institutional support were modest. AI was explicitly integrated in two-thirds of the studies, yet definitional inconsistencies blurred distinctions between genuine intelligence and automated adaptation. Quantitative designs were predominant, largely focusing on performance outcomes as derived from system logs and test data. While a small but growing number of mixed-methods studies have focused on learner experience and teacher mediation, the field remains constrained by methodological consistency and insufficient clarity regarding AI mechanisms. The findings highlight the importance of clearer conceptual frameworks, research designs that are participatory and context-sensitive, and ethical approaches that center teacher expertise and learner participation. This review argues that the transformative potential of AI for adaptive learning depends less on technological sophistication than on equitable, pedagogically informed integration between human judgment and automated systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Review of Research in Open & Distributed Learning is the property of Governors of Athabasca University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 122 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Learning analytics Type: general – SubjectFull: Distance education Type: general – SubjectFull: Educational technology Type: general – SubjectFull: Educational intervention Type: general – SubjectFull: Instructional systems Type: general – SubjectFull: Secondary education Type: general – SubjectFull: Online education Type: general Titles: – TitleFull: Artificial Intelligence in Education: Mapping Adaptive Learning and Learning Analytics in K–12 Online, Virtual, and Distance Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Boulhrir, Taoufik – PersonEntity: Name: NameFull: Ghreir, Hanan – PersonEntity: Name: NameFull: Hamash, Mahmoud – PersonEntity: Name: NameFull: Robert, Michael IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14923831 Numbering: – Type: volume Value: 27 – Type: issue Value: 2 Titles: – TitleFull: International Review of Research in Open & Distributed Learning Type: main |
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