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Mobile-Based Intelligent Tutoring Systems: Enhancing Personalized Learning in Digital Education.

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Title: Mobile-Based Intelligent Tutoring Systems: Enhancing Personalized Learning in Digital Education.
Authors: Akhter, Shamim1 shamim.akhter@newinti.edu.my, Kumar, Tribhuwan2 t.kumar@psau.edu.sa, Shaheen, Musart3 shaheenmusarat332@gmail.com
Source: International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 9, p4-16. 13p.
Subjects: Intelligent tutoring systems, Individualized instruction, Data privacy, Technology Acceptance Model, Digital learning, Self-determination theory
Abstract: The article focuses on mobile-based intelligent tutoring systems (M-ITS) and their role in enhancing personalized learning within digital education. It reviews theoretical foundations, including self-determination theory (SDT) and the technology acceptance model (TAM), and presents the Mobile Adaptive Personalized Tutoring (MAPT) framework, which integrates learner profiling, knowledge representation, pedagogical intelligence, mobile interfaces, and evaluation analytics. Empirical evidence indicates that M-ITS improve academic performance and learner engagement across diverse populations, including neurodivergent students and English as a Foreign Language (EFL) learners, with emerging generative AI (GenAI) components enabling dynamic, conversational tutoring. The article also discusses technical, pedagogical, and ethical challenges related to scalability, data privacy, and inclusivity, and suggests future research directions emphasizing longitudinal studies, cross-cultural validation, and human-AI collaboration in education. [Extracted from the article]
Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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: Engineering Source
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  Data: Mobile-Based Intelligent Tutoring Systems: Enhancing Personalized Learning in Digital Education.
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  Data: <searchLink fieldCode="AR" term="%22Akhter%2C+Shamim%22">Akhter, Shamim</searchLink><relatesTo>1</relatesTo><i> shamim.akhter@newinti.edu.my</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Tribhuwan%22">Kumar, Tribhuwan</searchLink><relatesTo>2</relatesTo><i> t.kumar@psau.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Shaheen%2C+Musart%22">Shaheen, Musart</searchLink><relatesTo>3</relatesTo><i> shaheenmusarat332@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2026, Vol. 20 Issue 9, p4-16. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+instruction%22">Individualized instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Data+privacy%22">Data privacy</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Acceptance+Model%22">Technology Acceptance Model</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+learning%22">Digital learning</searchLink><br /><searchLink fieldCode="DE" term="%22Self-determination+theory%22">Self-determination theory</searchLink>
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  Label: Abstract
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  Data: The article focuses on mobile-based intelligent tutoring systems (M-ITS) and their role in enhancing personalized learning within digital education. It reviews theoretical foundations, including self-determination theory (SDT) and the technology acceptance model (TAM), and presents the Mobile Adaptive Personalized Tutoring (MAPT) framework, which integrates learner profiling, knowledge representation, pedagogical intelligence, mobile interfaces, and evaluation analytics. Empirical evidence indicates that M-ITS improve academic performance and learner engagement across diverse populations, including neurodivergent students and English as a Foreign Language (EFL) learners, with emerging generative AI (GenAI) components enabling dynamic, conversational tutoring. The article also discusses technical, pedagogical, and ethical challenges related to scalability, data privacy, and inclusivity, and suggests future research directions emphasizing longitudinal studies, cross-cultural validation, and human-AI collaboration in education. [Extracted from the article]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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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        Value: 10.3991/ijim.v20i09.61509
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      – Code: eng
        Text: English
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        PageCount: 13
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    Subjects:
      – SubjectFull: Intelligent tutoring systems
        Type: general
      – SubjectFull: Individualized instruction
        Type: general
      – SubjectFull: Data privacy
        Type: general
      – SubjectFull: Technology Acceptance Model
        Type: general
      – SubjectFull: Digital learning
        Type: general
      – SubjectFull: Self-determination theory
        Type: general
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      – TitleFull: Mobile-Based Intelligent Tutoring Systems: Enhancing Personalized Learning in Digital Education.
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            – D: 01
              M: 05
              Text: 2026
              Type: published
              Y: 2026
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