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

Shamim Akhter; Tribhuwan Kumar; Musart Shaheen

doi:10.3991/ijim.v20i09.61509

Authors

Shamim Akhter INTI International University, Nilai, Malaysia
Tribhuwan Kumar Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia
Musart Shaheen Bahauddin Zakariya University, Multan, Pakistan

DOI:

https://doi.org/10.3991/ijim.v20i09.61509

Keywords:

Mobile Learning (m-learning), Intelligent Tutoring Systems (ITS), Personalized Learning, Artificial Intelligence in Education, Quality education, Gamification, Generative AI

Abstract

The proliferation of mobile technologies in educational contexts has catalysed the development of intelligent tutoring systems (ITS) that leverage artificial intelligence (AI) to deliver personalized, adaptive learning experiences. This paper presents a comprehensive review and conceptual framework examining the integration of mobile-based intelligent tutoring systems (M-ITS) in digital education environments. Drawing on self-determination theory (SDT), the technology acceptance model (TAM), and constructivist learning principles, this study investigates how M-ITS architectures comprising student modelling, domain modelling, pedagogical modules, and adaptive interfaces can enhance learner engagement, knowledge acquisition, and academic performance. Through a systematic analysis of recent empirical studies, theoretical frameworks, and technological implementations, the paper identifies critical design principles for effective M-ITS deployment, including personalization algorithms, gamified learning components, natural language processing capabilities, and real-time feedback mechanisms. Evidence suggests that M-ITS significantly improves learning outcomes across diverse learner populations, including neurodivergent students, English as a Foreign Language (EFL) learners, and higher education cohorts. Furthermore, the role of generative artificial intelligence (GenAI) as an emerging component in M-ITS is examined, highlighting its potential for dynamic content generation and conversational tutoring. The study concludes by proposing a research agenda addressing scalability, data privacy, pedagogical alignment, and cross-cultural adaptability of mobile intelligent tutoring solutions. These findings carry substantial implications for educators, instructional designers, policymakers, and technology developers working at the intersection of mobile learning and AI-driven pedagogy.

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