AI-Enabled Smart Wearable Learning Systems for Personalized Education Through Real-Time Sensing and Learning Analytics Innovation

Abstract

AI-enabled smart wearable learning systems can be regarded as a new innovative way of offering personalized and adaptive learning experiences by using the sensing and learning analytics capabilities. In the current research, the impact of the AI-enabled smart wearable learning systems on engagement, learning achievement, and instructional innovation is investigated. A sequential explanatory design with mixed methodology was applied with 320 participants and 24 teachers participating. Quantitative data were collected from the use of wearable sensors, questionnaires, pre-tests and post-tests, and learning analytics data from the AI-assisted learning system, while qualitative data came from interviews and focus group discussions. Quantitative data have been analyzed using descriptive statistics, Pearson correlation, multiple regression, and partial least squares structural equation modeling, while qualitative data have been analyzed using thematic analysis. The results showed that academic performance improved from 62.4 in the pre-test phase to 78.6 in the post-test phase. The module completion rate of the system was 86.5%, AI-feedback use was 79.4%, learner engagement was 87.3%, and the accuracy of the wearable sensor was 96.2%. Of academic achievement, student-centered innovation (β = 0.38) and the effectiveness of learning analytics (β = 0.34) showed to be statistically the most significant.



Received: 7 July 2026 | Revised: 11 September 2026 | Accepted: 22 September 2026



Conflicts of Interest

The author declares that he has no conflicts of interest to this work.



Data Availability Statement

Data are available from the corresponding author upon reasonable request.



Author Contribution Statement

Olarewaju Peter Ayeoribe: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.