AI-Enabled Mobile Attendance System for Higher Education: Enhancing Accuracy, Scalability, Student Engagement, and Academic Performance
DOI:
https://doi.org/10.54938/ijemdss.2026.05.1.685Keywords:
AI-Enabled Attendance System, Attendance Management, Higher Education, Smartphone-Based Learning, Student Engagement, Academic Performance, Incentive-Based Learning, Educational TechnologyAbstract
Student attendance is widely recognized as a key indicator of academic success, classroom engagement, and institutional effectiveness. Accurate and timely attendance recording enables educators to monitor student participation and identify learners who may require early intervention. However, attendance management remains a challenging task in many higher education institutions, particularly in large classes where manual attendance recording is time-consuming, disruptive to teaching, susceptible to human error, and places a considerable administrative burden on instructors.
Existing attendance management solutions, including manual roll calls, card-based systems, biometric devices, QR codes, and facial recognition technologies, often suffer from limitations such as scalability issues, infrastructure requirements, high implementation costs, privacy concerns, susceptibility to misuse, or increased processing time as class sizes grow. Moreover, many of these systems simply record attendance without motivating students to attend classes punctually or providing meaningful real-time feedback to support student success.
This paper proposes an innovative AI-enabled attendance management system that leverages the widespread availability of smartphones to automate attendance recording in higher education. As smartphones have become an indispensable part of students' daily lives, they provide a convenient, accessible, and cost-effective platform for implementing an intelligent attendance solution without requiring specialized hardware. The proposed system is accurate, fast, scalable, and fully automated, performing consistently regardless of class size. Whether a class comprises fewer than 10 students or more than 200, the system records attendance efficiently without increasing the instructor's workload. It incorporates an incentive-based mechanism that rewards punctual and regular attendance, encouraging greater student engagement and accountability. In addition, the system provides students with real-time access to their cumulative attendance records and utilizes AI-driven analytics to examine the relationship between attendance and academic performance. The findings demonstrate a strong positive correlation between regular attendance and improved academic achievement. The proposed solution offers a practical, intelligent, and scalable framework that enhances teaching efficiency, supports data-driven decision-making, and contributes to improved learning outcomes in higher education institutions.
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Copyright (c) 2025 Sidra Nazish , Izhar ul Haq
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