Smart Classroom Seat Allocation System Using IoT, Artificial Intelligence, Machine Learning and Optimization Techniques: A Review

Abstract: The transformation of conventional classrooms into intelligent learning environments has created new opportunities for automating classroom management and improving student learning experiences. Among these challenges, student seat allocation is an important task because seating arrangements can influence accessibility, classroom interaction, student engagement, learning conditions and effective utilization of available space. Traditional seat allocation is predominantly performed manually and becomes increasingly complex when the number of students, classrooms and allocation constraints increases. A Smart Classroom Seat Allocation System can address these limitations by integrating the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML) and optimization techniques. This paper presents a comprehensive review of research published between 2020 and 2026 concerning classroom seating, intelligent seat allocation, smart classroom technologies, IoT-based monitoring and optimization-based allocation approaches. Existing studies have explored genetic algorithms, mathematical programming, graph-based heuristics, agent-based modelling, sociometric sensing, RFID, IoT devices and automated classroom management systems. Research findings indicate that seating position and proximity can affect student engagement, academic performance, peer interaction and social relationships. IoT technologies can further provide real-time information regarding seat occupancy and classroom conditions, enabling dynamic allocation and monitoring. However, existing approaches frequently address individual aspects of the problem, such as examination seating, social distancing, classroom interaction or occupancy monitoring, rather than providing an integrated allocation framework. Therefore, there is a significant opportunity to develop an adaptive system that combines student characteristics, classroom constraints, seat availability, accessibility requirements, learning preferences and real-time occupancy information within a unified optimization framework. This review examines existing approaches, identifies their limitations and research gaps and discusses potential applications and future directions for developing intelligent, flexible and scalable smart classroom seat allocation systems.

Keywords: Smart Classroom, Seat Allocation, Artificial Intelligence, Machine Learning, Internet of Things, Optimization, RFID, Student Management, Smart Education.