Olutosin Taiwo

Middle school is a key window for building core academic skills and the learning routines students carry into later grades, yet many students still fall behind because help is often limited and comes too late, after they have already been stuck for a while. Learning Management Systems (LMSs) are now standard infrastructure for distributing materials, collecting work, assessing students’ tasks, and recording grades, but in most deployments they still behave more like workflow tools than instructional supports. The result is the usual bottleneck: students keep practicing through confusion, teachers triage questions, and feedback that could have corrected the misunderstanding arrives after the misconception has already hardened. To address this gap, we propose an AI-integrated LMS for middle school instruction, paired with a longitudinal study design to test whether sustained, bounded AI support changes outcomes through high school and into post-high school pathways. The proposed platform adds policy-gated AI assistance to everyday coursework, delivering formative feedback and hinting, recommending spaced review and adaptive practice based on mastery, and providing teacher-facing dashboards that summarize misconception patterns and flag sustained struggle. Because the platform is intended for minors, the design is privacy-first, using data minimization, role-based access control, age-appropriate response constraints, and auditable logs of AI interactions. Beyond short-term performance, the evaluation plan links fine-grained learning traces (attempts, revisions, help-seeking, and pacing) to institutional outcomes where feasible, so we can separate tool adoption effects from longer-run changes in learning trajectories.

Security of lives and properties is highly important for enhanced quality living. Smart home automation and its application have received much progress towards convenience, comfort, safety, and home security. With the advances in technology and the Internet of Things (IoT), the home environment has witnessed an improved remote control of appliances, monitoring, and home security over the internet. Several home automation systems have been developed to monitor movements in the home and report to the user. Existing home automation systems detect motion and have surveillance for home security. However, the logical aspect of averting unnecessary or fake notifications is still a major area of challenge. Intelligent response and monitoring make smart home automation efficient. This work presents an intelligent home automation system for controlling home appliances, monitoring environmental factors, and detecting movement in the home and its surroundings. A deep learning model is proposed for motion recognition and classification based on the detected movement patterns. Using a deep learning model, an algorithm is developed to enhance the smart home automation system for intruder detection and forestall the occurrence of false alarms. A human detected by the surveillance camera is classified as an intruder or home occupant based on his walking pattern. The proposed method’s prototype was implemented using an ESP32 camera for surveillance, a PIR motion sensor, an ESP8266 development board, a 5 V four‐channel relay module, and a DHT11 temperature and humidity sensor. The environmental conditions measured were evaluated using a mathematical model for the response time to effectively show the accuracy of the DHT sensor for weather monitoring and future prediction. An experimental analysis of human motion patterns was performed using the CNN model to evaluate the classification for the detection of humans. The CNN classification model gave an accuracy of 99.8%.

The smart home is now an established area of interest and research that contributes to comfort in modern homes. With the Internet being an essential part of broad communication in modern life, IoT has allowed homes to go beyond building to interactive abodes. In many spheres of human life, the IoT has grown exponentially, including monitoring ecological factors, controlling the home and its appliances, and storing data generated by devices in the house in the cloud. Smart home includes multiple components, technologies, and devices that generate valuable data for predicting home and environment activities. This work presents the design and development of a ubiquitous, cloud-based intelligent home automation system. The system controls, monitors, and oversees the security of a home and its environment via an Android mobile application. One module controls and monitors electrical appliances and environmental factors, while another module oversees the home’s security by detecting motion and capturing images. Our work uses a camera to capture images of objects triggered by their motion being detected. To avoid false alarms, we used the concept of machine learning to differentiate between images of regular home occupants and those of an intruder. The support vector machine algorithm is proposed in this study to classify the features of the image captured and determine if it is that of a regular home occupant or an intruder before sending an alarm to the user. The design of the mobile application allows a graphical display of the activities in the house. Our work proves that machine learning algorithms can improve home automation system functionality and enhance home security. The work’s prototype was implemented using an ESP8266 board, an ESP32-CAM board, a 5 V four-channel relay module, and sensors.

The use of modern technologies for control and monitoring and accessing devices in domestic or industrial buildings with convenience, comfortable and easy access from any location is the primary aim of the internet of things (IoT) technology for smart home automation. Complete Smart home automation, with overall control from any place at any time is still not fully available. Nevertheless, this work proposes a mobile application system for smart homes, with the purpose of overall monitoring and control of home appliances and devices. The proposed method is based on Zigbee, Arduino and Bluetooth for wireless communication among devices in the home. At the same time, a mobile application is used for the control and monitoring of the devices or appliances. In this study, Zigbee and Bluetooth are combined in order to establish efficient communication either within or outside the home premises. A user scenario of the proposed work was simulated using Proteus Simulation software to validate the practicability of the new system.

Smart home is an evolving technological innovation that originates from the numerous application areas of the Internet of Things (IoT). As the world is driving more and more closer to adopting smart cities based infrastructural environments, in which most activities involve innovative technological connectivity, smart home automation is one of the focused areas which has grown exponentially in the last few decades. The main objective of smart home automation is to make life easier and convenient for homeowners and users. The role of smart home automation is essential to healthcare and the social and economic well-being of all users through the provision of a convenient and conducive place of living. With the spontaneous evolution of new trends in smart home automation design, the various elements and functions of devices used in building smart home systems need to be explained as these technologies have significant benefits. They can assist traditional methods of controlling and monitoring home appliances, improve healthcare for the elderly and disabled, alert homeowners in case of potential risk and enable homes to be controlled even while the owner is far from home. Most importantly, smart home systems have great potential for reduction in energy loss. Furthermore, to provide valuable insights into these technological environments, we must clearly understand some of the available options and gaps in the area of smart home automation systems. This paper presents a taxonomy of IoT smart home automation systems with elaborate discussions on the technologies, trends and challenges in smart home automation system design. A constructive and detailed review of existing literature based on application areas of smart home automation is presented. Lastly, highlights of the approaches, technologies, and strength and weaknesses associated with the smart home automation systems are discussed.