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p-Index From 2021 - 2026
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All Journal Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC)
John Kamau
Mount Kenya University
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

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AI-Powered Mobile Proctoring Frameworks using Machine Learning Algorithms in Higher Education: Post-Covid Trends, Challenges, and Ethical Implications Bartholomew Oganda Mogoi; John Kamau; Raymond Ongus
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3600

Abstract

The rapid transition to online learning during and after the COVID-19 (Corona Virus Disease) pandemic has heightened the need for secure, scalable, and ethical online exam systems. AI-powered mobile proctoring frameworks have emerged as viable alternatives to traditional invigilation methods, enabling automated anomaly detection and behavior analysis through machine learning algorithms. This systematic review examines post-COVID trends, technological developments, challenges, and ethical implications of mobile AI proctoring in higher education. Following PRISMA 2020 guidelines, 180 studies were retrieved and screened, with 20 peer-reviewed articles meeting the inclusion criteria. Findings reveal that while AI-powered proctoring enhances scalability, integrity, and real-time monitoring, it raises significant concerns about privacy, algorithmic bias, accessibility, and technical reliability. The review identifies gaps in relation to technical and methodological issues, ethical and social concerns, and institutional and infrastructural readiness. This review illustrates a lapse in the existing literature, which focus on resource intensive proctoring frameworks without considering mobile compatibility and light-weight frameworks, discusses technical challenges, and recommends future research directions to balance technological effectiveness with ethical standards.
Multimodal CNN–LSTM Framework for Real-Time Maize Disease Detection Mercy Chepkoech Tonui; John Kamau; Raymond Wafula Ongus
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 2 (2026): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i2.3970

Abstract

Maize diseases present a major challenge to agricultural productivity and food security, particularly in low-resource settings in sub-Saharan Africa. Timely detection plays an important role in reducing yield losses and enabling effective farm management. This research introduces and validates a multimodal machine learning–based system for real-time maize disease detection in Bomet County, Kenya. The system integrates maize leaf image data, environmental sensor data, and farmer-reported observations to develop a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to automatically identify and categorize maize diseases. A mixed-methods research design was adopted, combining machine learning experiments with surveys and interviews involving farmers and agricultural officers. The findings revealed that Maize Lethal Necrosis (MLN) was the most prevalent disease (41%), followed by Gray Leaf Spot (33%) and Northern Leaf Blight (26%). Environmental variables such as humidity and temperature demonstrated strong associations with disease occurrence. The proposed multimodal CNN–LSTM framework integrates maize leaf images, environmental sensor data, and farmer observations, achieving an accuracy of 94.2%, which outperforms conventional image-only CNN models (87.5%) and environmental-data-based LSTM models (81.3%). Additionally, 78% of farmers reported faster disease diagnosis using the developed system. The findings demonstrate that the proposed system supports real-time maize disease detection through an edge-enabled architecture, enabling deployment on mobile devices and facilitating practical intelligent system integration in agricultural environments.
Co-Authors Bartholomew Oganda Mogoi Mercy Chepkoech Tonui Raymond Ongus Raymond Wafula Ongus