From calculators to artificial intelligence: A multi-level framework for technology adoption and resistance in education and organisations

Michael Mncedisi Willie — Thu, 26 Feb 2026 00:00:00 +0000

Technological innovations are frequently associated with enhanced efficiency and improved decision-making; however, their initial adoption has often been characterised by notable resistance. This study examined the persistence of this phenomenon by synthesising empirical evidence from the historical adoption of calculators, spreadsheets, and statistical software, and by comparing these insights with contemporary developments in artificial intelligence (AI). The findings indicated that resistance extended beyond technical limitations and reflected a complex, multi-dimensional process shaped by individual factors such as self-efficacy, perceived usefulness, and anxieties related to skill displacement as well as organisational culture and broader systemic conditions. The evidence further demonstrated that successful adoption was contingent upon structured support mechanisms, incremental exposure to new technologies, and the clear articulation of value propositions. At the organisational level, effective leadership, adequate resource allocation, and coherent policy alignment emerged as critical enablers of sustained integration. Drawing on these insights, the study proposed a multi-level conceptual framework that integrates individual, organisational, and systemic determinants to guide future technology adoption. The framework underscores that meaningful and sustainable integration depends on coordinated, cross-level interventions rather than isolated initiatives. The framework also highlights the need for further empirical research into AI adoption in under-resourced contexts. It calls for critical engagement with the ethical implications of algorithmic decision-making in evolving socio-technical systems.

Image processing techniques for detection of objects in blurry pictures: A comprehensive review

Mon, 16 Mar 2026 00:00:00 +0000

Detecting objects in blurry and degraded images remains a critical unsolved challenge in computer vision, affecting applications from medical diagnostics and autonomous navigation to remote sensing and surveillance. Image degradation caused by motion, defocus, poor lighting, or environmental factors severely compromises feature visibility and limits the performance of conventional detection algorithms. This paper presents a comprehensive, systematic review of state-of-the-art techniques designed to address this problem. We first categorize common image degradations and analyze classical and deep learning-based solutions for image deblurring and enhancement, including CNN (Convolutional Neural Networks), GAN (Generative Adversarial Networks), and transformer architectures. The review then critically examines object detection models, particularly YOLO (You Only Look Once) and CNN-based networks, adapted for low-quality inputs. A key focus is on integrated pipelines that jointly optimize restoration and detection. We synthesize findings from over 200 studies, highlighting performance across diverse domains such as UAV (Unmanned Aerial Vehicle) imagery, underwater exploration, and medical analysis. Furthermore, we discuss standard datasets and evaluation metrics, identify persistent challenges including real-time processing, multi-degradation handling, and domain adaptation, and outline promising research directions. This review serves as a foundational resource for researchers and practitioners aiming to build robust vision systems for real-world, blur-prone environments.

Computing and Artificial Intelligence

From calculators to artificial intelligence: A multi-level framework for technology adoption and resistance in education and organisations

Image processing techniques for detection of objects in blurry pictures: A comprehensive review