JOURNAL INFORMATION AND TECHNOLOGY MANAGEMENT (JISTM)

DIGITAL LITERACY AND LEARNING ADAPTABILITY AMONG UNIVERSITY STUDENTS IN CHINA: A CONCEPTUAL REVIEW IN THE ERA OF DIGITAL EDUCATION TRANSFORMATION

Zhao Hui , Muhammad Nazir Ahmad — Thu, 03 Sep 2026 00:00:00 +0800

The rapid advancement of digital technologies has fundamentally transformed higher education, reshaping how students acquire knowledge, interact with learning resources, and engage in academic activities. As universities increasingly adopt technology-enhanced learning environments, students are expected to develop not only digital competencies but also the capacity to adapt effectively to continuous pedagogical and technological changes. Consequently, digital literacy and learning adaptability have emerged as two essential competencies for success in contemporary higher education. This conceptual paper examines the relationship between digital literacy and learning adaptability among university students in China. Drawing on the Digital Competence Framework for Citizens (DigComp 2.2), digital literacy is conceptualised as a multidimensional construct comprising five core domains: information and data literacy, digital communication and collaboration, digital content creation, digital safety, and digital problem-solving. Learning adaptability is defined as students’ ability to adjust their cognitive, behavioural, emotional, motivational, and social responses to evolving learning environments and academic demands. The study employs a Narrative Literature Review (NLR) approach to synthesise and critically interpret existing literature related to both constructs. The thematic synthesis indicates that digital literacy serves as a foundational enabler of learning adaptability by enhancing students’ ability to navigate digital environments, engage in self-directed learning, and respond effectively to technological and pedagogical changes. The review also identifies significant research gaps, particularly within the context of Chinese higher education, where empirical evidence on the relationship between digital literacy and learning adaptability remains limited. Overall, this paper contributes to the growing discourse on educational digital transformation by providing a conceptual framework that integrates digital competencies with adaptive learning capabilities. The proposed framework offers a theoretical foundation for future empirical research and highlights the importance of fostering both competencies to enhance student success and lifelong learning readiness in an increasingly digital educational landscape.

ADOPTION OF DRONE TECHNOLOGY: A PRELIMINARY STUDY AMONG RICE FARMERS IN KADA, KELANTAN

Nor Diana Mohd Idris, Loyola Olga Pius — Thu, 10 Sep 2026 00:00:00 +0800

Despite increasing government support for precision agriculture, empirical evidence on the determinants of drone technology among Malaysian paddy farmers remains limited. This preliminary study addresses this gap by examines the early-stage drivers of drone technology adoption among paddy farmers in Malaysia's Kemubu Agricultural Development Authority (KADA) region. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study investigates the role of performance expectancy and social influence in shaping adoption intention. A quantitative survey involving 50 systematically selected farmers were analysed using descriptive statistics, Pearson correlation, and chi-square tests. As a pilot study, the research aimed to evaluate the feasibility of the research instrument and generate preliminary evidence for a larger empirical investigation. Results indicate that social influence demonstrated a stronger positive association with adoption intention than performance expectancy (r = 0.57, p < 0.001). Younger farmers (<40 years) and those with larger farm sizes (>5 acres) reported significantly higher adoption intention, suggesting demographic disparities in uptake. This study extends the application of UTAUT to precision agriculture by providing preliminary evidence from smallholder paddy farmers in Malaysia, a context that remains underexplored in the technology adoption literature. The findings suggest that agricultural extension programmes should leverage peer influence and demonstration farms to accelerate drone adoption, particularly among older farmers and those operating smaller farms. This study offers important groundwork for refining Malaysia’s agricultural modernisation strategies under the National Agro-Food Policy (2021-2030).

RESEARCH LANDSCAPE OF SUPPLY CHAIN RISKS IN CONSTRUCTION: A BIBLIOMETRIC REVIEW

Muhamad Norsyafiq Muhamad Nazri, Ahmad Shabudin Ariffin — Thu, 03 Sep 2026 00:00:00 +0800

The increasing complexity of global construction activities has intensified exposure to supply chain disruptions, creating significant challenges related to procurement, logistics, infrastructure vulnerability, and project sustainability. Despite the growing importance of supply chain risk management in the construction industry, existing studies remain fragmented, with limited understanding of the intellectual structure, research evolution, and emerging thematic trends within this domain. Therefore, this study aims to systematically analyse the research landscape of supply chain risks in the construction industry through a comprehensive bibliometric approach. Data were collected from the Scopus database using an advanced search strategy based on three primary keyword groups: supply chain, risk, and construction industry. Following the screening and refinement process, a total of 76 relevant publications were selected for analysis. The collected bibliographic data were subsequently analysed using the Scopus Analyzer to evaluate publication trends, citation structures, leading countries, influential articles, and source titles. To improve data consistency and reliability, OpenRefine was utilized for data cleaning and harmonization, while VOSviewer was employed to construct visualization networks for keyword co-occurrence and thematic mapping. The findings reveal a significant increase in publication output after 2019, indicating growing scholarly attention toward construction supply chain resilience, infrastructure vulnerability, and sustainability-related risks. The United States, China, and the United Kingdom emerged as the leading contributors to the field, while keyword analysis identified “supply chain management,” “critical infrastructure,” and “vulnerability” as dominant research themes. The study contributes to the body of knowledge by providing a structured overview of the research domain, identifying emerging research directions, and offering valuable insights for researchers and industry practitioners seeking to strengthen supply chain resilience and risk management strategies in the construction sector.

MIXED REALITY (MR) MODEL BASED ON VISUAL INTERACTION (VI) DESIGN PRINCIPLES FOR DEAF LEARNERS: A SYSTEMATIC LITERATURE REVIEW (SLR)

Tue, 15 Sep 2026 00:00:00 +0800

Although Mixed Reality (MR) technologies have increasingly been explored to support Deaf education, existing studies primarily emphasize technological capabilities rather than the visual interaction requirements and cognitive needs of Deaf learners. This study presents a systematic literature review (SLR), following the PRISMA guidelines, to examine MR applications developed for Deaf individuals between 2015 and 2025. Five academic databases were searched using predefined inclusion and exclusion criteria. Eleven MR applications for Deaf individuals were identified, of which six focused specifically on educational settings and were further analysed to examine their features, interaction approaches, and design limitations. The findings indicate that while existing applications incorporate advanced features such as 3D avatars, gesture recognition, and gamified interaction, none systematically integrates Visual Interaction (VI) design principles to support clarity, consistency, accessibility, and cognitive usability. Furthermore, no MR application was specifically developed for Malaysian Deaf learners. Based on these findings, this paper proposes the Mixed Reality–Visual Interaction Design for Deaf Learners (MRVI-DL), a design framework that integrates MR capabilities with VI principles to support the development of accessible and cognitively appropriate learning environments for Deaf learners.

STRUCTURE-BASED VALIDATION OF REINFORCEMENT-LEARNING-GENERATED JAK2 TYPE II INHIBITOR CANDIDATES: MOLECULAR DOCKING RESOLVES THE 1D REWARD-HACKING ARTIFACTS OF SINGLE-OBJECTIVE OPTIMIZATION

Xuelan Yang, Jasni Mohamad Zain, Gembong Edhi Setyawan, Diva Kurnianingtyas — Mon, 07 Sep 2026 00:00:00 +0800

Reinforcement learning (RL) is proving to be a powerful way to tackle generative molecular design. Optimizing molecules using 1D surrogate reward functions, however, is frequently not sufficient to include the biophysical constraints that are necessary for successful drug discovery. Heuristic reward hacking is a problem with existing RL algorithms, which when fed with generated molecules can maximize the heuristic reward, but not produce a meaningful increase in pharmacological activity. This problem is addressed by Janus Kinase 2 (JAK2) Type II inhibitors which represent a challenging standard to meet in three-dimensional structure. We demonstrate that the same 1D surrogate can be used in two ways which are essentially different. Without any constraints, the unconstrained optimization using REINFORCE will lead to a “fragment trap”, which means molecules with low MW and lower than normal “drug-likeness” scores. Constrained optimization, on the other hand, with Proximal Policy Optimization (PPO) leads to molecules that are bigger and more hydrophobic than they need to be, taking advantage of the biases that are in the favor of molecular size and hydrophobicity. Both strategies do not result in the moderate complexity chemotypes essential for an effective JAK2 Type II inhibitor. Therefore, we propose as a orthogonal unsupervised validation step three-dimensional molecular docking as a structure-based surrogate independent of the surrogate reward. Docking re-centers molecular selection by giving preference to candidates in an intermediate molecular weight range (around 470-550 Da) typical of the deep DFG-out allosteric pocket, and systematically down-ranking undersized and oversized molecules. Empirically calculated docking scores are prone to bias based on the size and lipophilicity of the ligands so score differences should be taken with a pinch of salt and the docking is used as a confirmatory filter rather than an absolute affinity oracle. The results in this study show that the validation of molecular 3D structures should be added as another alignment tool in the generative molecular design process, and that the integration of multiple objectives in drug discovery with physically informed knowledge is desirable.

POINT CLOUD PROCESSING ON 3D LIDAR DATA FOR UNMANNED SURFACE VEHICLES NAVIGATION IN MARINE ENVIRONMENTS

Aiman Norazaruddin, Zulkifli Zainal Abidin — Tue, 15 Sep 2026 00:00:00 +0800

Unmanned Surface Vehicles (USVs) require reliable autonomous navigation systems in dynamic maritime environments. LiDAR sensors, while effective for obstacle detection, are susceptible to noise and distortion from wave oscillations and water surface absorption, generating false positives and negatives in point cloud data. Most existing preprocessing-clustering evaluations are confined to terrestrial or simulated settings, with limited benchmarking under real maritime conditions marked by wave-induced motion, surface reflections, and sparse returns. This study addresses that gap through one of the first systematic evaluations of preprocessing-clustering combinations using real harbour LiDAR data collected under genuine environmental disturbance. A Velodyne HDL-32E LiDAR sensor was deployed at Klang Harbour, Malaysia, to collect real-world point cloud datasets. Three preprocessing techniques — Voxel Grid Downsampling, Statistical Outlier Removal (SOR), and Radius Outlier Removal (ROR) — were evaluated with two clustering algorithms chosen for their contrasting noise-handling strategies: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Euclidean Cluster Extraction (ECE). Performance was assessed using precision and accuracy against manually annotated ground truth. DBSCAN combined with SOR achieved the highest performance, with peak accuracy of 57% (ε = 1.0 m) and peak precision of 62% (ε = 2.0 m), as SOR removes wave-induced outliers while preserving obstacle geometry, enabling DBSCAN's density criterion to isolate genuine clusters more reliably. ROR consistently degraded performance by over-removing genuine sparse returns, while ECE underperformed across all configurations due to its lack of native noise handling. The overall pipeline reached approximately 60% accuracy, a modest ceiling reflecting persistent wave motion, surface reflections, sparse long-range returns, and moving vessels that geometry-based clustering alone cannot resolve. This study contributes a methodological benchmark for preprocessing-clustering pipeline selection in maritime LiDAR processing, and practical guidance for selecting efficient, noise-robust configurations for real-time USV obstacle detection. Future work should incorporate model-based object detection and multi-sensor fusion to improve classification capability and system reliability.

DIGITAL TRANSFORMATION IN HIGHER EDUCATION AND THE DEVELOPMENT OF STUDENT LEARNING ADAPTABILITY: A REVIEW OF CONTEMPORARY LITERATURE AND BIBLIOMETRIC TRENDS

Zhao Hui, Muhammad Nazir Ahmad — Mon, 07 Sep 2026 00:00:00 +0800

Digital transformation has become a dominant agenda in higher education worldwide, fundamentally changing educational practices, institutional management, and student learning experiences. The integration of digital technologies has accelerated the adoption of online learning, blended learning, intelligent educational systems, and data-driven pedagogical approaches. Consequently, students are increasingly required to demonstrate learning adaptability in response to rapidly changing educational environments. This paper presents a bibliometric and thematic analysis of contemporary literature related to digital transformation, digital literacy, and learning adaptability in higher education. The study draws upon bibliometric data from Web of Science (WoS) and China National Knowledge Infrastructure (CNKI) to examine publication trends, emerging research themes, and intellectual structures within the field. The findings reveal substantial growth in scholarly attention toward digital transformation, digital literacy, and adaptive learning over the past decade, particularly following the COVID-19 pandemic. However, the analysis highlights a persistent fragmentation in existing literature, where digital literacy and learning adaptability are often examined as separate constructs rather than as interconnected dimensions within a digital learning ecosystem. In addition, the review identifies a significant research imbalance, with most studies focusing on macro-level institutional and technological developments, while micro-level student learning experiences remain underexplored, especially in the Chinese higher education context. This paper further proposes that learning adaptability should be understood as a key outcome influenced by digital literacy within digitally transformed learning environments. The study concludes by highlighting the need for more integrated theoretical frameworks that connect digital competence development with adaptive learning capabilities to support sustainable digital transformation in higher education.

DEVELOPING AN ENTITY–RELATIONSHIP DATA MODEL FOR GIS-BASED ANALYSIS OF RECREATIONAL PARK ACCESSIBILITY AND DISTANCE FROM RESIDENTIAL AREAS

Thu, 10 Sep 2026 00:00:00 +0800

Recreational parks play an important role in promoting physical activity, social interaction, environmental sustainability, and healthy urban environments. The assessment of recreational park accessibility requires the integration of spatial and non-spatial datasets representing parks, residential areas, transportation networks, access points, and accessibility indicators. However, existing GIS-based recreational park accessibility studies have predominantly focused on accessibility measures, spatial equity, network analysis, and accessibility indices, while comparatively less attention has been given to the conceptual database structures required to organise and integrate the datasets underpinning these analyses. This study therefore develops an Entity–Relationship Data Model (ERD) as a conceptual GIS database structure for assessing recreational park accessibility and spatial distance between recreational parks and residential areas in Seri Iskandar, Perak. The proposed ERD is specifically designed to represent the spatial and attribute relationships required for park accessibility and distance/proximity assessment rather than to conduct the accessibility analysis itself. The database development process comprises the identification of required spatial and non-spatial datasets, formulation of entities and attributes, and identification of relationships among entities using Chen’s ERD notation. Five (5) core entities are identified: Recreational Park, Residential Area, Road Network, Park Access Point, and Accessibility Assessment. The resulting model establishes primary and foreign key relationships and provides a structured foundation for storing park characteristics, residential information, transportation network data, access locations, and accessibility assessment attributes such as distance and travel time. The proposed ERD is designed to support subsequent GIS-based proximity and network accessibility analyses. The primary contribution of this study is therefore a conceptual GIS database model that improves the systematic organisation, consistency, and integrity of data required for recreational park accessibility assessment. The model can be further implemented and adapted to support recreational park planning and accessibility assessment in other study areas.

RAINFALL- DRIVEN SATELLITE-BASED MOBILE EARLY WARNING SYSTEM FOR LANDSLIDE RISK ASSESSMENT IN ULU KLANG, MALAYSIA

Thu, 03 Sep 2026 00:00:00 +0800

Malaysia's equatorial tropical climate is characterised by high rainfall throughout the year, making the country particularly susceptible to rainfall-induced landslides. Landslides can have serious environmental and socioeconomic impacts including loss of lives, damage to infrastructure, damage to properties, and long-term psychological effects on affected communities. Geological factors coupled with intense rainfall have been identified as key factors in slope failures in many high-risk zones . However, existing landslide early warning systems in Malaysia are often limited by limited accessibility, reliance on ground-based monitoring infrastructure, or the lack of user-friendly mobile platforms that can provide timely risk information to the general public. This study presents a mobile early warning system based on satellite rainfall observations by integrating Global Precipitation Measurement (GPM) rainfall information with a cross-platform mobile application for near real-time landslide risk assessment. The GPM rainfall estimates were pre-processed into JSON format and analyzed using cumulative rainfall thresholds over daily, 3-day and 30-day periods before being visualised through a React Native mobile application. The processed rainfall information was implemented as a cross-platform mobile application accessible on Android and iOS devices. Functional testing showed successful processing of GPM rainfall inputs, implementation of the validated risk assessment model, generation of corresponding landslide warning levels, and visualization of rainfall conditions in an interactive mobile interface. The results demonstrate the potential of combining satellite rainfall observations with mobile technology to promote community-based landslide preparedness and provide a scalable approach to disaster risk reduction in Malaysia.

GENERATIVE AI ADOPTION FOR ACADEMIC LEARNING AMONG FOUNDATION STUDENTS IN MALAYSIA: AN APPLICATION OF UTAUT2

Thu, 10 Sep 2026 00:00:00 +0800

Generative artificial intelligence (GenAI) is becoming more common in education, particularly as students use AI tools to support their learning and academic work. Despite this growing use, little research has focused on what influences GenAI use among foundation students in Malaysia. This study examines students’ intention to use GenAI and their actual use of it for academic learning based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). A quantitative approach was used, with data collected from Malaysian foundation students through a structured questionnaire. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results showed that performance expectancy, social influence, habit, and behavioural intention had significant effects, whereas effort expectancy, facilitating conditions, hedonic motivation, and price value did not significantly affect behavioural intention. Facilitating conditions and habit also had significant effects on actual use behaviour. Overall, the results indicate that foundation students are more likely to use GenAI when they see clear academic benefits, are influenced by their surrounding environment, and have developed regular usage habits. The study adds to existing GenAI research by providing evidence from Malaysian foundation students and applying UTAUT2 within a pre-university setting.

REAL-TIME TEMPERATURE MONITORING AND CONTROL OF A POWER INVERTER IN A WIRELESS POWER TRANSFER SYSTEM

Thu, 03 Sep 2026 00:00:00 +0800

This study presents a real-time temperature monitoring and control system for a Class E inverter in a low-power wireless power transfer prototype. The system was designed to monitor voltage, current, temperature and output power while supporting zero-voltage switching and protecting the inverter from overheating. MATLAB Simulink was used to model the transmitter and receiver stages, while Proteus supported PWM generation, control logic, and PCB design. The hardware prototype integrated a microcontroller, MOSFET driver, MOSFET, resonant coil network, rectifier, temperature sensor, LCD display, relay-controlled cooling fan and ESP32-based IoT monitoring. Simulation and hardware testing confirmed high-frequency power transfer, rectified DC output, and automatic fan activation at the 37 °C threshold. The findings show that active thermal control improves the safety, stability, and practicality of low-power wireless power transfer systems.

AI-SMART GLASSES FOR THE INTELLIGENT WASTE MANAGEMENT SYSTEMS

Marizuana Mat Daud, Fazlina Mohd Ali, Mohammad Nazir Ahmad — Thu, 10 Sep 2026 00:00:00 +0800

In this paper, we develop and pilot a Smart Bin Management System that combines smart glasses and QR code scanning technology to enhance the urban waste collection system. The current waste management method is not well-suited to the problem of monitoring and data accuracy and so the operations are very expensive. Utilizing the computer vision and display capabilities of smart glasses, it helps waste management personnel to scan QR codes on the phone without the need of tools and allows waste bins to be identified and reported. In collaboration with KDEB Waste Management Sdn Bhd (KDEBWM) our project was designed and tested to see if this wearable solution works. A total of 203 QR codes were successfully scanned and detected with a 68.97% success rate. A detailed analysis suggests that the delays of images to scan over more than one minute were mainly due to hardware difficulties and not the cloud database or QR architecture. In particular, the continuous capture and streaming of data over Bluetooth Low Energy (BLE) caused system problems and caused the device to shut down immediately and the data to not be readable. We believe that the current smart glass system will have to be reworked to overcome such deficiencies, and the proposed framework is well established for future wearable industrial applications in municipal waste management.

CONFIDENCE-WEIGHTED MID-LEVEL LIDAR–STEREO FUSION WITH HYBRID PCA-K-MEANS CLUSTERING FOR MARITIME 3D OBJECT DETECTION

Muhammad Aiman Norazaruddin, Zulkifli Zainal Abidin — Tue, 15 Sep 2026 00:00:00 +0800

Autonomous Unmanned Surface Vessels (USVs) operating in nearshore environments require robust perception systems capable of handling challenging maritime conditions. However, LiDAR and stereo vision each suffer from complementary limitations: LiDAR produces sparse and reflection-sensitive measurements, while stereo vision provides dense depth estimates but performs poorly under occlusion and weak-texture conditions. While early- and late-fusion approaches have been widely explored, mid-level fusion strategies that preserve cross-modal feature interactions while adapting to sensor uncertainty remain underdeveloped for maritime perception. This paper introduces M-ACF Net (Maritime Adaptive Confidence Fusion Network), a confidence-adaptive mid-level fusion framework that dynamically integrates sparse LiDAR and dense stereo features based on pixel-level reliability. The fused representation is processed using PCA-K-Means clustering to generate orientation-invariant obstacle representations for Guidance, Navigation, and Control (GNC). The framework was implemented on an instrumented USV platform and evaluated on a multimodal dataset of 17,634 synchronised LiDAR–stereo–GNSS samples collected across three nearshore Malaysian sites under wave heights of 0.1–0.5 m. Across different wave conditions, M-ACF Net reduced depth-estimation RMSE by 44–60% compared with LiDAR-only measurements. The framework achieved 82.6% 3D Average Precision for ship detection under easy conditions and outperformed YOLOv8-Stereo, PointPillars, Pseudo-LiDAR, Frustum PointNet, and DSGN baselines, while matching DBSCAN's clustering quality at less than half its computation time and sustaining 18 frames per second on an embedded NVIDIA Jetson Orin NX. Field trials further validated real-time obstacle detection under operational conditions. These results demonstrate the effectiveness of confidence-adaptive mid-level fusion for real-time maritime obstacle perception.

A HUMAN-CENTRIC RESIDENTIAL SAFETY SYSTEM FOR INDEPENDENT ELDERLY INDIVIDUALS

Mon, 07 Sep 2026 00:00:00 +0800

The number of ageing individuals who live alone increases the necessity of holistic residential safety measures. This research paper portrays the human-centred smart home safety system combining environmental hazards detection, as well as activity monitoring. The system incorporates the use of gas, flame, temperature and humidity sensors, along with (ultrasonic-based) occupancy tracking to check high-risk areas, especially bedding and toilet areas. The core of the solution is an ESP32 microcontroller that allows real-time data collection, local notifications, and IoT-based features through the Blynk and Telegram platforms. To test the performance of the system, a prototype residential model was created, which showed experimental outcomes displaying that the average alert latency was about 1.97 seconds, which means that the system is nearly able to respond to emergencies in real-time. The proposed system can offer a cost-effective, non-invasive, and practical way of improving the safety of the residence by integrating both environmental sensing and time-based analysis of activities. The results prove the practicability of incorporating the multi-sensor monitoring system into the IoT connectivity to ensure the safety of independent living of older citizens.

DESIGNING A USER-CENTRIC MOBILE INTERVENTION FOR FOOD WASTE REDUCTION IN MALAYSIAN URBAN HOUSEHOLD: A DESIGN THINKING APPROACH

Wong Hui Lin, Haliyana Khalid — Tue, 15 Sep 2026 00:00:00 +0800

In Malaysia, generation of household food waste is more serious than the food waste generated from other industries or sectors. Urban households generally generate more food wastes although high level of awareness on the impact of environmental sustainability. They aware the issue and understand the consequences, but less effective action has been taken to manage or reduce the food waste, and therefore, create a say-do-gap. This study aimed to explore the perspectives, needs, challenges, and motivations of food waste reduction in urban household Malaysia using design thinking approach. Semi-structured interview and site-observation were conducted in empathy stage. A total of 11 participants were targeted in the interview session. The 11 participants were categorized into five distinct user personas, including Health-Conscious Individual Cook, The Cultural Compliance Professional, The Green-Minded Urban Renter, The Guilty Minimalist Professional, and The Practical Disposer. Study revealed the reasons of forgetfulness, over-purchasing, and improper food storage contributed to the generation of food waste. To address the challenges, the “GLANCE” digital app for food planning and inventory tracking was designed, introduced, and tested by four users in prototype and test stages. Online survey form was distributed to the participants, and the testing results showed the participants were strongly agreed the user-friendliness and practicability of the app. Overall, the study was designed as an exploratory pilot to establish proof of concept, the app can further improve for long-term user engagement.

USER-CENTRIC INVESTIGATION ON ICT ADOPTION AMONG RURAL FARMERS IN KELANTAN

Thu, 03 Sep 2026 00:00:00 +0800

Agriculture is a major economic activity in Kelantan, where many rural communities rely on small-scale farming for their income. However, productivity remain low due to several factors such as limited mechanization and access to information. Nowadays Information and Communication Technology (ICT) play a significant role in improving agricultural activity including farm management, market access, and decision-making. Despite national initiatives to improve digital inclusion in agriculture such as the Digital Economy Blueprint (MyDIGITAL) and the National Agrofood Policy 2021–2030 (NAP 2.0), there are still a lot of challenges in ICT adoption among rural farmers in Kelantan. This study aimed to assess the reliability and suitability of a questionnaire designed to measure farmers’ perceptions toward ICT adoption and to identify preliminary patterns of awareness, attitudes, and challenges. A quantitative approach was employed using a structured questionnaire comprising 24 items with six constructs including importance, ease of use, knowledge, equipment, cost, and attitude. Data collected from 30 rural farmers were analyzed and presented using descriptive statistics. All constructs demonstrated acceptable reliability, with Cronbach alpha values ranging from 0.76 to 0.84. The findings confirm that the instrument is reliable and suitable for large-scale use while emphasizing the need for improved digital infrastructure and farmer training.

TRACING THE ISLAMIC INTELLECTUAL LEGACY IN THE CONCEPTUAL FOUNDATIONS OF GENERATIVE ARTIFICIAL INTELLIGENCE

Wan Rusydiah Salehudin, Zilal Saari, Hafiza Abas, Syamilah Saliman — Thu, 10 Sep 2026 00:00:00 +0800

The rapid development of Generative Artificial Intelligence (GenAI) has transformed the global technology landscape through the development of Large Language Models (LLMs), Deep Learning (DL) and multimodal content generation systems. Nevertheless, mainstream historiography Artificial Intelligence (AI) often situates its development within the modern Western tradition, while there are significant contributions from Islamic civilization remain underappreciated. Therefore, this study aims to examine the conceptual relationship between Islamic intellectual traditions and the intellectual foundations of GenAI through a historical and epistemological analysis. Specifically, this study seeks to (i) identify major concepts within Islamic intellectual heritage that demonstrate conceptual relevance to GenAI, (ii) analyse their significance within the broader evolution of computational thought, and (iii) discuss their implications for contemporary debates concerning AI ethics, governance and human-centred technological development. This study adopts a qualitative historical approach through a literature analysis of scholarly works related to the history of science, Islamic philosophy and GenAI. This article argues that Islamic intellectual traditions have indirectly contributed to the conceptual foundations of GenAI through the development of formal logical reasoning systems, algebraic methodologies and algorithms, as well as language and knowledge analysis structures. Although Islamic scholars did not develop GenAI in its modern form, their frameworks of thought formed part of the epistemological and technical foundations that ultimately influenced the development of modern computing and GenAI. This study also asserts that Islamic traditions offer important ethical and epistemological dimensions in understanding the limitations of AI as well as the importance of human oversight in future technological development.