Authors - HSIU-FENG WANG, PEI-LUN LEE, MAO-JEN CHEN Abstract - With the advent of new developments, social robots are increasingly adopted in educational settings. The impact of social robot gestures on children’s learning outcomes, emotional responses, and affect was explored. One hundred and five elementary students participated in the experiment. Findings revealed a significant effect of robot gestures on children’s learning emotions. The impact of social robot gestures on learning outcomes and affect in children was explored. Results indicated that robot gestures significantly influenced learning emotions and knowledge retention. Children preferred interacting with the gesturing social robot, suggesting enhanced emotional engagement during learning. However, the effect on knowledge transfer was not significant. These findings underscore the potential of social robot gestures in enhancing children’s engagement and learning effectiveness.
Authors - Jude Osakwe, Josephina Muntuumo, Daphine Gondo Abstract - Digital ecosystems have altered how organisations approach marketing decision-making, creating demand for advanced data management methods. This paper analyses 144 peer-reviewed articles published between 2013 and 2025, examining customer segmentation algorithms, real-time data processing systems, and integrated marketing analytics platforms. Organisations implementing these techniques report marketing return on investment improvements of 15 to 25 percent and conversion rate gains of 10 to 30 percent. A structured comparison of four technique categories, namely big data analytics, machine learning, AI and natural language processing, and predictive analytics, reveals distinct performance profiles and deployment trade-offs. Big data analytics delivers the broadest gains but demands the highest infrastructure investment, while predictive analytics offers a lower-cost entry point with shorter payback periods. Persistent challenges include data quality deficiencies, skills shortages, integration difficulties, privacy compliance obligations, and organisational resistance to change. The paper contributes a systematic framework and benchmarking evidence, and maps implementation constraints that explain the gap between reported performance benchmarks and operational outcomes
Authors - Chann Nyein Soe, SOKOUT Hamidullah Abstract - Developing countries determine the housing market based on how far from the city center. However, in Myanmar, submarkets are stronger than the Central Business District (CBD). The differences in the data analysis become results of the collected housing data for 45 days in Yangon, Mandalay, and Pyin Oo Lwin. Price per square foot is the basic unit to compare different properties. The manually collected dataset consists of 710 total listings, in which apartment in Yangon, landed houses, and vacant land in all three cities taken into consideration. Descriptive diagnostic methods, hedonic regressions, and segmentation were used. The result indicates that Myanmar housing prices are not determined by a smooth general rule, but rather distinct submarkets and different pricing behaviors exist. Townships are strongly substantial, whereas apartments show the strongest evidence of segmentation. Although CBD is statistically meaningful, the effect differs across property types. Meanwhile, housing markets exhibit significant differences for each property type. On the other hand, apartments are naturally based on the utility area and are more stable. Housing markets, price estimation models, and property valuation in Myanmar need to change from a city-level single average model to individual property and each township model, relying on median instead of means, because of the outliers in elite properties. Despite the scarcity of estate data in Myanmar, the research supports the Myanmar housing market insights for developing data analysis.
Authors - Merja Drake, Mariitta Rauhala Abstract - Artificial intelligence (AI) is increasingly embedded in knowledge work, reshaping how tasks are performed and how work is experienced. While prior research has emphasized efficiency and productivity gains, less is known about how everyday human–AI interaction influences employees’ well-being and sense of meaningful work. This study examines AI-enabled work through the dual lenses of hedonic enjoyment and eudaimonic well-being, focusing on how pleasure-oriented and growth-oriented experiences emerge in organizational contexts. Drawing on a mixed-methods design, the study combines a survey of 474 Finnish knowledge workers with 44 in-depth interviews conducted in spring 2024. The analysis explores how generative artificial intelligence use and development re-late to psychological resources such as self-efficacy, proactivity, autonomy, work engagement, and social participation. The findings show that generative artificial intelligence often functions as a hedonic facilitator by streamlining tasks and reducing effort. However, more sustained well-being is associated with eudaimonic experiences, particularly opportunities for learning, participation, and meaningful contribution to AI development. Importantly, the results highlight that access to generative AI tools alone is in-sufficient. Organizational practices such as transparent communication, inclusive development processes, and shared learning environments play a central role in shaping positive human–AI interaction. The study contributes to Human–Computer Interaction research by offering a human-centered perspective on generative AI as a sociotechnical system that can support both enjoyment and flourishing at work.
Authors - Vijak Sethaput, Supachate Innet Abstract - The rise of digital currencies—encompassing cryptocurrencies, stablecoins, tokenized deposits, and central bank digital currencies (CBDCs)—has outpaced frameworks for understanding their coexistence as financial infrastructure. This paper proposes the Digital Finance Infrastructure Framework (DFIF): a four-layer architecture—Settlement, Commercial, Retail, and Open— that maps five digital currency categories to complementary infrastructure tiers and explicitly links each tier to smart-systems enablers, sustainability dimensions, and security considerations. This paper makes four contributions: (1) a seven-dimensional comparative taxonomy of five digital currency categories; (2) the DFIF framework; (3) a four-dimensional interoperability model; and (4) a six-category systemic risk taxonomy with evidence-based smart-system mitigations. We situate the DFIF against four established frameworks (IMF Money Flower, BIS Singleness of Money, Carstens Digital Money Galaxy, Brunnermeier Digital Currency Areas) and validate it against deployment evidence, including BIS mBridge (reaches MVP stage and BIS exit), Project Agorá (seven central banks), MiCA full applicability, Nigeria's eNaira (
Authors - Usman G. Abdullahi, Kashifu I. Abdullahi Abstract - Weak governance of public-sector digital investments continues to undermine the effectiveness of digital transformation initiatives in many developing countries, resulting in inefficiencies, project failures, and erosion of public trust. While existing literature has largely focused on digital service delivery and user adoption, less attention has been given to upstream governance mechanisms that shape how digital investments are assessed and approved. This paper examines stakeholders' perceptions of the effectiveness of the IT project clearance process, implemented by Nigeria's National Information Technology Development Agency (NITDA) as a risk-based, stage-gated centralised digital investment governance mechanism. The study adopted a descriptive survey research design approach. The design was suitable for this study because the research focused on evaluating the perceived impact of the clearance framework on compliance across Ministries, Departments, and Agencies (MDAs), local content promotion, reduction in duplication and waste, standardisation and interoperability, transparency in IT projects, and, more importantly, trust in public investments. The study employed a quantitative research approach, which facilitated the objective measurement of respondents' views using numerical data and statistical analysis. The findings from this study indicate a very strong positive perception of the effectiveness of NITDA's IT Projects Clearance process among stakeholders. Using a 5-point Likert scale, all evaluated attributes recorded mean scores above 4.40, suggesting widespread agreement that the clearance framework contributes significantly to accountability, coordination, transparency, trust and value optimisation in government IT investments. More importantly, the study contributes to digital governance literature by reframing IT project assurance as a foundational element of ethical and trust-based governance systems, offering insights relevant to governments seeking to strengthen oversight of digital investments. The findings are also expected to provide useful insights for policymakers, government institutions, ICT regulators, researchers, and development practitioners seeking to improve digital governance frameworks and optimise public sector technology investments.
Authors - Waqas Ahmed, Muhammad Khalid, Adil Khan, Gulraiz Khan Abstract - Robust traffic sign recognition being important component in integrated Autonomous Driving Assistance Systems (ADAS), driving safety systems; suffers from many challenges including Occlusions and obstructions in road signs. Occlusions may occur due to natural or man made objects; weather conditions, lightening effects, vegetation, paint sprays, parked vehicles may result in traffic sign misclassifications adding more to endangerment of human life and infrastructure. This research contributed by addressing the occlusion problem by devising an occlusion-aware masked autoencoder Vit-OCC for reconstruction of obstructed traffic sign while utilizing German Traffic Sign Recognition Benchmark (GTSRB). Study encompassed four Vit models; a standard classifier (ViT-CLS), a region-aware Cutout-augmented model (ViT-Cutout), a model initialized via random-mask Masked Auto encoding (ViT-MAE), and a proposed occlusion-aware Masked Autoencoder (ViT-OCC). All the models are evaluated using variable and increasing occlusion levels, comprising of 9 different levels from 0% being lowest occlusion level to highly obstructed 80% threshold. Results indicate different patterns of degradation among the four models, although ViT-MAE achieved highest baseline accuracy of 87.23%, ViT-Cutout exhibited stable performance with the introduction of high occlusion levels. ViT-Cutout ViT-OCC exhibited sharp drop in performance under high occlusion thresholds. This study contributed in a systematic exploration of Vision Transformer robustness across variants under controlled and increasing occlusion thresholds while concluding unique patterns in degradation and stability.
Authors - Moncef Zarrouk, Abdelmajid Bessate, Faissal El Bouanani Abstract - Edge artificial intelligence systems require inference mechanisms that jointly account for predictive accuracy and resource consumption. Compact edge models provide low-cost and low-latency inference, but they may be less reliable on difficult inputs, whereas larger cloud models are more accurate but expensive to invoke for every sample. In this paper, we propose a constrained optimization framework for calibration-aware adaptive edge–cloud inference. The edge model first produces a local prediction and a confidence score. This confidence is post-calibrated by scalar temperature scaling, and a threshold gate then either accepts the local prediction or defers the input to a cloud model. The threshold is selected by solving an empirical constrained optimization problem that maximizes accuracy under an operator-specified of- load budget. We show that temperature scaling preserves the edge-model decision, that the empirical offload rate is monotone in the threshold, and that the finite empirical threshold-selection problem can be solved exactly by enumerating the gate partitions induced by the observed confidence scores, correctly handling the strict deferral boundary. Experiments on Fashion-MNIST show that constrained deferral recovers most of the edge–cloud accuracy gap while offloading only a controlled fraction of inputs. Moreover, an additive-noise stress test shows that calibration substantially reduces confidence miscalibration when the edge model be- comes unreliable, confirming that calibration mainly contributes reliabil- ity, while selective deferral drives the accuracy gain.
Authors - Guillermo Francia III, Eman El-Sheikh, Md Abdur Rahman Abstract - Global Navigation Satellite Systems (GNSS) are essential components of modern unmanned aerial vehicle (UAV) operations, providing positioning, navigation, and timing (PNT) services that enable autonomous flight, waypoint navigation, and coordinated mission execution. However, the increasing reliance of UAVs on radio frequency (RF) communications and GNSS signals has exposed these systems to a growing range of cybersecurity threats, including spoofing, jamming, malware infections, distributed denial-of-service (DDoS) attacks, and anomalous network behaviors. This paper investigates the application of deep learning techniques for enhancing RF-based security in GNSS-enabled drone communication networks. A comprehensive drone communication dataset containing 52,585 records and four traffic classes—normal traffic, malware infections, DDoS attacks, and anomalous behavior—was utilized to develop and evaluate a Deep Learning Radio Frequency Security (DL-RFS) model. To address severe class imbalance, a balanced TensorFlow data pipeline incorporating stratified sampling, class-wise dataset generation, and equal-probability sampling was designed. The proposed neural network architecture employs fully connected layers with Rectified Linear Unit (ReLU) activation functions and a softmax output layer optimized using the Adam optimizer. Experimental evaluation conducted on an NVIDIA A100 GPU demonstrated exceptional classification performance, achieving an AUC of 0.999, accuracy of 99.3%, precision of 99.9%, recall of 99.9%, and an F1-score of 1.000. Comparative analysis shows that the proposed DL-RFS model outperforms several state-of-the-art machine learning and deep learning approaches for drone network intrusion detection. The results demonstrate the effectiveness of balanced deep learning pipelines for RF signal security analysis and establish a foundation for future research in GNSS security, RF fingerprinting, and AI-driven cyber defense mechanisms for autonomous systems.
Authors - Saltanat Adilzhanova, Gulshat Amirkanova, Bauyrzhan Amirkhanov, Dana Sybanova, Anas Salem Abstract - The increasing adoption of large language models (LLMs) by adversarial actors has introduced a critical threat to web application security: AI-mutated malicious payloads - injection attacks automatically rewritten by LLMs to preserve malicious functionality while evading signaturebased detection. Existing intrusion detection approaches, including classical machine learning classifiers and deep learning architectures trained on static historical corpora, do not address this threat class and degrade severely when confronted with LLM-obfuscated variants of known attacks. This paper presents a three-component framework for detecting and explaining AImutated malicious API payloads. First, a novel three-class labelled dataset is constructed by applying a controlled GPT-4o-mini mutation pipeline, spanning five structurally distinct obfuscation strategies, to payloads drawn from three established attack corpora, with validation against a sandboxed DVWA instance. Second, a CodeBERT encoder is fine-tuned for three-class classification distinguishing benign traffic, classically malicious payloads, and AI-mutated payloads, achieving a macro-F1 of 0.9472 and an AUC-ROC of 0.9990 on the held-out test set, with a class-specific F1 of 0.8627 on AI-mutated samples - an improvement of 27.18 points over the strongest baseline. Third, a dual-layer explainability module combining attention visualisation and SHAP-based token attribution is evaluated through a faithfulness deletion test, confirming that both methods identify decision-relevant tokens and revealing distinct detection strategies for classical versus AI-mutated payloads. A controlled ablation study demonstrates that AI-mutated training data is a necessary condition for detecting this class, with class-specific F1 collapsing to zero when such data is withheld. The dataset and model are released publicly to support reproducible research.
Authors - Noah Holmdin, Martin Gilje Jaatun Abstract - The increased reliance on IT has resulted in a need for decision support for IT system usage scenarios. This is often done through risk assessment, where events are evaluated based on their likelihood of consequences and the severity of consequences. However, this may be difficult in scenarios when there is limited, uncertain, conflicting, or changing information about the IT system’s behaviour. A complementary approach for this is evaluating the system based on a Decision Maker’s (DM) trust in it. This paper introduces a subjective trust assessment framework for IT usage decisions.
Authors - Gulshat Amirkhanova, Alikhan Amirkhanov, Gulnur Tyulepberdinova, Bauyrzhan Amirkhanov, Yenlik Faruzkyzy Abstract - Digital twins for manufacturing usually rely on a Manufacturing Execution System (MES) that records what each operation does and when. Many small and medium-sized enterprises (SMEs) lack such systems yet increasingly meter the electricity of individual machines. This paper asks whether a plant's process can be reconstructed, simulated and optimised from electrical metering alone, and formalises the answer as Energy-Driven Process Reconstruction (EDPR). The subject is a commercial bakery in Kazakhstan whose fifteen machines are metered and integrated through OpenEgiz, a digital-twin platform built on the open-source OpenTwins framework; about 65 million readings span 198 days. EDPR detects machine states from active power, abstracts them into events, and links events into per-batch chains by a batch-anchored lead-lag operator with one-to-one assignment in O(N log N) time. With no MES ground truth available, EDPR is scored on a labelled synthetic benchmark, where it reaches an eventdetection F1 of 0.97 and, against three baselines, is the only method that combines competitive chain accuracy with a valid one-to-one batch partition. On the real plant, conformance checking raises model precision from 0.22 for a naive day-case model to 1.00 for the EDPR reconstruction, and a discrete-event twin parameterised only by the energy-derived lead times reproduces the observed throughput of about 21 batches per day. Using the twin, three demand-side measures are estimated to give a potential electricity-cost reduction of 20.5 per cent at constant output. The pipeline uses only open-source software and permachine power.
Authors - Tlangelani Promise Mlambo, Tranos Zuva, Andrew Brown, Ramadile Moletsane Abstract - South African organizations are experiencing rapid digital transformation as digital technologies increasingly shape service delivery, data management, and interactions between the state and citizens. In this time where machines such as Generative Artificial Intelligence (GenAI) can generate answers from the user prompt, there’s a need to control their use in order not to harm others. This study examines digital governance in the generative AI era, focusing on how privacy and trust are managed within organizations in South Africa. The study adapts a qualitative research approach utilizing the traditional literature review and secondary data analysis. There’s a disconnect between the initiated policies and the implementation or regulation of these policies, therefore, to develop a comprehensive digital governance framework for South African organizations to help minimize any harm that can be caused by use of Generative Artificial Intelligence. The research shows that in the generative AI era, is necessary for digital governance to move from only technology-centric strategies to a comprehensive framework that will combine institutional, ethical and operational aspects. The study contributes theoretically and practically to a deeper understanding of governance challenges and opportunities in South Africa. The study is limited to South African organizations and on secondary data from existing literature. Future work can extend the scope of the study beyond South Africa and conduct empirical and longitudinal studies to gain more insights on digital governance in the generative AI era.
Authors - Gulshat Amirkhanova, Alikhan Amirkhanov Abstract - Wearable and Internet-of-Medical-Things (IoMT) devices now stream cardiac signals continuously, yet turning that flow into an early warning still needs a model that says when an adverse event is likely, not just whether one will occur. We treat that question as a time-toevent problem and benchmark five survival models on the UCI Heart Failure Clinical Records cohort (299 patients, 96 deaths, follow-up 4– 285 days): Cox proportional hazards, penalised Cox, random survival forest (RSF), gradient-boosted survival analysis, and the deep survival network DeepSurv. Models are compared under 5×5 repeated stratified cross-validation with Harrell’s and IPCW concordance, the integrated Brier score, and time-dependent AUC. The two tree-ensemble survival models lead: gradient boosting reaches a C-index of 0.731 and RSF 0.725, both ahead of Cox (0.709) and well ahead of DeepSurv (0.645), which also shows the worst calibration. On a cohort of this size the deep model does not pay its way. Permutation and SHAP analysis of the RSF point to serum creatinine, ejection fraction and age as the dominant risk drivers, which agrees with established cardiology. We frame the survival learner as the analytics stage of an IoMT cardiovascular-monitoring pipeline and release code, data split and seeds for full reproducibility.
Authors - Zubaria Inayat, Hanin Esawi, Maya Daneva, Marten van Sinderen, Giancarlo Guizzardi, Luiz Bonino da Silva Santos Abstract - Explainable artificial intelligence (XAI) is becoming an important component of healthcare systems, supporting transparent and trustworthy AI-assisted decision-making. However, existing explainable AI approaches are mainly designed around healthcare professionals, while patient needs and expectations regarding AI-generated explanations remain insufficiently explored. This study investigates how patients perceive the quality of explanations provided by XAI healthcare systems and identifies the challenges that influence their understanding, trust, and engagement. A mixed-method research approach was adopted, combining (i) a rapid literature review, (ii) an online survey with 33 participants, and (iii) expert consultation for validation. The literature review identified nine quality dimensions for patient-oriented XAI explanations. The survey findings revealed eight challenges experienced by patients when interacting with explainable AI systems. Based on the synthesis of these findings, a patient-centred evaluation matrix was developed, linking explanation quality dimensions with patient-related challenges. The proposed matrix was validated through expert feedback. The results highlight the importance of moving beyond developer and clinician-centric XAI design towards patient-centred explainable healthcare systems. This work contributes to trustworthy artificial intelligence in healthcare by providing guidance for evaluating explanation quality, improving usability, supporting patient trust, and enabling informed shared decision-making.
Authors - Ahmed Abdelmoula, Balint Molnar Abstract - Honey fraud, including adulteration, origin mislabeling, and unsuitable thermal handling, remains difficult to control because analytical verification and digital traceability are rarely integrated. Laboratory methods can assess honey quality and authenticity, while blockchain systems can protect traceability records; however, ledger immutability does not ensure that recorded evidence is scientifically valid. This paper introduces a preliminary framework for converting honey-quality metrics into states suitable for permissioned blockchain environments. The architecture combines IoT sensing, edge-level validation, metric computation, offchain storage, cryptographic referencing, and rule-based smart-contract logic. The main contribution is a metric-to-state mapping mechanism that encodes environmental, biochemical, and process-related evidence as blockchain events, hashes, quality indicators, and certification states. A simulation-based prototype evaluates a 24-hour hive-monitoring scenario using synthetic temperature and humidity readings collected every 15 minutes. Of 96 generated readings, 93 passed validation and were aggregated into 24 hourly quality-state transactions, reducing potential ledger entries by 75% while preserving SHA–256 hash-chain integrity. The results provide initial feasibility evidence for the proposed sensor-to-ledger pipeline, while remaining limited to a proof-of-concept setting rather than a full industrial deployment or permissioned blockchain benchmark. Future work will incorporate real hive data, laboratory measurements, and complete permissioned blockchain implementation.
Authors - More Ickson Manda Abstract - The rapid advancement of digital technologies and artificial intelligence has created significant opportunities and challenges for developing countries such as South Africa. To harness these opportunities and mitigate associated risks, South Africa must adopt strategic policy and investment interventions that promote innovation, strengthen digital capabilities, and support inclusive and sustainable socio-economic growth. Technological developments such as Artificial Intelligence, robotics, internet of things and cloud-based technologies have disrupted every sector. Concerns around ethics, sovereignty, security, privacy, skills, affordability, governance and infrastructure have hindered developing countries from fully leveraging the benefits of AI. The purpose of this paper is therefore to identify key pillars for building an inclusive digital society in South Africa in the age of Artificial Intelligence where citizens can fully access and benefit from using digital services. The study found that developing an inclusive digital society, digital leadership, digital infrastructure development, embracing new and emerging technologies, digital policy and governance and digital resilience are key pillars of an inclusive digital society.
Authors - AA Adebomehin, FJ Ibrahim, AS Dahiru, TK Akinduyite, TO Obinna-Esiowu, I Ofodile, OA Odeyemi, T Salman Abstract - This paper presents novel AI-enabled Kalman filter techniques that enhance positioning accuracy in inertial navigation systems (INS). Essentially, accurate positioning remains a core challenge in multi-sensor INS; since performance of traditional Kalman filtering degrades in nonlinear environments. Our method significantly improves the precision of INS by integrating adaptive AI-based machine-learning Kalman filters with classical estimation theory. The approach focuses on multi-sensor fusion, adaptive noise modeling, and robust improvements for INS applications. Simulation results demonstrate the effectiveness of the proposed techniques, especially airborne INS. This is significant in that achievement of precision without sacrificing overall accuracy is essential to sensor data in view of factors like sensor thermal noise, lower inference quantization, and tolerance which could affect real–world performances. Additionally, in a multi–sensor fusion setting, effectiveness of INS hinges on crucial and dependable filter systems for real data integration, noise reduction, reliable predictive analysis, and real-time processing. Consequently, this research developed AI-based models and utilized them for all filter GPS positioning data to update the INS states covering INS position output, INS internal states (position, velocity and heading), and finally for estimation of bias on the accelerometer sensor. It is believed that the approach has possible applications in diverse fields like defense & security with favorable implications for autonomous systems; as well as surveys & disaster management. Key highlights of the improved multi-sensor INS algorithm models are presented in this paper.
Authors - Caitlin Loh, Susmitha Vekkot, Pancham Shukla Abstract - Deepfakes generated using modern machine learning techniques pose growing risks to digital trust by enabling realistic manipulation of both audio and video content. Many existing detection approaches rely on a single modality, limiting robustness when confronted with increasingly sophisticated forgeries. This paper evaluates a multimodal deepfake detection framework that integrates audio and visual information using deep learning. The proposed system combines stateof-the-art audio and visual encoders within a modular architecture and conducts a systematic comparison of three fusion strategies: early fusion, late fusion, and cross-attention. Experiments are conducted on the PolyGlotFake dataset, a multilingual benchmark containing synthetic and authentic audio–visual media. Results show that multimodal approaches substantially outperform unimodal baselines, with late fusion achieving an AUROC of 0.955 and cross-attention models reaching accuracies of up to 0.996. These findings provide a controlled comparison of fusion strategies and demonstrate that multimodal fusion significantly improves detection performance and highlights its potential for building more robust deepfake detection systems.
Authors - Muhammad Sohaib Ayub, Sikandar Bakhat, Mian Muhammad Awais, Umair ul Hassan Abstract - Smallholder farmers in Pakistan make irrigation, fertiliser, and crop-selection decisions largely on inherited practice rather than field-specific evidence, even though the satellite data needed to inform those decisions is now free and globally available. This paper presents the Agriculture Portal, a role-based web platform that turns raw Sentinel2 imagery into decision-ready guidance for farmers and the extension officers who support them. The system computes NDVI, EVI, NDMI, NDWI, and a radar vegetation proxy from Sentinel-2 surface reflectance through Google Earth Engine, complements them with ground-deployed IoT sensors and an AI land classifier, then fuses the signal through an ensemble of the Analytic Hierarchy Process, fuzzy TOPSIS, and VIKOR into two scores: a Crop Health Index describing vegetation vigour and a Soil Suitability Index describing cultivation potential. A fixed agronomic normalisation scheme prevents the degenerate behaviour that field-relative normalisation produces over spatially uniform plots. Beyond the satellite layer, extension officers can author standardised crop advisories and farmers can log field activities against them, with the portal automatically generating a compliance report that surfaces the gap between guidance and practice. Field trials across cultivated and forested sites in Pakistan confirm that the dual-score design correctly separates vegetation vigour from cultivation suitability and that the compliance tracker correctly reconciles recommended and logged activity.
Authors: Johnson Masinde, Mugambi Frankline, Daniel Wambiri Abstract: Over time, the field of librarianship has had to adapt due to factors such as technological advancements, which have significantly impacted the library landscape. This evolution has prompted librarians to reconsider their roles and responsibilities, as well as the future and long-term sustainability of conventional library practices and services. The recent past has seen a significant intensification of the debate surrounding the role and future of academic libraries, driven by the rapid advancements in technology. Traditionally, academic libraries have served as core pillars of university education, promoting learning, teaching, and research. However, institutions of higher learning are transitioning from traditional in-person teaching methods to online methods of instruction. Furthermore, an increasing number of students are opting for online modes of instruction instead of the traditional in-person classroom. This study was motivated by the need to explore the changing dynamics of academic libraries, influenced by the opportunities and challenges present in contemporary society. The research study conducted a foresight analysis of the potential characteristics at the convergence of academic libraries and the fourth industrial revolution (4IR) to uncover the significant trends and uncertainties that might not be immediately visible. Furthermore, it utilized both qualitative and quantitative research methods, providing a thorough analysis of the potential future of the academic library landscape. The Shawaz scenario planning process was utilized to assess the key drivers of change in academic libraries over time enabling the study to paint a picture of the plausible features with the study structured in three distinct phases: (i) the literature review to uncover the mega trends and uncertainties, (ii) the delphi survey involving 33 participants (iii) and the formulation of the scenario. Study findings from the first phase show a complex network of relationships among the major trends and uncertainties influencing the academic library landscape in both the short term and long-term future. In addition, the findings indicate that economic, technological, and political factors significantly influence the academic library landscape. Academic libraries are evolving into a more dynamic landscape adopting new roles and responsibilities such as digital archiving, data analysis support, and research data management services. The findings show that while earlier studies suggested that technological factors shape the academic library landscape, it is evident that economic and political factors also play significant roles in influencing this environment.
