Smart Curriculum Mapping: A Blockchain Approach to Transparent and Customizable Educational Pathways
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Background. Traditional curriculum mapping often faces challenges of transparency, flexibility, and personalization. Existing digital systems tend to be centralized, limiting stakeholder trust and adaptability in designing individualized learning trajectories. Blockchain technology offers an innovative solution to ensure transparency, immutability, and decentralized control over educational data, enabling both institutions and learners to co-create customizable educational pathways.
Purpose. This study aimed to investigate the potential of blockchain-based smart curriculum mapping in fostering transparent governance of curricula and supporting adaptive learning designs. Specifically, it examined how blockchain can integrate institutional requirements with learner-driven customization while ensuring accountability and security.
Method. Using a mixed-method design, the research engaged 210 university students and 45 lecturers across three higher education institutions. Data were collected through surveys, interviews, and prototype testing of a blockchain-enabled curriculum mapping platform. The findings were analyzed using statistical methods and thematic coding to evaluate user perceptions, system usability, and pedagogical impact.
Results. The findings indicate that blockchain-based curriculum mapping enhances trust among stakeholders by ensuring transparent records of course progress and requirements. Students reported increased autonomy in designing personalized pathways, while lecturers emphasized the benefits of immutable documentation for accreditation and evaluation. However, challenges such as technical literacy and system scalability were also identified.
Conclusion. This study highlights the transformative role of blockchain in curriculum management. By integrating transparency, security, and learner-centered customization, smart curriculum mapping offers a scalable model for future educational governance. The findings contribute to both educational technology innovation and institutional policy-making, offering pathways toward more accountable and personalized higher education systems.
Abdualwhab, M. (2024). Leveraging IoT Data for Real-Time Business Decision-Making. Ismsit 2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies Proceedings, Query date: 2025-08-22 02:44:20. https://doi.org/10.1109/ISMSIT63511.2024.10757297
Ali, S. A. (2023). Leveraging Machine Learning for Real-time Anomaly Detection and Self-Repair in IoT Devices. 2023 International Conference on Communication Security and Artificial Intelligence Iccsai 2023, Query date: 2025-08-22 02:44:20, 982–986. https://doi.org/10.1109/ICCSAI59793.2023.10421539
Arriba-Pérez, F. de. (2025). Leveraging large language models through natural language processing to provide interpretable machine learning predictions of mental deterioration in real time. Arabian Journal for Science and Engineering, 50(15), 11577–11591. https://doi.org/10.1007/s13369-024-09508-2
Behki, P. (2024). Leveraging IoT for Real-Time Heart Rate Forecasting and Anomaly Detection. 2nd International Conference on Signal Processing Communication Power and Embedded Systems Scopes 2024, Query date: 2025-08-22 02:44:20. https://doi.org/10.1109/SCOPES64467.2024.10991097
Chavan, P. (2025). Leveraging real-time data: A location-based ambulance booking and tracking system with geofencing. Journal of Integrated Science and Technology, 13(2). https://doi.org/10.62110/sciencein.jist.2025.v13.1039
Deng, H. W. (2025). Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor. International Journal of Transportation Science and Technology, 17(Query date: 2025-08-22 02:44:20), 131–147. https://doi.org/10.1016/j.ijtst.2024.03.004
Guzman, J. S. (2023). Leveraging Real Time Operational Data to Reduce Greenhouse Gas Emissions. Society of Petroleum Engineers Adipec Adip 2023, Query date: 2025-08-22 02:44:20. https://doi.org/10.2118/216258-MS
Hornik, J. (2025). Leveraging real-time digital twins for smart livestreaming platforms to enhance consumers’ experience. Journal of Supercomputing, 81(8). https://doi.org/10.1007/s11227-025-07386-5
Ismail, R. (2024). Leveraging Internet of Things for Upstream and Downstream Real-Time Monitoring in Flood-Prone Areas: A Case Study in Brunei Darussalam. 2024 6th IEEE Symposium on Computers and Informatics Isci 2024, Query date: 2025-08-22 02:44:20, 311–316. https://doi.org/10.1109/ISCI62787.2024.10668067
Katumba, A. (2024). Leveraging edge computing and deep learning for the real-time identification of bean plant pathologies. Smart Agricultural Technology, 9(Query date: 2025-08-22 02:44:20). https://doi.org/10.1016/j.atech.2024.100627
Kumar, K. (2025). Leveraging Machine Learning for Real-Time Crowd Control and Safety at Kumbh Mela. 2025 3rd International Conference on Communication Security and Artificial Intelligence Iccsai 2025, Query date: 2025-08-22 02:44:20, 1416–1422. https://doi.org/10.1109/ICCSAI64074.2025.11064144
Larriva-Novo, X. (2023). Leveraging Explainable Artificial Intelligence in Real-Time Cyberattack Identification: Intrusion Detection System Approach. Applied Sciences Switzerland, 13(15). https://doi.org/10.3390/app13158587
Mishra, A. (2023). Leveraging Machine Learning for Constructing Robust Automated Real-Time Data Analysis Systems. Proceedings 2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario Icpsitiags 2023, Query date: 2025-08-22 02:44:20, 354–360. https://doi.org/10.1109/ICPSITIAGS59213.2023.10527679
Mittal, S. (2025). Leveraging Neuromarketing Technologies to Enhance Agile Marketing Strategies: A Study on Consumer Behavior Insights and Real-Time Adaptation. Digital Transformation Initiatives for Agile Marketing, Query date: 2025-08-22 02:44:20, 275–302. https://doi.org/10.4018/979-8-3693-4466-8.ch011
Muthukumaran, D. (2024). Leveraging IoT for Real-Time Air Quality Sensing and Optimization in Vehicle Interiors using Gradient Boosting Algorithm. Icepe 2024 6th International Conference on Energy Power and Environment Towards Indigenous Energy Utilization, Query date: 2025-08-22 02:44:20. https://doi.org/10.1109/ICEPE63236.2024.10668881
Nalinipriya, G. (2025). Leveraging Double-Valued Neutrosophic Set for Real-Time Chronic Kidney Disease Detection and Classification. International Journal of Neutrosophic Science, 25(1), 279–290. https://doi.org/10.54216/IJNS.250125
Narang, S. (2025). Leveraging IoT for Environmental Monitoring: Real-Time Data Collection and Analysis for Sustainable Development. 2025 3rd International Conference on Communication Security and Artificial Intelligence Iccsai 2025, Query date: 2025-08-22 02:44:20, 532–536. https://doi.org/10.1109/ICCSAI64074.2025.11064650
Navaneethan, S. (2024). Leveraging NVIDIA Clara for Real-Time Cardiac Image Segmentation and Diagnosis. Proceedings of the 2024 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2024, Query date: 2025-08-22 02:44:20. https://doi.org/10.1109/ICSES63760.2024.10910358
Park, H. J. (2024). Leveraging Non-Causal Knowledge via Cross-Network Knowledge Distillation for Real-Time Speech Enhancement. IEEE Signal Processing Letters, 31(Query date: 2025-08-22 02:44:20), 1129–1133. https://doi.org/10.1109/LSP.2024.3388956
Pourmoradnasseri, M. (2023). Leveraging IoT data stream for near-real-time calibration of city-scale microscopic traffic simulation. Iet Smart Cities, 5(4), 269–290. https://doi.org/10.1049/smc2.12071
Rethisha, R. (2025). Leveraging Machine Learning Techniques of Real Time Detection of UPI Fraud. Proceedings of the 7th International Conference on Intelligent Sustainable Systems Iciss 2025, Query date: 2025-08-22 02:44:20, 1506–1510. https://doi.org/10.1109/ICISS63372.2025.11076419
Rhouma, R. (2024). Leveraging mobile NER for real-time capture of symptoms, diagnoses, and treatments from clinical dialogues. Informatics in Medicine Unlocked, 48(Query date: 2025-08-22 02:44:20). https://doi.org/10.1016/j.imu.2024.101519
Satyanarayana, K. (2024). Leveraging IoT and AI Technologies for Real-Time Remote Patient Monitoring Innovations in Healthcare Delivery and Outcomes. Proceedings of the 2024 International Conference on Artificial Intelligence and Emerging Technology Global AI Summit 2024, Query date: 2025-08-22 02:44:20, 513–518. https://doi.org/10.1109/GlobalAISummit62156.2024.10947800
Shumba, A. T. (2022). Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications. Sensors, 22(19). https://doi.org/10.3390/s22197675
Singh, M. (2025). Leveraging machine learning for real-time loyalty program optimization. Revolutionizing Hospitality Management Systems with AI VR and Machine Learning, Query date: 2025-08-22 02:44:20, 263–292. https://doi.org/10.4018/979-8-3693-8769-6.ch010
Singh, S. (2024). Leveraging FPGA-based System-on-Chip for Real-time Sensor Data Hosting in IoT. Iemecon 2024 12th International Conference on Internet of Everything Microwave Embedded Communication and Networks, Query date: 2025-08-22 02:44:20. https://doi.org/10.1109/IEMECON62401.2024.10846196
Swinnerton, K. (2025). Leveraging near-real-time patient and population data to incorporate fluctuating risk of severe COVID-19: Development and prospective validation of a personalised risk prediction tool. Eclinicalmedicine, 81(Query date: 2025-08-22 02:44:20). https://doi.org/10.1016/j.eclinm.2025.103114
Syed, M. J. (2023). Leveraging explainable Artificial Intelligence for real-time detection of tidal blade damage. Proceedings of the European Wave and Tidal Energy Conference, Query date: 2025-08-22 02:44:20. https://doi.org/10.36688/ewtec-2023-617
Yang, F. (2025). Leveraging Mobile Interaction Technologies for Real-Time Decision Making in Enterprise Management Systems. International Journal of Interactive Mobile Technologies, 19(2), 65–78. https://doi.org/10.3991/ijim.v19i02.53743
Zhao, R. (2024). Leveraging Monte Carlo Dropout for Uncertainty Quantification in Real-Time Object Detection of Autonomous Vehicles. IEEE Access, 12(Query date: 2025-08-22 02:44:20), 33384–33399. https://doi.org/10.1109/ACCESS.2024.3355199
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