Publications

@article{abdelmagid_integrating_2025,

title = {Integrating Interactive Metaverse Environments and Generative Artificial Intelligence to Promote the Green Digital Economy and e-Entrepreneurship in Higher Education},

author = {A. S. Abdelmagid and N. M. Jabli and A. Y. Al-Mohaya and A. A. Teleb},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105008981835&doi=10.3390%2fsu17125594&partnerID=40&md5=0eaea40f26536c05c29c7b3f0d42d37d},

doi = {10.3390/su17125594},

issn = {20711050 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Sustainability (Switzerland)},

volume = {17},

number = {12},

abstract = {The rapid evolution of the Fourth Industrial Revolution has significantly transformed educational practices, necessitating the integration of advanced technologies into higher education to address contemporary sustainability challenges. This study explores the integration of interactive metaverse environments and generative artificial intelligence (GAI) in promoting the green digital economy and developing e-entrepreneurship skills among graduate students. Grounded in a quasi-experimental design, the research was conducted with a sample of 25 postgraduate students enrolled in the “Computers in Education” course at King Khalid University. A 3D immersive learning environment (FrameVR) was combined with GAI platforms (ChatGPT version 4.0, Elai.io version 2.5, Tome version 1.3) to create an innovative educational experience. Data were collected using validated instruments, including the Green Digital Economy Scale, the e-Entrepreneurship Scale, and a digital product evaluation rubric. The findings revealed statistically significant improvements in students’ awareness of green digital concepts, entrepreneurial competencies, and their ability to produce sustainable digital products. The study highlights the potential of immersive virtual learning environments and AI-driven content creation tools in enhancing digital literacy and sustainability-oriented innovation. It also underscores the urgent need to update educational strategies and curricula to prepare future professionals capable of navigating and shaping green digital economies. This research provides a practical and replicable model for universities seeking to embed sustainability through emerging technologies, supporting broader goals such as SDG 4 (Quality Education) and SDG 9 (Industry, Innovation, and Infrastructure). © 2025 by the authors.},

keywords = {Artificial intelligence, digitization, e-entrepreneurship, entrepreneur, generative artificial intelligence, green digital economy, green economy, higher education, Learning, Metaverse, Sustainable development},

pubstate = {published},

tppubtype = {article}

}

@article{wu_geospatial_2024,

title = {Geospatial Big Data: Survey and Challenges},

author = {J. Wu and W. Gan and H. -C. Chao and P. S. Yu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200804056&doi=10.1109%2fJSTARS.2024.3438376&partnerID=40&md5=53ee1c9695b3f2e78d6b565ed47f7585},

doi = {10.1109/JSTARS.2024.3438376},

issn = {19391404 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},

volume = {17},

pages = {17007–17020},

abstract = {In recent years, geospatial big data (GBD) has obtained attention across various disciplines, categorized into big Earth observation data and big human behavior data. Identifying geospatial patterns from GBD has been a vital research focus in the fields of urban management and environmental sustainability. This article reviews the evolution of GBD mining and its integration with advanced artificial intelligence techniques. GBD consists of data generated by satellites, sensors, mobile devices, and geographical information systems, and we categorize geospatial data based on different perspectives. We outline the process of GBD mining and demonstrate how it can be incorporated into a unified framework. In addition, we explore new technologies, such as large language models, the metaverse, and knowledge graphs, and how they could make GBD even more useful. We also share examples of GBD helping with city management and protecting the environment. Finally, we discuss the real challenges that come up when working with GBD, such as issues with data retrieval and security. Our goal is to give readers a clear view of where GBD mining stands today and where it might go next. © 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.},

keywords = {Artificial intelligence, artificial intelligence (AI), Behavioral Research, Big Data, Data challenges, Data Mining, Data surveys, Data visualization, Earth observation data, Environmental management, environmental protection, Geo-spatial, Geo-spatial analysis, Geo-spatial data, Geospatial big data, geospatial big data (GBD), geospatial data, GIS, Green products, Human behaviors, Knowledge graph, Knowledge graphs, satellite, sensor, spatial data, Sustainable development, urban planning},

pubstate = {published},

tppubtype = {article}

}

@article{otoum_machine_2024,

title = {Machine Learning in Metaverse Security: Current Solutions and Future Challenges},

author = {Y. Otoum and N. Gottimukkala and N. Kumar and A. Nayak},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193466017&doi=10.1145%2f3654663&partnerID=40&md5=b35485c5f2e943ec105ea11a80712cbe},

doi = {10.1145/3654663},

issn = {03600300 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {ACM Computing Surveys},

volume = {56},

number = {8},

abstract = {The Metaverse, positioned as the next frontier of the Internet, has the ambition to forge a virtual shared realm characterized by immersion, hyper-spatiotemporal dynamics, and self-sustainability. Recent technological strides in AI, Extended Reality, 6G, and blockchain propel the Metaverse closer to realization, gradually transforming it from science fiction into an imminent reality. Nevertheless, the extensive deployment of the Metaverse faces substantial obstacles, primarily stemming from its potential to infringe on privacy and be susceptible to security breaches, whether inherent in its underlying technologies or arising from the evolving digital landscape. Metaverse security provisioning is poised to confront various foundational challenges owing to its distinctive attributes, encompassing immersive realism, hyper-spatiotemporally, sustainability, and heterogeneity. This article undertakes a comprehensive study of the security and privacy challenges facing the Metaverse, leveraging machine learning models for this purpose. In particular, our focus centers on an innovative distributed Metaverse architecture characterized by interactions across 3D worlds. Subsequently, we conduct a thorough review of the existing cutting-edge measures designed for Metaverse systems while also delving into the discourse surrounding security and privacy threats. As we contemplate the future of Metaverse systems, we outline directions for open research pursuits in this evolving landscape. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.},

keywords = {'current, Block-chain, Blockchain, digital twin, E-Learning, Extended reality, Future challenges, Generative AI, machine learning, Machine-learning, Metaverse Security, Metaverses, Security and privacy, Spatio-temporal dynamics, Sustainable development},

pubstate = {published},

tppubtype = {article}

}

@article{marquez_perspective_2023,

title = {A perspective on the synergistic potential of artificial intelligence and product-based learning strategies in biobased materials education},

author = {R. Marquez and N. Barrios and R. E. Vera and M. E. Mendez and L. Tolosa and F. Zambrano and Y. Li},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162078243&doi=10.1016%2fj.ece.2023.05.005&partnerID=40&md5=76cd274af795123f1e31e345dd36eded},

doi = {10.1016/j.ece.2023.05.005},

issn = {17497728 (ISSN)},

year  = {2023},

date = {2023-01-01},

journal = {Education for Chemical Engineers},

volume = {44},

pages = {164–180},

abstract = {The integration of product-based learning strategies in Materials in Chemical Engineering education is crucial for students to gain the skills and competencies required to thrive in the emerging circular bioeconomy. Traditional materials engineering education has often relied on a transmission teaching approach, in which students are expected to passively receive information from instructors. However, this approach has shown to be inadequate under the current circumstances, in which information is readily available and innovative tools such as artificial intelligence and virtual reality environments are becoming widespread (e.g., metaverse). Instead, we consider that a critical goal of education should be to develop aptitudes and abilities that enable students to generate solutions and products that address societal demands. In this work, we propose innovative strategies, such as product-based learning methods and GPT (Generative Pre-trained Transformer) artificial intelligence text generation models, to modify the focus of a Materials in Chemical Engineering course from non-sustainable materials to sustainable ones, aiming to address the critical challenges of our society. This approach aims to achieve two objectives: first to enable students to actively engage with raw materials and solve real-world challenges, and second, to foster creativity and entrepreneurship skills by providing them with the necessary tools to conduct brainstorming sessions and develop procedures following scientific methods. The incorporation of circular bioeconomy concepts, such as renewable resources, waste reduction, and resource efficiency into the curriculum provides a framework for students to understand the environmental, social, and economic implications in Chemical Engineering. It also allows them to make informed decisions within the circular bioeconomy framework, benefiting society by promoting the development and adoption of sustainable technologies and practices. © 2023 Institution of Chemical Engineers},

keywords = {Artificial intelligence, Bio-based, Bio-based materials, Biobased, ChatGPT, Chemical engineering, Chemical engineering education, Education computing, Engineering education, Formulation, Generative AI, Learning strategy, Learning systems, Material engineering, Materials, Students, Sustainable development, Teaching approaches, Traditional materials, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}