National College of Business Administration and Economics

In rapidly growing cities, the move toward Autonomous Electric Vehicles (AEVs) is challenging the current Energy Management Systems (EMS). The goal in smart cities is to reduce emissions and improve efficiency by optimizing vehicular energy; however, it remains challenging to address real-time decisions, complex AI, and extensive computing requirements for this task. Although AI and optimization are regularly used, they cannot be trusted in safety-related situations due to issues with complexity, scalability, and lack of clarity in their actions. To achieve transparent, smart energy systems in future transportation, it is crucial to address these issues. This research proposes an Explainable Quantum AI (XQAI) model that combines the computational capabilities of Quantum Machine Learning (QML) with the interpretability of Explainable AI (XAI). With QML, dealing with complex vehicular data is more efficient, and the model uses Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to ensure transparency and interpretability in the model’s decision-making process. This proposed model is developed using data from real cities, encompassing a wide range of features, to predict vehicular energy consumption across various trip types accurately and to provide insight into the reasons behind these predictions. According to simulation results, the proposed XQAI model is effective, as the Hybrid Classical–Quantum Regressor shows superior prediction performance with an R2 score of 0.8439. Furthermore, using LIME revealed a confidence score of 0.95, further establishing its credibility, interpretability, and reliability. The results demonstrate that the model meets the needs for scalable, understandable, and regulated vehicular energy forecasting in smart cities.

Background:People who have Alzheimer's disease (AD) experience a progressive decline in their neurological function, which leads to mental deterioration and diminished memory abilities, and altered behaviors that affect both patients and their care providers severely. Diagnosis of the disease at an early stage and with precision helps ensure appropriate intervention strategies. Objectives:Modern artificial intelligence (AI) technology is promising in medical use for imaging and diagnostic work, specifically involving AD detection and classification. This study aims to develop and evaluate an explainable transformer-based framework that leverages Bidirectional-Encoder representations from Image Transformers (BEiT) to automatically classify AD stages from magnetic resonance imaging (MRI) brain scans. Method:The proposed framework employs BEiT as a feature extractor on a dataset of 8511 MRI brain images categorized into three diagnostic groups (mild, moderate, and no impairment). Class imbalance is addressed through a Wasserstein generative adversarial network with gradient penalty-based oversampling strategy that generates synthetic MRI images for minority classes, and these images are combined with the original scans to form a balanced training set. Results:The experiments showed outstanding accuracy levels reaching 96%, while the F1-scores indicated 0.94, 1.00, and 0.95 for mild, moderate, and no AD group classifications. Performance evaluation metrics from the study demonstrate strong outcomes with a mean absolute error reaching 0.0727 and Cohen's kappa equaling 0.9451, while Matthews correlation coefficient reached 0.9455 and Hamming loss remained at 0.0365.

The model investigating the effects of activation energy and viscous dissipation on methanol-based hybrid nanofluid with a nonlinear heat source has important applications in thermal management systems that need effective energy control and heat transfer. This model can be used in fields where controlling temperature gradients and improving heat dissipation are crucial, such as electronics cooling, energy storage systems, and automobile engineering. The activation energy term enhances the comprehension of thermal behavior in chemical and industrial processes, and the inclusion of viscous dissipation effects enables more accurate modeling of fluid flow in high-temperature conditions. The nonlinear heat source feature is also helpful in the construction of sophisticated reactors, energy-efficient heating systems, and cooling systems for high-performance machinery and equipment. The main emphasize is to expands the fluid dynamics by nonlinear flow to whom it increases a metal's ductility and softness.

The sustainability of public health systems relies heavily on the mental health and well-being of the healthcare workforce. In resource-constrained settings like South Punjab, Pakistan, excessive job demands pose a significant risk to both employee welfare and the quality of healthcare delivery. This study examines the impact of job demands on job performance, specifically investigating the mediating role of emotional exhaustion as a critical determinant of public health service effectiveness. Grounded in the Job Demands–Resources (JD-R) model and Effort–Reward Imbalance (ERI) theory, this research utilized a quantitative cross-sectional design. Data were collected through a structured questionnaire administered to a purposively selected sample of 249 healthcare professionals, including doctors, nurses, and paramedical staff, from public sector institutions. Structural Equation Modeling (SEM) using the Partial Least Squares (PLS) technique was employed to test the hypothesized relationships. The findings indicate that high job demands exert a significant positive effect on emotional exhaustion (β = 0.835), which subsequently degrades job performance. Emotional exhaustion was found to significantly mediate the relationship between job demands and performance (β = 0.426), suggesting that workforce burnout is a primary pathway through which systemic pressures undermine healthcare service quality.

Due to the advent of Covid-19 pandemic, different socio-economic challenges have been faced by all the countries worldwide. Every country has formulated and implemented different measures, which includes limited uses of public places, social distancing, closure of schools and Universities etc. The purpose of the study is to understand the possibilities of Covid and post-Covid E-learning reforms in higher education system. Content analysis has been conducted with the help of a number of selected studies, specific search terms, and a pre-established criterion to consider articles focusing on higher education and E-learning outcomes. This systematic literature review (SLR) seeks to investigate research articles concerning Covid and post-Covid E-learning published from 2019 to 2026, sourced from publicly accessible databases. The study conducts a critical analysis of the most significant theories in this domain and employs bibliometric analysis, utilizing Microsoft Excel to develop pertinent research themes. The bibliometric examination includes trends in selected literature, identification of influential keywords, leading authors, co-occurrence networks, thematic maps, and factorial analyses. The PRISMA methodology was employed to ensure rigorous systematic screening. Consequently, this review constitutes a valuable addition to the existing body of literature. It indicates that there is a big difference between the developed and developing countries in terms of resources for imparting higher education through E-Learning. Furthermore, it has been noted that the teachers in developing countries are less conversant with the use of technology in E-learning at higher education level. The article suggests a study agenda for future institutional innovation management studies based on the findings of the SLR. References Abu Talib, M., Bettayeb, A. M., & Omer, R. I. (2021). Analytical study on the impact of technology in higher education during the age of COVID-19: Systematic literature review. Education and Information Technologies. https://doi.org/10.1007/s10639-021-10507-1 Aldulaim, E. (2021). E-learning in higher education and COVID-19 outbreak: Challenges and opportunities. Psychology and Education Journal, 58(2), 38–43. https://doi.org/10.17762/pae.v58i2.1054 Ali, S., Uppal, M. A., & Gulliver, S. R. (2019). A conceptual framework highlighting e-learning implementation barriers. Information Technology & People. https://doi.org/10.1108/ITP-10-2016-0246 Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the e-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25(6), 5261–5280. https://doi.org/10.1007/s10639-020-10219-y AlMalki, H. A., & Durugbo, C. M. (2022). Systematic review of institutional innovation literature: Towards a multi-level management model. Management Review Quarterly. https://doi.org/10.1007/s11301-022-00259-8 Amiri, S. M. H., Goswami, P., Islam, M. M., Kabir, M. S., Hossen, M. S., Barmmon, C. K., & Amiri, S. M. H. (2026). The future of learning is hybrid: Exploration of EdTech's role in shaping the post-pandemic educational landscape. SSRN Electronic Journal, 7(1), 1–16. https://doi.org/10.2139/ssrn.5561598 Aparício, M., Bação, F., & Oliveira, T. (2016). An e-learning theoretical framework. Educational Technology & Society, 19(1), 292–307. https://www.jstor.org/stable/jeductechsoci.19.1.292 Aucejo, E. M., French, J., Ugalde Araya, M. P., & Zafar, B. (2020). The impact of COVID-19 on student experiences and expectations: Evidence from a survey. Journal of Public Economics, 191, 104271. https://doi.org/10.1016/j.jpubeco.2020.104271 Bacher, H. A., Goodman, J., & Mulhern, C. (2021). Inequality in household adaptation to schooling shocks: COVID-induced online learning engagement in real time. Journal of Public Economics, 193, 104345. https://doi.org/10.1016/j.jpubeco.2020.104345 Bates, A., & Poole, G. (2003). Effective teaching with technology in higher education: Foundations for success. Jossey-Bass. Bell, M., Martin, G., & Clarke, T. (2004). Engaging in the future of e-learning: A scenarios-based approach. Education + Training, 46(6/7), 296–307. https://doi.org/10.1108/00400910410555204 Bogdandy, B., Tamas, J., & Toth, Z. (2020). Digital transformation in education during COVID-19: A case study. In 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom) (pp. 173–178). IEEE. https://doi.org/10.1109/CogInfoCom50765.2020.9237840 Butt, S., Umair, T., & Tajammal, R. (2024). Nexus between key determinants of service quality and students' satisfaction in higher education institutions (HEIs). Annals of Human and Social Sciences, 5(2), 659–671. https://doi.org/10.35484/ahss.2024(5-II-S)62 Butt, S., & Yazdani, N. (2023). Implementation of quality management practices and firm's innovation performance: Mediation of knowledge creation processes and moderating role of digital transformation. Pakistan Journal of Humanities and Social Sciences, 11(4), 3881–3902. Canovan, C., & Fallon, N. (2021). Widening the divide: The impact of school closures on primary science learning. SN Social Sciences, 1(5), 117. https://doi.org/10.1007/s43545-021-00122-9 Chaturvedi, K., Vishwakarma, D. K., & Singh, N. (2021). COVID-19 and its impact on education, social life and mental health of students: A survey. Children and Youth Services Review, 121, 105866. https://doi.org/10.1016/j.childyouth.2020.105866 Cicha, K., Rizun, M., Rutecka, P., & Strzelecki, A. (2021). COVID-19 and higher education: First-year students' expectations toward distance learning. Sustainability, 13(4), 1889. https://doi.org/10.3390/su13041889 Clarke, C., Mullin, M., McGrath, D., & Farrelly, N. (2024). University students and study habits. Irish Journal of Psychological Medicine, 41(2), 179–188. https://doi.org/10.1017/ipm.2021.28 Denis, S., & Frances, N. (2014). E-learning for university effectiveness in the developing world. Global Journal of Human-Social Science: G Linguistics & Education, 14(3). Draghici, A., Popescu, A.-D., Fistis, G., & Borca, C. (2014). Behaviour attributes that nurture the sense of e-learning community perception. Procedia Technology, 16, 745–754. https://doi.org/10.1016/j.protcy.2014.10.024 Fernández, B. J. M., Román, G. P., Reyes, R. M. M., & Montenegro, R. M. (2021). Impact of educational technology on teacher stress and anxiety: A literature review. International Journal of Environmental Research and Public Health, 18(2), 548. https://doi.org/10.3390/ijerph18020548 Finlay, M. J., Tinnion, D. J., & Simpson, T. (2022). A virtual versus blended learning approach to higher education during the COVID-19 pandemic: The experiences of a sport and exercise science student cohort. Journal of Hospitality, Leisure, Sport & Tourism Education, 30, 100363. https://doi.org/10.1016/j.jhlste.2021.100363 Gasser, P., Grajeda, A., Cordova, J. P., La Fuente, I., Cordova, P., Naranjo, H., & Sanjinés, A. (2025). Mental cost in higher education: A comparative study on academic stress as a predictor of mental health in university students during and after the COVID-19 pandemic. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2024.2445968 Gokah, T. K., Gupta, N., & Ndiweni, E. (2015). E-learning in higher education: Opportunities and challenges for Dubai. International Journal on E-Learning, 14(4), 443–470. No DOI was identified in the bibliographic records checked. Gopika, J. S., & Rekha, R. V. (2025). Awareness and use of digital learning before and during COVID-19. International Journal of Educational Reform, 34(4), 754–766. https://doi.org/10.1177/10567879231173389 Gunawardhana, L. K. P. D. (2020). Review of e-learning as a platform for distance learning in Sri Lanka. Education Quarterly Reviews, 3(2). https://doi.org/10.31014/aior.1993.03.02.126 Gupta, V. (2021). Globalized blended education: Securing synergies among far-flung universities. SN Social Sciences, 1(5), 126. https://doi.org/10.1007/s43545-021-00142-5 Hagans, K. S., & Good, R. H., III. (2013). Decreasing reading differences in children from disadvantaged backgrounds: The effects of an early literacy intervention. Contemporary School Psychology, 17(1), 103–117. Han, X., & Li, Y. (2025). Equity in digital education: Addressing the digital divide in a post-pandemic world. Frontiers in Educational Research, 8(1). https://doi.org/10.25236/fer.2025.080107 Hasan, N., & Bao, Y. (2020). Impact of "e-learning crack-up" perception on psychological distress among college students during COVID-19 pandemic: A mediating role of "fear of academic year loss." Children and Youth Services Review, 118, 105355. https://doi.org/10.1016/j.childyouth.2020.105355 Hermawan, D. (2021). The rise of e-learning in COVID-19 pandemic in private university: Challenges and opportunities. IJORER: International Journal of Recent Educational Research, 2(1), 86–95. https://doi.org/10.46245/ijorer.v2i1.77 Holmström, T., & Pitkänen, J. (2012). E-learning in higher education: A qualitative field study examining Bolivian teachers' beliefs about e-learning in higher education [Bachelor's thesis, Umeå University]. No DOI identified. Iglesias, P. S., Hernández, G. Á., Chaparro, P. J., & Prieto, J. L. (2021). Emergency remote teaching and students' academic performance in higher education during the COVID-19 pandemic: A case study. Computers in Human Behavior, 119, 106713. https://doi.org/10.1016/j.chb.2021.106713 Iivari, N., Sharma, S., & Ventä, O. L. (2020). Digital transformation of everyday life—How COVID-19 pandemic transformed the basic education of the young generation and why information management research should care? International Journal of Information Management, 55, 102183. https://doi.org/10.1016/j.ijinfomgt.2020.102183 Imran, M., Almusharraf, N., & Abbasova, M. Y. (2025). Digital learning transformation: A study of teachers' post-COVID-19 experiences. Social Sciences & Humanities Open, 11(2), 101228. https://doi.org/10.1016/j.ssaho.2024.101228 Imran, R., Fatima, A., Elbayoumi Salem, I., & Allil, K. (2023). Teaching and learning delivery modes in higher education: Looking back to move forward post-COVID-19 era. International Journal of Management Education, 21(2), 100805. https://doi.org/10.1016/j.ijme.2023.100805 Kumar, S., Wotto, M., & Bélanger, P. (2018). E-learning, M-learning and D-learning: Conceptual definition and comparative analysis. E-Learning and Digital Media, 15(4), 191–216. https://doi.org/10.1177/2042753018785180 Kuperman, V., Geva, E., Taler, V., & Thériault, K. (2026). Recovery from university grade inflation after the COVID-19 pandemic varies by faculty. Studies in Higher Education, 51(2), 406–421. https://doi.org/10.1080/03075079.2025.2470297 Li, Z., Li, Q., Han, J., & Zhang, Z. (2022). Perspectives of hybrid performing arts education in the post-pandemic era: An empirical study in Hong Kong. Sustainability, 14(15), 9194. https://doi.org/10.3390/su14159194 Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. International Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790 Lips, A. (2021). The situation of young people at home during COVID-19 pandemic. Childhood Vulnerability Journal. https://doi.org/10.1007/s41255-021-00014-3 Luo, T., Freeman, C., & Stefaniak, J. (2020). "Like, comment, and share"—Professional development through social media in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1659–1683. https://doi.org/10.1007/s11423-020-09790-5 MacLeod, C. M., & MacDonald, P. A. (2000). Interdimensional interference in the Stroop effect: Uncovering the cognitive and neural anatomy of attention. Trends in Cognitive Sciences, 4(10), 383–391. https://doi.org/10.1016/S1364-6613(00)01530-8 Mahajan, R., Lim, W. M., Kumar, S., & Sareen, M. (2023). COVID-19 and management education: From pandemic to endemic. International Journal of Management Education, 21(2), 100801. https://doi.org/10.1016/j.ijme.2023.100801 Martins, T. B., Lorenzetti Branco, J. H., Martins, T. B., Santos, G. M., & Andrade, A. (2025). Impact of social isolation during the COVID-19 pandemic on the mental health of university students and recommendations for the post-pandemic period: A systematic review. Brain, Behavior, & Immunity—Health, 43, 100941. https://doi.org/10.1016/j.bbih.2024.100941 Mishra, L., Gupta, T., & Shree, A. (2020). Online teaching-learning in higher education during lockdown period of COVID-19 pandemic. International Journal of Educational Research Open, 1, 100012. https://doi.org/10.1016/j.ijedro.2020.100012 Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097 Moravec, T., Štěpánek, P., & Valenta, P. (2015). The influence of using e-learning tools on the results of students at the tests. Procedia—Social and Behavioral Sciences, 176, 81–86. https://doi.org/10.1016/j.sbspro.2015.01.446 Mseleku, Z. (2020). A literature review of e-learning and e-teaching in the era of COVID-19 pandemic. International Journal of Innovative Science and Research Technology, 5. No DOI identified in the source checked. Muneeba, A. (2024). Teacher professional development in the digital age: Addressing the evolving needs post-COVID. International Journal for Multidisciplinary Research, 6(1). https://doi.org/10.36948/ijfmr.2024.v06i01.12386 Mustea, A., Mureşan, M., & Herman, C. (2014). Integrating e-learning into the transdisciplinary methodology as a solution to the challenges of 21st century society. Procedia—Social and Behavioral Sciences, 128, 366–372. https://doi.org/10.1016/j.sbspro.2014.03.173 Nadkarni, S., & Prügl, R. (2021). Digital transformation: A review, synthesis and opportunities for future research. Management Review Quarterly, 71(2), 233–341. https://doi.org/10.1007/s11301-020-00185-7 Nawaz, Q. A. (2015). Challenges and opportunity of e-learning in developed and developing countries—A review. International Journal of Emerging Research in Management & Technology. No DOI identified. Nortvig, A.-M., Petersen, A. K., & Balle, S. H. (2018). A literature review of the factors influencing e-learning and blended learning in relation to learning outcome, student satisfaction and engagement. Electronic Journal of E-Learning, 16(1), 46–55. No DOI identified. Otto, S., Bertel, L. B., Lyngdorf, N. E. R., Markman, A. O., Andersen, T., & Ryberg, T. (2024). Emerging digital practices supporting student-centered learning environments in higher education: A review of literature and lessons learned from the COVID-19 pandemic. Education and Information Technologies, 29(2), 1673–1696. https://doi.org/10.1007/s10639-023-11789-3 Palvia, S., Aeron, P., Gupta, P., Mahapatra, D., Parida, R., Rosner, R., & Sindhi, S. (2019). Online education: Worldwide status, challenges, trends, and implications. Journal of Global Information Technology Management, 21(4), 233–241. https://doi.org/10.1080/1097198X.2018.1542262 Proff, A., Musalam, R., & Matar, F. (2025). Lessons learned for leaders: Implications for parent-school communication in post-pandemic learning environments. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1496319 Ractham, P., & Chen, C. (2013). Promoting the use of online social technology as a case-based learning tool. Journal of Information Systems Education, 24(4). No DOI identified. Rafiq, F. (2020). Analyzing students' attitude towards e-learning: A case study in higher education in Pakistan. Pakistan Social Sciences Review, 4(1), 367–380. https://doi.org/10.35484/pssr.2020(4-i)29 Rafiq, M., & Ameen, K. (2019). Use of digital media and demand for digitized contents in higher education sector of Pakistan. International Information & Library Review, 44(3), 116–122. https://doi.org/10.1080/10572317.2012.10762922 Reis, J., Amorim, M., Melão, N., Cohen, Y., & Rodrigues, M. (2019). Digitalization: A literature review and research agenda. In Advances in intelligent systems and computing (pp. 443–456). Springer. https://doi.org/10.1007/978-3-030-43616-2_47 Ren, X., Sotardi, V. A., & Brown, C. (2025). Exploring academic stress and coping experiences among university students during the COVID-19 pandemic. Education Sciences, 15(3). https://doi.org/10.3390/educsci15030314 Rendi, & Rahmawati, Y. (2024). Reading habits and comprehension: A study of university students. Borneo Educational Journal (Borju), 6(2), 196–211. https://doi.org/10.24903/bej.v6i2.1762 Rodrigues, H., Almeida, F., Figueiredo, V., & Lopes, S. L. (2019). Tracking e-learning through published papers: A systematic review. Computers & Education, 136, 87–98. https://doi.org/10.1016/j.compedu.2019.03.007 Roman, M., & Plopeanu, A. P. (2021). The effectiveness of the emergency eLearning during COVID-19 pandemic: The case of higher education in economics in Romania. International Review of Economics Education, 37, 100218. https://doi.org/10.1016/j.iree.2021.100218 Ryan, S., Kaufman, J., Greenhouse, J., She, R., & Shi, J. (2016). The effectiveness of blended online learning courses at the community college level. Community College Journal of Research and Practice, 40(4), 285–298. https://doi.org/10.1080/10668926.2015.1044584 Sailer, M., Schultz-Pernice, F., & Fischer, F. (2021). Contextual facilitators for learning activities involving technology in higher education: The C♭-model. Computers in Human Behavior, 121, 106794. https://doi.org/10.1016/j.chb.2021.106794 Samir, A. E.-S. M., Taj-Eddin, I. A. T. F., Seddiek, N., El-Khouly, M. M., & Nosseir, A. (2019). E-learning and students' motivation: A research study on the effect of e-learning on higher education. International Journal of Emerging Technologies in Learning, 9(4), 20–26. https://doi.org/10.3991/ijet.v9i4.3465 Sathishkumar, V., Radha, R., Saravanakumar, A., & Mahalakshmi, K. (2020). E-learning during lockdown of COVID-19 pandemic: A global perspective. International Journal of Control and Automation, 13(4), 1088–1099. No DOI identified; the article is indexed with its journal/article URL instead. Schmidtlein, F. A., & Milton, T. H. (1990). A review of literature on higher education institutional planning. National Center for Postsecondary Governance and Finance, University of Maryland. Seed Ahmed, M., Soltani, A., Zahra, D., Allouch, S., Al Saady, R. M., Nasr, A., … Ali, K. (2025). Remote online learning reimagined: Perceptions and experiences of medical students in a post-pandemic world. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-025-06815-6 Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2020). Effects of COVID-19 in e-learning on higher education institution students: The group comparison between male and female. Quality & Quantity. https://doi.org/10.1007/s11135-020-01028-z Shek, D. T. L. (2021). COVID-19 and quality of life: Twelve reflections. Applied Research in Quality of Life. https://doi.org/10.1007/s11482-020-09898-z Smith, B. P. (2009). Student ratings of teaching effectiveness for faculty groups based on race and gender. Education, 129(4), 615–624. Tawfik, A. A., Gatewood, J., Gish-Lieberman, J. J., & Hampton, A. J. (2021). Toward a definition of learning experience design. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-020-09482-2 Valverde, J., Burgos, C., & Morales, M. B. (2020). Trends in educational research about e-learning: A systematic literature review (2009–2018). Sustainability, 12(12), 5153. https://doi.org/10.3390/su12125153 van Zyl, L. E. (2021). Social study resources and social wellbeing before and during the intelligent COVID-19 lockdown in the Netherlands. Social Indicators Research. https://doi.org/10.1007/s11205-021-02654-2 Vinet, L., & Zhedanov, A. (2011). A "missing" family of classical orthogonal polynomials. Journal of Physics A: Mathematical and Theoretical, 44(8), 085201. https://doi.org/10.1088/1751-8113/44/8/085201 Vladova, G., Ullrich, A., Bender, B., & Gronau, N. (2021). Students' acceptance of technology-mediated teaching—How it was influenced during the COVID-19 pandemic in 2020: A study from Germany. Frontiers in Psychology, 12, 636086. https://doi.org/10.3389/fpsyg.2021.636086 Wang, Q., Zhu, Z., Chen, L., & Yan, H. (2009). E-learning in China. Campus-Wide Information Systems, 26(2), 77–81. https://doi.org/10.1108/10650740910946783 Woldegiorgis, E. T. (2026). Challenges of integrating technology in East African higher education systems in the post-pandemic era: Emerging academic cultures. Technology, Pedagogy and Education, 35(2), 361–379. https://doi.org/10.1080/1475939X.2025.2520589 World Bank. (2020). Global economic prospects, June 2020. World Bank. https://doi.org/10.1596/978-1-4648-1553-9 Younas, M., Dong, Y., Zhao, G., Menhas, R., Luan, L., & Noor, U. (2025). Unveiling digital transformation and teaching prowess in English education during COVID-19 with structural equation modelling. European Journal of Education, 60(1). https://doi.org/10.1111/ejed.12818 Zawack, R. O. (2021). The current state and impact of COVID-19 on digital higher education in Germany. Human Behavior and Emerging Technologies, 3(1), 218–226. https://doi.org/10.1002/hbe2.238