ISSN: 2222-6990
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The rapid advancement of artificial intelligence (AI) has spurred its adoption across various industries, including transportation and environmental sustainability sectors for example in End-of-Life Vehicle (ELV) management. AI holds the potential to revolutionize ELV practices by enhancing the efficiency of dismantling processes, optimizing recycling techniques, and ensuring resource recovery in a sustainable manner. However, despite the promising prospects, the success of AI implementation in ELV depends significantly on the perceptions and readiness of technical graduates entering the workforce. A survey was distributed anonymously to recent graduates from four institutions within the Malaysian Technical University Network (MTUN). A total of 152 responses were analysed. The results suggest that 67.1% of respondents were aware of the use of AI in ELV practices, and 75.7% agreed that AI could improve the efficacy of vehicle dismantling processes. Nevertheless, a substantial socio-economic challenge was underscored by the 36.2% of graduates who expressed apprehensions regarding the potential for AI to result in job displacement. Furthermore, 88.8% of respondents expressed the belief that additional education and training on AI in ELV practices are essential. These results emphasise the necessity of targeted educational reforms and industry partnerships to address the AI preparedness disparity among technical graduates. The study concludes with suggestions for policymakers, educators, and industry leaders to guarantee that the integration of AI not only improves the efficacy of ELVs but also fosters a skilled workforce that is prepared to adopt future technological advancements.
Alam, M. J., Ogawa, K., & Ahsan, A. H. M. (2024). Japanese investment for skills development program in attaining sustainable employability: The case of Bangladesh. World Development Sustainability, 4. https://doi.org/10.1016/j.wds.2024.100150
Ayanwale, M. A., Molefi, R. R., & Matsie, N. (2023). Modelling secondary school students’ attitudes toward TVET subjects using social cognitive and planned behavior theories. Social Sciences and Humanities Open, 8(1). https://doi.org/10.1016/j.ssaho.2023.100478
Cardamone, G. F., Ardolino, F., & Arena, U. (2022). Can plastics from end-of-life vehicles be managed in a sustainable way? Sustainable Production and Consumption, 29, 115–127. https://doi.org/10.1016/j.spc.2021.09.025
Elkadry, H., Shamsuzzaman, M., Piya, S., Haridy, S., Bashir, H., & Khadem, M. (2024). A fuzzy Delphi-AHP framework for identifying and prioritizing factors affecting students’ satisfaction in public high schools: Insights from the United Arab Emirates. Journal of Engineering Research (Kuwait). https://doi.org/10.1016/j.jer.2023.12.008
Florin Cojanu, P. V. (2017). New Perspectives To Develop Psychomotor Capacity For Romanian Childrens From Primary. Social & Behavioural Sciences Edu World 2016 7th International Conference.
Ismail, N., & Yusof, U. K. (2023). A systematic literature review: Recent techniques of predicting STEM stream students. Computers and Education: Artificial Intelligence, 5. https://doi.org/10.1016/j.caeai.2023.100141
Jeske, D., Sheehan, C., Linehan, M., & Sheehan, & Linehan. (2017). Employee engagement in discussion: Goals, perspectives and recommendations. Journal of Human Resource Management, 21–29.
Krause, D. R., & Ellram, L. M. (1997). Critical Elements of Supplier Development the Buying-Firm Perspective. European Journal of Purchasing & Supply Management, 3(1), 21–31. https://doi.org/10.1016/S0969-7012(96)00003-2
Liao, F. N., Zhang, C., Zhang, J. J., Yan, X., & Chen, T. X. (2024). Hyperbole or reality? The effect of auditors’ AI education on audit report timeliness. International Review of Financial Analysis, 91. https://doi.org/10.1016/j.irfa.2023.103050
Lee, S. J., & Kwon, K. (2024). A systematic review of AI education in K-12 classrooms from 2018 to 2023: Topics, strategies, and learning outcomes. Computers and Education: Artificial Intelligence, 6. https://doi.org/10.1016/j.caeai.2024.100211
Liao, F. nan, Zhang, C., Zhang, J. jin, Yan, X., & Chen, T. xiang. (2024). Hyperbole or reality? The effect of auditors’ AI education on audit report timeliness. International Review of Financial Analysis, 91. https://doi.org/10.1016/j.irfa.2023.103050
Magagula, M. M., & Awodiji, O. A. (2024). The implications of the fourth industrial revolution on technical and vocational education and training in South Africa. Social Sciences and Humanities Open, 10. https://doi.org/10.1016/j.ssaho.2024.100896
Makarova, I., Shubenkova, K., Package, C. E., Buyvol, P., Shepelev, V., & Gritsenko, A. (2021). The role of reverse logistics in the transition to a circular economy: Case study of automotive spare parts logistics. Waste Management, 173–185.
Nur, T., Raja, A., Zameri, M., Saman, M., Sharif, S., Simic, V., & Wahab, D. A. (2018). Development of a performance evaluation tool for end-of-life vehicle management system implementation using the analytic hierarchy process. https://doi.org/10.1177/0734242X18790361
Rao, J., Hagare, D., & Tao, Z. (2024). Upcycling of End-of-Life-Vehicle ( ELV ) plastics as a replacement for natural fine aggregate in concrete. Resources, Conservation & Recycling Advances, 21(March).
Saidin, Z. H. (2020). The Influence of After-Sales Service Quality, Relationship Quality and Alternative Attractiveness On Customer Loyalty of Malaysian National Carmakers.
Sanusi, I. T., Olaleye, S. A., Oyelere, S. S., & Dixon, R. A. (2022). Investigating learners’ competencies for artificial intelligence education in an African K-12 setting. Computers and Education Open, 3, 100083. https://doi.org/10.1016/j.caeo.2022.100083
Stöhr, C., Ou, A. W., & Malmström, H. (2024). Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence, 7. https://doi.org/10.1016/j.caeai.2024.100259
Summers, A., Haddad, M. El, Prichard, R., Clarke, K. A., Lee, J., & Oprescu, F. (2024). Navigating challenges and opportunities: Nursing student’s views on generative AI in higher education. Nurse Education in Practice, 79. https://doi.org/10.1016/j.nepr.2024.104062
Yao, N., & Wang, Q. (2024). Factors influencing pre-service special education teachers’ intention toward AI in education: Digital literacy, teacher self-efficacy, perceived ease of use, and perceived usefulness. Heliyon, 10(14). https://doi.org/10.1016/j.heliyon.2024.e34894
Yuik, C. J., Zameri, M., Saman, M., Hasrul, N., Ngadiman, A., & Hamzah, H. S. (2023). Supply chain optimisation for recycling and remanufacturing sustainable management in end-of-life vehicles?: A mini-review and classification. Waste Management & Research, 41(13), 554–565. https://doi.org/10.1177/0734242X221123486
Mohamad, A. F. Bin, Suhaimin, M. K. N. Bin, Akmal, S., Hasrulnizzzam, W., & Rahim, H. R. A. (2024). Perception of Ai Integration in End-of-Life Vehicle (ELV) Practices among Technical Graduates In Malaysia. International Journal of Academic Research in Business and Social Sciences, 14(10), 539–551.
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