Enhancing Talent Development Using AI-Driven Curriculum-Industry Integration

Norhaslinda Kamaruddin; Abdul Wahab Abdul Rahman; Frederick C. Harris Jr.

doi:10.21834/e-bpj.v8i26.5129

Authors

Norhaslinda Kamaruddin Universiti Teknologi MARA
Abdul Wahab Abdul Rahman Kulliyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia
Frederick C. Harris Jr. Department of Computer Science and Engineering, University of Nevada at Reno, Nevada, United States

DOI:

https://doi.org/10.21834/e-bpj.v8i26.5129

Keywords:

Talent Development, Artificial Intelligence, Graduate Employability, Industry Needs

Abstract

The specific hiring needs render low-skill-based job-seeking invalid in coping with the nation's economic development. There needs to be more graduate readiness for the industry's needs. This paper explores the transformative potential of Artificial Intelligence (AI) in fostering a symbiotic relationship between academic curricula and industry demands, aimed at building a robust talent pool for the future. A new hiring selection model that matches industry-identified hiring parameters with the knowledge and skills obtained from the university. By aligning educational programs with real-world challenges and market needs, this novel approach seeks to propel the growth of talents.

References

Baluch, A. & Main, K. (2023, July 24). 10 best free job posting sites (August 2023). https://www.forbes.com/advisor/business/best-free-job-posting-sites/

Bongard, A. (2019). Automating talent acquisition: Smart recruitment, predictive hiring algorithms, and the data-driven nature of artificial intelligence. Psychosociological Issues in Human Resource Management, 7(1), 36–41.

Cacciolatti, L., Lee, S. H., & Molinero, C. M. (2017). Clashing institutional interests in skills between Government and industry: An analysis of demand for technical and soft skills of graduates in the UK. Technological Forecasting and Social Change, 119, 139-153. https://doi.org/10.1016/j.techfore.2017.03.024 DOI: https://doi.org/10.1016/j.techfore.2017.03.024

Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and Improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17-26.

Gerencer, T. (2023, August 23). 30+ best job search engines & boards for finding a job 2023. https://zety.com/blog/best-job-search-sites

Hamiz, M., Bakri, M., Kamaruddin, N., & Mohamed, A. (2018). Assessment analytic theoretical framework based on learners’ continuous learning improvement. Indonesian Journal of Electrical Engineering and Computer Science, 11(2), 682-687. https://doi.org/10.11591/ijeecs.v11.i2.pp682-687 DOI: https://doi.org/10.11591/ijeecs.v11.i2.pp682-687

Hughey, J. (2020). Individual personalized learning. Educational Considerations, 46(2), 10. https://doi.org/10.4148/0146-9282.2237 DOI: https://doi.org/10.4148/0146-9282.2237

Indeed (2023). Indeed. https://malaysia.indeed.com/

Jobstreet (2023). Jobstreet by SEEK. https://www.jobstreet.com.my/jobs

Kamaruddin, N., Rahman, A. W. A., & Lawi, R. A. M. (2019). Jobseeker-industry matching system using automated keyword selection and visualization approach. Indonesian Journal of Electrical Engineering and Computer Science, 13(3), 1124-1129. http://doi.org/10.11591/ijeecs.v13.i3.pp1124-1129 DOI: https://doi.org/10.11591/ijeecs.v13.i3.pp1124-1129

ManpowerGroup (2023, August 28). 2023 Global Talent Shortage Survey. https://go.manpowergroup.com/hubfs/MPG_TS_2023_Infographic_FINAL.pdf

Manyika, J., Lund, S., Chui, M., Bughin, J. Woetzel, J., Batra, P. Ko, R. & Sanghvi S. (2017, November 28). Jobs lost, jobs gained: Workforce transitions in a time of automation. https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages

Mezhoudi, N., Alghamdi, R., Aljunaid, R., Krichna, G., & Düştegör, D. (2023). Employability prediction: a survey of current approaches, research challenges and applications. Journal of Ambient Intelligence and Humanized Computing, 14(3), 1489-1505. https://doi.org/10.1007/s12652-021-03276-9 DOI: https://doi.org/10.1007/s12652-021-03276-9

Ministry of Economy, Department of Statistic Malaysia Official Website (2022, July 27). Graduates statistic 2021. https://www.dosm.gov.my/portal-main/release-content/graduates-statistics-2021

National Federation of Independent Business. (2023, July 11). Small business owners continue to manage challenging economic environment in July. https://www.nfib.com/surveys/small-business-economic-trends/

Shrivastava, S., Bardoel, E. A., Djurkovic, N., Rajendran, D., & Plueckhahn, T. (2022). Co-creating curricula with industry partners: A case study. The International Journal of Management Education, 20(2), 100646. https://doi.org/10.1016/j.ijme.2022.100646 DOI: https://doi.org/10.1016/j.ijme.2022.100646

Sjöö, K., & Hellström, T. (2019). University–industry collaboration: A literature review and synthesis. Industry and Higher Education, 33(4), 275-285. https://doi.org/10.1177/0950422219829697 DOI: https://doi.org/10.1177/0950422219829697

Valiente Bermejo, M., Eynian, M., Malmsköld, L., & Scotti, A. (2022). University–industry collaboration in curriculum design and delivery: A model and its application in manufacturing engineering courses. Industry and Higher Education, 36(5), 615-622. https://doi.org/10.1177/09504222211064204 DOI: https://doi.org/10.1177/09504222211064204

Weaver, A., & Osterman, P. (2017). Skill demands and mismatch in U.S. Manufacturing. ILR Review, 70(2), 275–307. https://doi.org/10.1177/0019793916660067 DOI: https://doi.org/10.1177/0019793916660067

World Economic Forum. (2020, October 1). The Future of Jobs Report 2020. http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf