Shaping AI-related competencies for labor market and business. A PLS-SEM approach
DOI:
https://doi.org/10.15837/ijccc.2025.1.6894Keywords:
Artificial intelligence (AI), educator, competencies, labor market, business sectorAbstract
In the era of digitalization and rapid technological advancement, artificial intelligence (AI) has emerged as a decisive factor in transforming the labor market, requiring the continuous adjustment of educational competencies to prepare students for the labor market demand. This study investigates the impact of AI on educational requirements, identifying the essential competencies that educational systems and the business sector must shape to equip future professionals for AIdriven challenges and opportunities. Employing Partial Least Squares Structural Equation Modeling (SEM), the research analyzed the survey data from a sample of 138 educators from various pre-university and university environments in Bihor county, Romania. to determine the relationships between the educational system, business sector, educational competencies, and AI career preparedness. The findings show significant influences from both sectors in shaping competencies that, in turn, affect labor market demands. This study highlights the imperative for educational systems the business sector to develop forward-thinking programs that anticipate future changes, thereby maximizing an AI-driven economy’s economic and social benefits. The results indicate that both the educational system and the business sector are integral to developing the competencies required in the AI era, with AI career preparedness exerting the greatest influence on labor market demands.
References
Acemoglu, D.; Restrepo, P. (2018a); Artificial Intelligence, Automation, and Work, NBER Working Paper Series, 24196, Cambridge, MA: National Bureau of Economic Research.
Acemoglu, D.; Restrepo, P. (2018b); Low-skill and high-skill automation, Journal of Human Capital, 12(2), 204–232, https://doi.org/10.1086/697242.
Acemoglu, D.; Restrepo, P. (2018c); The race between man and machine: implications of technology for growth, factor shares, and employment, American Economic Review, 108(6), 1488–1542, https://doi.org/10.1257/aer.20160696.
Acemoglu, D.; Restrepo, P. (2020); Robots and jobs: evidence from US labor markets, Journal of Political Economy, 128(6), 2188–2244, https://doi.org/10.1086/705716.
Adams, C.; Pente, P.; Lemermeyer, G.; Rockwell, G. (2023); Ethical principles for artificial intelligence in K-12 education, Computers and Education: Artificial Intelligence, 4, 100131, https://doi.org/10.1016/j.caeai.2023.100131.
Adiguzel, T.; Kaya, M.; Cansu, F. (2023); Revolutionizing education with AI: Exploring the transformative potential of ChatGPT, Contemporary Educational Technology, 15(3), ep429, https://doi.org/10.30935/cedtech/13152.
Akgun, S.; Greenhow, C. (2022); Artificial intelligence in education: Addressing ethical challenges in K-12 settings, AI and Ethics, 2, 431–440, https://doi.org/10.1007/s43681-021-00096-7.
Alam, A.; Mohanty, A. (2022); Business Models, Business Strategies, and Innovations in EdTech Companies: Integration of Learning Analytics and Artificial Intelligence in Higher Education, IEEE 6th Conference on Information and Communication Technology (CICT), Gwalior, India, 1-6, doi: 10.1109/CICT56698.2022.9997887.
Alekseeva, L.; Azar, J.; Giné, M.; Samila, S.; Taska, B. (2021); The Demand for AI Skills in the Labor Market, Labor Economics, 71, 102002, https://doi.org/10.1016/j.labeco.2021.102002.
Aly, H. (2022); Digital transformation, development and productivity in developing countries: is artificial intelligence a curse or a blessing?, Review of Economics and Political Science, 7(4), 238–256, https://doi.org/10.1108/REPS-11-2019-0145.
Autor, D. (2015); Why are there still so many jobs? The history and future of workplace automation, Journal of Economic Perspectives, 29(3), 3–30, https://doi.org/10.1257/jep.29.3.3.
Awan, U.; Sroufe, R.; Shahbaz, M. (2021); Industry 4.0 and the circular economy: a literature review and recommendations for future research, Business Strategy and the Environment, 30(4), 2038–2060, https://doi.org/10.1002/bse.2731.
Beaudry, P.; Green, D.; Sand, B. (2016); The great reversal in the demand for skill and cognitive tasks, Journal of Labor Economics, 34(1), S199–S247, https://doi.org/10.1086/682347.
Beauducel, A.; Wittmann, W. W. (2005); Simulation study on fit indexes in CFA based on data with slightly distorted simple structure, Structural Equation Modeling, 12(1), 41-75.
Bellman, R. (1978); An Introduction to Artificial Intelligence: Can Computers Think?, San Francisco, California: Boyd & Fraser Publishing Company.
Berger, T.; Frey, C. (2016); Structural Transformation in the OECD: Digitalisation, Deindustrialisation and the Future of Work, OECD Social, Employment and Migration Working Papers, No. 193, OECD Publishing, https://doi.org/10.1787/5jlr068802f7.
Bholat, D.; Susskind, D. (2021); The assessment: Artificial intelligence and financial services, Oxford Review of Economic Policy, 37(3), 417–434, https://doi.org/10.1093/oxrep/grab015.
Bird, I. (2023); Reskilling your workforce in the time of AI, Available at: https://www.ibm.com/ blog/reskilling-your-workforce-in-the-time-of-ai/ [Accessed 15 06 2024].
Bostrom, N. (2017); Strategic Implications of Openness in AI Development, Global Policy, 8(2), 135-148, https://doi.org/10.1111/1758-5899.12403.
Bratti, M.; Matteucci, N. (2004); Is There Skill-biased Technological Change in Italian Manufacturing? Evidence from Firm-level Data, Università degli Studi di Ancona, Dipartimento di Economia, https://doi.org/10.1016/j.joms.2005.05.314.
Buckley, R.; Zetzsche, D.; Arner, D.; Tang, B. (2021); Regulating artificial intelligence in finance: Putting the human in the loop, The Sydney Law Review, 43(1), 43–81.
Chan, C. (2023); A comprehensive AI policy education framework for university teaching and learning, International Journal of Educational Technology in Higher Education, 20, 38, https://doi.org/10.1186/s41239-023-00408-3.
Chin, W. W. (2010); How to Write Up and Report PLS Analyses, In: Esposito Vinzi, V.; Chin, W. W.; Henseler, J.; Wang, H., Eds., Handbook of Partial Least Squares: Concepts, Methods and Applications, Springer, Heidelberg, Dordrecht, London, New York, 655-690, https://doi.org/10.1007/978-3-540-32827-8-29.
Chiu, T.K.F.; Xia, Q.; Zhou, X.; Chai, C.S.; Cheng, M. (2023); Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education, Computers and Education: Artificial Intelligence, 4, 100118, https://doi.org/10.1016/j.caeai.2022.100118.
Chiu, T. K. F.; Ahmad, Z.; Ismailov, M.; Sanusi, I. T. (2024); What are artificial intelligence literacy and competency? A comprehensive framework to support them, Computers and Education Open, 6, 100171, https://doi.org/10.1016/j.caeo.2024.100171.
Cronbach, L.J.; Shavelson, R.J. (2004); My Current Thoughts on Coefficient Alpha and Successor Procedures, Educational and Psychological Measurement, 64, 391-418, https://doi.org/10.1177/0013164404266386.
Diez, E. (2023); Artificial intelligence and social work: Contributions to an ethical artificial intelligence at the service of the people, In: A. López Peláez & G. Kirwan, eds., The Routledge International Handbook of Digital Social Work, Routledge, 368-381.
European Union (2022); The Impact of Artificial Intelligence on the Future of Workforces in the EU and the US, Available at: https://digital-strategy.ec.europa.eu/en/library/ impact-artificial-intelligence-future-workforces-eu-and-us, [Accessed 01 06 2024].
Farayola, O. A.; Abdul, A. A.; Irabor, B. O.; Okeleke, E.C. (2023); Innovative Business Models Driven by AI Technologies: A Review, Computer Science & IT Research Journal, 4(2), 85-110, https://doi.org/10.51594/csitrj.v4i2.608.
Felten, E.; Raj, M.; Seamans, R. (2021); Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses, Strategic Management Journal, 42(12), 2195-2217, https://doi.org/10.1002/smj.3286.
Fleming, P. (2018); Robots and organization studies: why robots might not want to steal your job, Organization Studies, 40(1), 23–38, https://doi.org/10.1177/0170840618765568.
Giuggioli, G.; Pellegrini, M.M. (2023); Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research, International Journal of Entrepreneurial Behavior & Research, 29(4), 816-837, https://doi.org/10.1108/IJEBR-05-2021-0426.
Ghailani, J. S.; Khan, S. A. (2004); Quality of Secondary Education and Labor Market Requirement, Journal of Services Research, 4(1), 161-172.
Gonzalez-Calatayud, V.; Prendes-Espinosa, P.; Roig-Vila, R. (2021); Artificial intelligence for student assessment: A systematic review, Applied Sciences, 11(12), 5467, https://doi.org/10.3390/app11125467.
Goos, M.; Manning, A. (2007); Lousy and lovely jobs: the rising polarization of work in Britain, The Review of Economics and Statistics, 89(1), 118–133, https://doi.org/10.1162/rest.89.1.118.
Graetz, G.; Michaels, G. (2018); Robots at work, The Review of Economics and Statistics, 100(5), 753–768, https://doi.org/10.1162/rest-a-00754.
Green, A. (2024); Artificial intelligence and the changing demand for skills in the labor market, OECD Publishing, Paris.
Green, A.; Lamby, L. (2023); The supply, demand and characteristics of the AI workforce across OECD countries, OECD Publishing, Paris, https://doi.org/10.1787/bb17314a-en.
Gregory, T.; Salomons, A.; Zierahn, U. (2016); Racing with or against the Machine? Evidence from Europe, ZEW-Centre for European Economic Research Discussion Paper, Mannheim, Germany, Paper 16-053, https://doi.org/10.2139/ssrn.2815469.
Haugeland, J. (1989); Artificial intelligence: The very idea, MIT Press, https://doi.org/10.7551/mitpress/1170.001.0001.
Herdan, A.; Stuss, M. M. (2019); Shaping the Competences of Graduates of Higher Education for the Needs of the EU Labor Market - Case Study of Business School Students, Proceedings of the 7th Teaching & Education Conference, London, https://doi.org/10.20472/TEC.2019.007.006.
Hu, L. T.; Bentler, P. M. (1999); Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives, Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
IBM Institute for Business Value (2024); Augmented work for an automated, AI-driven world, Available at: https://www.ibm.com/thought-leadership/institute-business-value/ en-us/report/augmented-workforce, [Accessed 15 06 2024].
Jongwanich, J.; Kohpaiboon, A.; Obashi, A. (2022); Technological advancement, import penetration and labor markets: evidence from Thailand, World Development, 151, 105746, https://doi.org/10.1016/j.worlddev.2021.105746.
Jorgenson, D. (2001); Information technology and the US economy, American Economic Review, 91(1), 1–32, https://doi.org/10.1257/aer.91.1.1.
Ka, H. (2023); Evaluation of Technological Breakthrough in Global Education and Future Employment Opportunity, Journal of Liberal Arts and Humanities, 8(4), 1-62, https://doi.org/10.48150/jlah.v4no8.2023.a1.
Kaggwa, S.; Eleogu, T. F.; Okonkwo, F.; Farayola, O. A.; Uwaoma, P. U.; Akinoso, A. (2024); AI in decision making: transforming business strategies, International Journal of Research and Scientific Innovation, 10(12), 423-444.
Kenny, D. A.; McCoach, D. B. (2003); Effect of the number of variables on measures of fit in structural equation modeling, Structural Equation Modeling, 10(3), 333-351.
Kim, K.; Kwon, K. (2023); Exploring the AI competencies of elementary school teachers in South Korea, Computers and Education: Artificial Intelligence, 4, 100137, https://doi.org/10.1016/j.caeai.2023.100137.
Luckin, R. (2017); Towards artificial intelligence-based assessment systems, Nature Human Behaviour, 1, 0028, https://doi.org/10.1038/s41562-016-0028.
Maxwell, S. E.; Delaney, H. D. (2004); Designing Experiments and Analyzing Data: A Model Comparison Perspective (2nd Edition), Mahwah, NJ: Lawrence Erlbaum Associates Publishers.
Mitchell, M. (2019); Artificial Intelligence: A Guide for Thinking Humans, London, UK: Pelican Books.
Ng, D.T.K.; Leung, J.K.L.; Su, J.; Ng, R.C.W.; Chu, S.K.W. (2023); Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world, Educational Technology Research and Development, 71, 137-161, https://doi.org/10.1007/s11423-023-10203-6.
OECD (2022); The impacts of artificial intelligence on the workplace, Available at: https://www.oecd.org/future-of-work/reports-and-data/ impacts-of-artificial-intelligence-on-the-workplace.htm, [Accessed 03 06 2024].
OECD (2023); Future of work, Available at: https://www.oecd.org/en/topics/ policy-issues/future-of-work.html, [Accessed 12 06 2024].
Organisation for Economic Co-operation and Development (2019); OECD AI Principles Overview, Available at: https://oecd.ai/en/ai-principles, [Accessed 23 05 2024].
Reamer, F. (2023); Artificial Intelligence in Social Work: Emerging Ethical Issues, International Journal of Social Work Values and Ethics, 20(2), DOI: 10.55521/10-020-205.
Shiohira, K. (2021); Understanding the impact of artificial intelligence on skills development, UNESCO-UNEVOC.
Simut, R.; Simut, C.; Badulescu, D.; Badulescu, A. (2024); Artificial Intelligence and the Modelling of Teachers’ Competencies, Amfiteatru Economic, 26(65), 181-200, https://doi.org/10.24818/EA/2024/65/181.
Sollosy, M.; McInerney, M. (2022); Artificial intelligence and business education: What should be taught, The International Journal of Management Education, 20(3), 100720, https://doi.org/10.1016/j.ijme.2022.100720.
Sun, Z.; Hou, Y. (2019); How does industrial intelligence reshape the employment structure of Chinese labor force, China Industrial Economics, 5, 61–79, https://doi.org/10.19581/j.cnki.ciejournal.2019.05.004.
Tedre, M.; Toivonen, T.; Kahila, J.; Vartiainen, H.; Valtonen, T.; Jormanainen, I.; Pears, A. (2021); Teaching Machine Learning in K–12 Classroom: Pedagogical and Technological Trajectories for Artificial Intelligence Education, IEEE Access, 9, 110558–110572, https://doi.org/10.1109/ACCESS.2021.3097962.
Terentyeva, I. V.; Kirillova, O.; Kirillova, T.; Pugacheva, N.; Lunev, A.; Chemerilova, I.; Luchinina, A. (2018); Arrangement of cooperation between labor market and regional vocational education system, International Journal of Educational Management, 32(6), 1041–1055, https://doi.org/10.1108/IJEM-10-2017-0296.
Thomas, M. (2017); The rise of technology and its influence on labor market outcomes, Proceedings of the Gettysburg Economic Review, https://cupola.gettysburg.edu/ger/vol10/iss1/3.
UK Government Office for Science (2016); Artificial intelligence: opportunities and implications for the future of decision making, Available at: https://www.gov.uk/government/ publications/artificial-intelligence-an-overview-for-policy-makers, [Accessed 23 05 2024].
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