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Building skills for the future of work: Students’ perspectives on emerging jobs in the Data and AI Cluster through artificial intelligence in education

Maja Rožman, Polona Tominc, Igor Vrečko

Article ID: 1670
Vol 8, Issue 2, 2023, Article identifier:

VIEWS - 3152 (Abstract) 1814 (PDF)

Abstract

The main goal of this paper is to supplement the existing literature with new knowledge in the field of artificial intelligence and education, which relates to the importance of courses in statistics, quantitative methods, and students’ perspectives about emerging jobs in the Data and AI Cluster. A multidimensional model of the perceived usefulness of artificial intelligence in students’ perspective about emerging jobs in the Data and AI Cluster was formed; it includes constructs students’ knowledge of the meaning of “artificial intelligence”, their perception of its usefulness in their studies, the perceived ease of use of AI, the perceived usefulness of statistics and quantitative methods, students’ perspective on work skills for the future, and their perspective on emerging jobs in the Data and AI Cluster. The empirical research included 197 undergraduate and postgraduate students from the University of Maribor, Faculty of Economics and Business in Slovenia, who had prior knowledge of statistics obtained during their studies. The data were analyzed using structural equation modeling. The main findings of our research are important for curricula development and stress these implications: emphasis on teaching the meaning and importance of AI, integration of AI in coursework, strengthening quantitative skills and developing future work skills that are aligned with emerging trends in the Data and AI Cluster.


Keywords

education; students; artificial intelligence; statistics; quantitative methods

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References

1. OECD. Trustworthy artificial intelligence (AI) in education. Available online: https://www.oecd.org/education/trustworthy-artificial-intelligence-ai-in-education-a6c90fa9-en.htm (accessed on 21 July 2023).

2. Kambur E, Akar C. Human resource developments with the touch of artificial intelligence: A scale development study. International Journal of Manpower 2022; 43(1): 168–205. doi: 10.1108/IJM-04-2021-0216

3. Agarwal P, Swami S, Malhotra SK. Artificial intelligence adoption in the post COVID-19 new-normal and role of smart technologies in transforming business: A review. Journal of Science and Technology Policy Management 2022. doi: 10.1108/JSTPM-08-2021-0122

4. UNESCO. AI in education: Change at the speed of learning. Available online: https://iite.unesco.org/wp-content/uploads/2020/11/Steven_Duggan_AI-in-Education_2020.pdf (accessed on 21 July 2023).

5. Tyson MM, Sauers NJ. School leaders’ adoption and implementation of artificial intelligence. Journal of Educational Administration 2021; 59(3): 271–285. doi: 10.1108/JEA-10-2020-0221

6. Razia B, Awwad B, Taqi N. The relationship between artificial intelligence (AI) and its aspects in higher education. Development and Learning in Organizations 2023; 37(3): 21–23. doi: 10.1108/DLO-04-2022-0074

7. Keleş PU, Aydın S. University students’ perceptions about artificial intelligence. Shanlax International Journal of Education 2021; 9(1): 212–220. doi: 10.34293/education.v9iS1-May.4014

8. Joshi S, Rambola RK, Churi P. Evaluating artificial intelligence in education for next generation. Journal of Physics Conference Series 2021; 1714(1): 012039. doi: 10.1088/1742-6596/1714/1/012039

9. Aljohani RA. Teachers and students’ perceptions on the impact of artificial intelligence on English language learning in Saudi Arabia. Journal of Applied Linguistics and Language Research 2021; 8(1): 36–47. doi: 10.5296/ijele.v9i2.18782

10. Kairu C. Students’ attitude towards the use of artificial intelligence and machine learning to measure classroom engagement activities. In: Proceedings of EdMedia + Innovate Learning; 23 June 2020; online. pp. 793–802.

11. Al-Badi A, Khan A, Alotaibi E. Perceptions of learners and instructors towards artificial intelligence in personalized learning. Procedia Computer Science 2022; 201(3): 445–451. doi: 10.1016/j.procs.2022.03.058

12. Rodzman SBB, Bakar NA, Choo YH, et al. I-OnAR: A rule-based machine learning approach for intelligent assessment in an online learning environment. Indonesian Journal of Electrical Engineering and Computer Science 2019; 17(2): 1021–1028. doi: 10.11591/ijeecs.v17.i2.pp1021-1028

13. Chang J, Lu X. The study on students’ participation in personalized learning under the background of artificial intelligence. In: Proceedings of 2019 10th International Conference on Information Technology in Medicine and Education (ITME); 23–25 August 2019; Qingdao, China.

14. Bhatt P, Muduli A. Artificial intelligence in learning and development: A systematic literature review. European Journal of Training and Development 2022. doi: 10.1108/ejtd-09-2021-0143

15. Mun SC. AI in education: Building skills for the future of work. Available online: https://munshing.com/education/ai-in-education-building-skills-for-the-future-of-work (accessed on 21 July 2023).

16. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science 2021; 372(7): 1205–1210. doi: 10.1126/science.aaa8415

17. Friedrich S, Antes G, Behr S, et al. Is there a role for statistics in artificial intelligence? Advances in Data Analysis and Classification 2022; 16: 823–846. doi: 10.1007/s11634-021-00455-6

18. Cakır R, Solak E. Attitude of Turkish EFL learners towards e-learning through tam model. Procedia-Social and Behavioral Sciences 2015; 176(2): 596–601. doi: 10.1016/j.sbspro.2015.01.515

19. Sánchez-Prieto JC, Olmos-Migueláñez S, García-Peñalvo F. Learning and pre-service teachers: An assessment of the behavioral intention using an expanded Tam model. Computers in Human Behavior 2017; 72: 644–654. doi: 10.1016/j.chb.2016.09.061

20. Padilla-Meléndez A, Aguila-Obra AR, Garrido-Moreno A. Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education 2014; 63: 306–317. doi: 10.1016/j.compedu.2012.12.014

21. Saad R, Bahli B. The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management 2005; 42(2): 317–327. doi: 10.1016/j.im.2003.12.013

22. Vlasova VK, Simonova GI, Soleymani N. Pedagogical support components of students’ social adaptation. International Journal of Environmental and Science Education 2016; 5: 641–653. doi: 10.12973/ijese.2016.337a

23. Sumakul DTYG, Hamied FA, Sukyadi D. Students’ perceptions of the use of AI in a writing class. Advances in Social Science, Education and Humanities Research 2021; 624(4): 1–6. doi: 10.2991/assehr.k.220201.009

24. Chai CS, Lin P, Jong MSY, et al. Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society 2021; 24(3): 89–101.

25. Rabah T, Mukhallafi A. Using artificial intelligence for developing English language teaching/learning: An analytical study from university students’ perspective. International Journal of English Linguistics 2020; 10(6): 40–53. doi: 10.5539/ijel.v10n6p40

26. Holmes W, Bialik M, Fadel C. Artificial Intelligence in Education. Center for Curriculum Redesign; 2019.

27. Homes M. Near real-time comprehension classification with artificial neural networks: Decoding e-learner non-verbal behavior. IEEE Transactions on Learning Technologies 2018; 11(1): 5–12. doi: 10.1109/TLT.2017.2754497

28. Melesko J, Eugenijus K. Semantic technologies in e-learning: Learning analytics and artificial neural networks in personalised learning systems. In: Proceedings of 8th International Conference on Web Intelligence, Mining and Semantics; 25–27 June 2018; Novi Sad, Serbia. pp. 1–7.

29. Birjali M, Mohammed E. A novel adaptive e-learning model based on big data by using competence-based knowledge and social learner activities. Applied Soft Computing 2018; 69(1): 14–32. doi: 10.1016/j.asoc.2018.04.030

30. Lisa-Angelique L, Dawson S, Gašević D, et al. Students’ perceptions of, and emotional responses to, personalised learning analytics-based feedback: An exploratory study of four courses. Assessment & Evaluation in Higher Education 2021; 46(3): 339–359. doi: 10.1080/02602938.2020.1782831

31. Abelardo P, Jovanovic J, Dawson S, et al. Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology 2019; 50(1): 128–138. doi: 10.1111/bjet.12592

32. Islam AK, Al-Badi A. Emerging data sources in decision making and AI. Procedia Computer Science 2020; 177(6): 318–323. doi: 10.1016/j.procs.2020.10.042

33. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 1989; 13(3): 319–340. doi: 10.2307/249008

34. Kim NY. A study on the use of artificial intelligence chatbots for improving English grammar skills. Journal of Digital Convergence 2019; 17(8): 37–46. doi: 10.14400/JDC.2019.17.8.037

35. Obari H, Lambacher S. Improving the English skills of native Japanese using artificial intelligence in a blended learning program. Available online: https://files.eric.ed.gov/fulltext/ED600973.pdf (accessed on 21 July 2023).

36. Šebjan U, Tominc P. Impact of support of teacher and compatibility with needs of study. Computers in Human Behavior 2015; 53: 354–365. doi: 10.1016/j.chb.2015.07.022

37. Tominc P, Krajnc M, Vivod K, et al. Students’ behavioral intentions regarding the future use of quantitative research methods. Naše Gospodarstvo/Our Economy 2018; 64(2): 25–33. doi: 10.2478/ngoe-2018-0009

38. Ferguson SL, Hovey KA, Henson RK. Quantitative preparation in doctoral education programs: A mixed-methods study of doctoral student perspectives on their quantitative training. International Journal of Doctoral Studies 2017; 12: 137–156. doi: 10.28945/3789

39. Ahmad K, Qadir J, Al-Fuqaha A, et al. Data-driven artificial intelligence in education: A comprehensive review. Available online: https://edarxiv.org/zvu2n/ (accessed on 21 July 2023).

40. Karandish D. 7 benefits of AI in education. Available online: https://thejournal.com/articles/2021/06/23/7-benefits-of-ai-in-education.aspx (accessed on 21 July 2023).

41. Mansor NA, Hamid Y, Anwar ISK, et al. The awareness and knowledge on artificial intelligence among accountancy students. International Journal of Academic Research in Business and Social Sciences 2022; 12(11): 1629–1640. doi: 10.6007/IJARBSS/v12-i11/15307

42. Steyn P. Why is statistics important in data science, machine learning, and analytics. Available online: https://towardsdatascience.com/why-is-statistics-important-in-data-science-machine-learning-and-analytics-92b4a410f686 (accessed on 21 July 2023).

43. OECD. The impact of big data and artificial intelligence (AI) in the insurance sector. Available online: www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.htm (accessed on 21 July 2023).

44. Boulay B. Artificial intelligence as an effective classroom assistant. IEEE Intelligent Systems 2016; 31(6): 76–81. doi: 10.1109/MIS.2016.93

45. Guan C, Mou J, Jiang Z. Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies 2020; 4(4): 134–147. doi: 10.1016/j.ijis.2020.09.001

46. Munir H, Vogel B, Jacobsson A. Artificial intelligence and machine learning approaches in digital education: A systematic revision. Information 2022; 13(4): 1–26. doi: 10.3390/info13040203

47. Chincholi A. How AI is changing the way students learn. Available online: https://www.forbes.com/sites/forbestechcouncil/2022/09/20/how-ai-is-changing-the-way-students-learn/?sh=674979687338 (accessed on 21 July 2023).

48. Seo K, Tang J, Roll I, et al. The impact of artificial intelligence on learner-instructor interaction in online learning. International Journal of Educational Technology in Higher Education 2021; 18(12): 54–67. doi: 10.1186/s41239-021-00292-9

49. Chai CS, Wang X, Xu C. An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn artificial intelligence. Mathematics 2020; 8(11): 1–18. doi: 10.3390/math8112089

50. Cruz-Benito J, Sánchez-Prieto JC, Therón R, et al. Measuring students’ acceptance to AI-driven assessment in eLearning: Proposing a first TAM-based research model. In: Zaphiris P, Ioannou A (editors). Learning and Collaboration Technologies. Designing Learning Experiences. Springer; 2019. pp. 15–25. doi: 10.1007/978-3-030-21814-0_2

51. Wogu IAP, Misra S, Olu-Owolabi EF, et al. Artifcial intelligence, artifcial teachers and the fate of learners in the 21st century education sector: Implications for theory and practice. International Journal of Pure and Applied Mathematics 2018; 119(16): 2245–2259.

52. Melo N. Incorporating artificial intelligence into the classroom: An examination of benefits, challenges, and best practices. Available online: https://elearningindustry.com/incorporating-artificial-intelligence-into-classroom-examination-benefits-challenges-and-best-practices (accessed on 21 July 2023).

53. Bryla M. Data literacy : A critical skill for the 21st century. Available online: https://www.tableau.com/about/blog/2018/9/data-literacy-critical-skill-21st-century-94221 (accessed on 21 July 2023).

54. Reece J. Starting a career in artificial intelligence. Available online: https://www.bestcolleges.com/blog/future-proof-industries-artificial-intelligence/ (accessed on 21 July 2023).

55. Selenko E, Bankins S, Shoss M, et al. Artificial intelligence and the future of work: A functional-identity perspective. Current Directions in Psychological Science 2022; 31(3): 272–279. doi: 10.1177/09637214221091823

56. Mohammadi A, Yang J, Borgianni Y, Zeng Y. Barriers and enablers of TRIZ: A literature analysis using the TASKS framework. Journal of Engineering, Design and Technology 2022. doi: 10.1108/JEDT-01-2022-0066

57. Wang YP. Effects of online problem-solving instruction and identification attitude toward instructional strategies on students’ creativity. Frontiers in Psychology 2021; 14(2): 1–6. doi: 10.3389/fpsyg.2021.771128

58. Wang YP, Wu TJ. Effects of online cooperative learning on students’ problem-solving ability and learning satisfaction. Frontiers in Psychology 2022; 10(13): 1–7. doi: 10.3389/fpsyg.2022.817968

59. Heidicker P, Langbehn E, Steinicke F. Infuence of avatar appearance on presence in social VR. In: Proceedings of 2017 IEEE symposium on 3D user interfaces (3DUI); 18–19 March 2017; Los Angeles, USA. pp. 233–234.

60. Dou J, Li H, Li X. Problem-oriented industrial designing method on extenics. Procedia Computer Science 2018; 139: 356–363. doi: 10.1016/j.procs.2018.10.279

61. Zhou Y, Zhou H. Research on the quality evaluation of innovation and entrepreneurship education of college students based on extenics. Procedia Computer Science 2022; 199(2): 605–612. doi: 10.1016/j.procs.2022.01.074

62. Sánchez-Prieto JC, Cruz-Benito J, Therón R, et al. Assessed by machines: Development of a TAM-based tool to measure AI-based assessment acceptance among students. International Journal of Interactive Multimedia and Artificial Intelligence 2020; 6(4): 80–86. doi: 10.9781/ijimai.2020.11.009

63. Noh NHM, Raju R, Eri ZD, et al. Extending technology acceptance model (TAM) to measure the students’ acceptance of using digital tools during open and distance learning (ODL). IOP Conference Series: Materials Science and Engineering 2021; 12: 1–14. doi: 10.1088/1757-899X/1176/1/012037

64. Hair JF, Hult GTM, Ringle CM, et al. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE; 2014.

65. Kock N. WarpPLS User Manual: Version 6.0. Laredo; 2019.

66. Hair JF, Black WC, Babin, BJ, et al. Multivariate Data Analysis. Prentice Hall; 2010.

67. Fornell C, Lacker DF. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 1981; 18(1): 39–50. doi: 10.2307/3151312

68. Byrne BM. Structural Equation Modeling with AMOS—Basic Concepts, Applications and Programming, 3rd ed. Routledge; 2016.

69. Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice 2011; 19(2): 139–151. doi: 10.2753/MTP1069-6679190202

70. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum; 1988.

71. Filiz M, Early E, Thurston A, et al. Measuring and improving university students’ statistics self-concept: A systematic review. International Journal of Educational Research Open 2020; 10(1): 1–16. doi: 10.1016/j.ijedro.2020.100020

72. Dai Y, Chai CS, Lin PY, et al. Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability 2020; 12(16): 1–15. doi: 10.3390/su12166597

73. Schleutker E. Seven suggestions for teaching quantitative methods. PS: Political Science & Politics 2022; 55(2): 419–423. doi: 10.1017/S1049096521001426

74. Davies M, Calma A. Fixing holes where the rain gets in: Problem areas in the development of generic skills in business. Journal of International Education in Business 2013; 6(1): 35–50. doi: 10.1108/18363261311314944

75. Tengesdal M, Griffin A. Quantitative and computer skills employers want vs. what the business curriculum can provide. In: Kensinger JW (editor). Signs that Markets are Coming Back. Emerald Group Publishing Limited; 2014. pp. 95–111. doi: 10.1108/S0196-382120140000030008

76. Hijazi R, Zoubeidi T. State of business statistics education in MENA region: A comparative study with best practices. Journal of International Education in Business 2017; 10(1): 68–88. doi: 10.1108/JIEB-07-2016-0017

77. Winstone N, Carless D. Designing Effective Feedback Processes in Higher Education: A Learning-Focused Approach. Routledge; 2019.

78. Algayres MG, Triantafyllou E. Learning analytics in flipped classrooms: A scoping review. The Electronic Journal of E-Learning 2020; 18(5): 397–409. doi: 10.34190/JEL.18.5.003

79. Goel AK, Polepeddi L. Jill Watson: A virtual teaching assistant for online education. Available online: https://smartech.gatech.edu/handle/1853/59104 (accessed on 21 July 2023).

80. Perin D, Lauterbach M. Assessing text-based writing of low-skilled college students. International Journal of Artifcial Intelligence in Education 2018; 28(1): 56–78. doi: 10.1007/s40593-016-0122-z

81. Luckin R. Towards artifcial intelligence-based assessment systems. Nature Human Behaviour 2017; 1(3): 1–3. doi: 10.1038/s41562-016-0028

82. Ross B, Chase AM, Robbie D, et al. Adaptive quizzes to increase motivation, engagement and learning outcomes in a frst year accounting unit. International Journal of Educational Technology in Higher Education 2018; 15(1): 30–45. doi: 10.1186/s41239-018-0113-2

83. Chan R. The Cambridge Analytica whistleblower explains how the firm used Facebook data to sway elections. Business Insider. Available online: https://www.businessinsider.com/cambridge-analytica-whistleblower-christopher-wyliefacebook-data-2019-10 (accessed on 21 July 2023).

84. Crawford K, Calo R. There is a blind spot in AI research. Nature 2016; 538(7625): 311–313. doi: 10.1038/538311a

85. Murphy RF. Artifcial intelligence applications to support K-12 teachers and teaching. Available online: https://www.rand.org/pubs/perspectives/PE315.html (accessed on 21 July 2023).

86. Gierdowski DC, Galanek J. ECAR study of the technology needs of students with disabilities, 2020. Available online: https://er.educause.edu/blogs/2020/6/ecar-study-of-the-technology-needs-of-students-with-disabilities-2020 (accessed on 21 July 2023).

87. Baloğlu M, Deniz ME, Kesici Ş. A descriptive study of individual and cross-cultural differences in statistics anxiety. Learning and Individual Differences 2011; 21(4): 387–391. doi: 10.1016/j.lindif.2011.03.003

88. Iddo G, Ginsburg L. The role of beliefs and attitudes in learning statistics: Towards an assessment framework. Journal of Statistics Education 1994; 2(2): 1–23. doi: 10.1080/10691898.1994.11910471

89. Ncube B, Moroke ND. Students’ perceptions and attitudes towards statistics in South African university: An exploratory factor analysis approach. Journal of Governance and Regulation 2015; 4(3): 231–240. doi: 10.22495/jgr_v4_i3_c2_p5

90. Hirsch LS, O’Donnell AM. Representativeness in statistical reasoning: Identifying and assessing misconceptions. Journal of Statistics Education 2001; 9(2): 1–22. doi: 10.1080/10691898.2001.11910655

91. Onwuegbuzie AJ, Wilson VA. Statistics anxiety: Nature, etiology, antecedents, effects, and treatments—A comprehensive review of the literature. Teaching in Higher Education 2003; 8(2): 195–209. doi: 10.1080/1356251032000052447

92. Chai CS, Liang JC, Tsai CC, et al. Surveying and modelling China high school students’ experience of and preferences for twenty-first-century learning and their academic and knowledge creation efficacy. Educational Studies 2019; 46(6): 658–679. doi: 10.1080/03055698.2019.1627662

93. Chai CS, Lin PY, Jong MSY, et al. Primary school students’ perceptions and behavioral intentions of learning artificial intelligence. Educational Technology & Society 2020; 24(3): 89–101.

94. Zawacki-Richter O, Marín VI, Bond M, et al. Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education 2019; 16: 39–45. doi: 10.1186/s41239-019-0171-0

95. Smith PJ, Murphy KL, Mahoney SE. Towards identifying factors underlying readiness for online learning: An exploratory study. Distance Education 2003; 24: 57–67. doi: 10.1080/01587910303043

96. Komarraju M, Ramsey A, Rinella V. Cognitive and non-cognitive predictors of college readiness and performance: Role of academic discipline. Learning and Individual Differences 2013; 24(5): 103–109. doi: 10.1016/j.lindif.2012.12.007

97. IBM. Majority of generation Z students believe AI will impact their careers—And they feel unprepared. Available online: https://www.fenews.co.uk/skills/majority-of-generation-z-students-believe-ai-will-impact-their-careers-and-they-feel-unprepared/ (accessed on 21 July 2023).

98. Shivers-Blackwell SL, Charles AC. Ready, set, go: Examining student readiness to use ERP technology. Journal of Management Development 2006; 25: 795–805. doi: 10.1108/02621710610684268

99. Ertl B, Luttenberger S, Paechter M. The impact of gender stereotypes on the self-concept of female students in stem subjects with an under-representation of females. Frontiers in Psychology 2017; 8(5): 703–725. doi: 10.3389/fpsyg.2017.00703

100. Lee MH, Chai CS, Hong HY. STEM education in asia pacific: Challenges and development. The Asia-Pacific Education Researcher 2019; 28: 1–4. doi: 10.1007/s40299-018-0424-z


DOI: https://doi.org/10.54517/esp.v8i2.1670
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