Skip to main content Skip to main navigation menu Skip to site footer
Articles
DOI: 10.14254/1795-6889.2025.21-3.7
Submitted: 2024-11-29
Published: 2025-12-30

The impact of artificial intelligence on task performance and decision-making: Empirical evidence on generation Z

University of Warmia and Mazury in Olsztyn, Poland; Brno University of Technology Czechia
Brno University of Technology Czechia; Nicolaus Copernicus University in Torun Poland
Brno University of Technology
Artificial Intelligence AI Higher Education Task Performance Decision-Making Experimental Study

Abstract

This study examines how generative artificial intelligence (AI) reshapes task performance, decision-making, and evaluative judgement in higher education assessments, with a focus on emerging human-AI assemblages among Generation Z university students. A controlled three-stage scenario-based experiment was conducted with the same cohort of students of business and economics, comparing a baseline session (no AI), independent reasoning (no AI), and identical AI-assisted conditions. Participants completed tasks involving situational judgment, quantitative reasoning, and short written responses. Results reveal that AI access increased average performance but markedly compressed score variance and reduced internal reliability, undermining the assessment’s diagnostic capacity to differentiate independent abilities. Qualitative findings indicate that students perceived non-AI conditions as more cognitively effortful and educationally valuable, with AI shifting agency toward tool management and oversight. Together, these results highlight how AI redistributes agency in assessment, raising questions about responsibility and validity in sociotechnical contexts. Based on these insights, the study recommends hybrid assessment designs that separately evaluate independent reasoning and AI-augmented performance, incorporating reflective components to render distributed agency visible and preserve evaluative judgement.

Metrics

Metrics Loading ...

References

  1. Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary educational technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152 DOI: https://doi.org/10.30935/cedtech/13152
  2. Andronie, M., Blažek, R., Iatagan, M., Skypalova, R., Uță, C., Dijmărescu, A., Kovacova, M., Grecu, G., Pârvu, I., Strakova, J., Guni, C., Zabojnik, S., Chiru, C., Sedláčková, A. N., Novák, A., & Dijmărescu, I. (2024). Generative artificial intelligence algorithms in Internet of Things blockchain-based fintech management. Oeconomia Copernicana, 15(4), 1349-1381. https://doi.org/10.24136/oc.3283 DOI: https://doi.org/10.24136/oc.3283
  3. Angammana, J. S. K., & Jayawardena, M. (2022). Influence of artificial intelligence on warehouse performance: The case study of the Colombo area, Sri Lanka. Journal of Sustainable Development of Transport and Logistics, 7(2), 80–110. https://doi.org/10.14254/jsdtl.2022.7-2.6 DOI: https://doi.org/10.14254/jsdtl.2022.7-2.6
  4. Athaya, H., Nadir, R. D. A., Indra Sensuse, D., Kautsarina, K., & Suryono, R. R. (2021, September). Moodle implementation for e-learning: A systematic review. In Proceedings of the 6th International Conference on Sustainable Information Engineering and Technology (pp. 106–112). DOI: https://doi.org/10.1145/3479645.3479646
  5. Baker, R. S. J. d., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (2nd ed., pp. 253–274). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.015 DOI: https://doi.org/10.1017/CBO9781139519526.016
  6. Balcerzak, A. P., & Valaskova, K. (2024). Artificial intelligence: Financial management under pressure of transformative technology. Equilibrium. Quarterly Journal of Economics and Economic Policy, 19(4), 1127-1137. https://doi.org/10.24136/eq.3394 DOI: https://doi.org/10.24136/eq.3394
  7. Bardach, L., Rushby, J. V., Kim, L. E., & Klassen, R. M. (2021). Using video-and text-based situational judgement tests for teacher selection: a quasi-experiment exploring the relations between test format, subgroup differences, and applicant reactions. European Journal of Work and Organizational Psychology, 30(2), 251–264. https://doi.org/10.1080/1359432X.2020.1736619 DOI: https://doi.org/10.1080/1359432X.2020.1736619
  8. Barlybayev, A., Razakhova, B., Sharipbay, A., Nazyrova, A., Tursynova, N., Zulkhazhav, A., & Yelibayeva, G. (2025). Сomparative analysis of grading models using fuzzy logic to enhance fairness and consistency in student performance evaluation. Cogent education, 12(1). https://doi.org/10.1080/2331186X.2025.2481008. DOI: https://doi.org/10.1080/2331186X.2025.2481008
  9. Barr, N., Pennycook, G., Stolz, J. A., & Fugelsang, J. A. (2015). The brain in your pocket: Evidence that smartphones are used to supplant thinking. Computers in Human Behavior, 48, 473–480. https://doi.org/10.1016/j.chb.2015.02.029 DOI: https://doi.org/10.1016/j.chb.2015.02.029
  10. Batista, J., Mesquita, A., & Carnaz, G. (2024). Generative AI and Higher Education: Trends, Challenges, and Future Directions from a Systematic Literature Review. Information (Basel), 15(11), 676. https://doi.org/10.3390/info15110676 DOI: https://doi.org/10.3390/info15110676
  11. Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. Assessment and evaluation in higher education, 49(6), 893-905. https://doi.org/10.1080/02602938.2024.2335321 DOI: https://doi.org/10.1080/02602938.2024.2335321
  12. Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4-41. https://doi.org/10.1186/s41239-023-00436-z DOI: https://doi.org/10.1186/s41239-023-00436-z
  13. Bonfield, C. A., Salter, M., Longmuir, A., Benson, M., & Adachi, C. (2020). Transformation or evolution?: Education 4.0, teaching and learning in the digital age. Higher education pedagogies, 5(1), 223–246. https://doi.org/10.1080/23752696.2020.1816847 DOI: https://doi.org/10.1080/23752696.2020.1816847
  14. Castillo-Martínez, I. M., Flores-Bueno, D., Gómez-Puente, S. M., & Vite-León, V. O. (2024). AI in higher education: A systematic literature review. In Frontiers in Education (Vol. 9, p. 1391485). Frontiers Media SA. https://doi.org/10.3389/feduc.2024.1391485 DOI: https://doi.org/10.3389/feduc.2024.1391485
  15. Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510 DOI: https://doi.org/10.1109/ACCESS.2020.2988510
  16. Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and education. Artificial intelligence, 6, 100197. https://doi.org/10.1016/j.caeai.2023.100197 DOI: https://doi.org/10.1016/j.caeai.2023.100197
  17. Clark, R. (2009). Accelerating expertise with scenario-based learning. Learning Blueprint. Merrifield, VA: American Society for Teaching and Development, 10.
  18. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555 DOI: https://doi.org/10.1007/BF02310555
  19. Davis, K. A., Grote, D., Mahmoudi, H., Perry, L., Ghaffarzadegan, N., Grohs, J., Hosseinichimeh, N., Knight, D. B., & Triantis, K. (2023). Comparing Self-Report Assessments and Scenario-Based Assessments of Systems Thinking Competence. Journal of science education and technology, 32(6), 793-813. https://doi.org/10.1007/s10956-023-10027-2 DOI: https://doi.org/10.1007/s10956-023-10027-2
  20. Deci, E. L., & Ryan, R. M. (2000). The" what" and" why" of goal pursuits: Human needs and the self-determination of behavior. Psychological inquiry, 11(4), 227-268. https://doi.org/10.1207/S15327965PLI1104_01 DOI: https://doi.org/10.1207/S15327965PLI1104_01
  21. Durica, M., Frnda, J., & Svabova, L. (2023). Artificial neural network and decision tree-based modelling of non-prosperity of companies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(4), 1105-1131. https://doi.org/10.24136/eq.2023.035 DOI: https://doi.org/10.24136/eq.2023.035
  22. Dzindolet, M. T., Peterson, S. A., Pomranky, R. A., Pierce, L. G., & Beck, H. P. (2003). The role of trust in automation reliance. International journal of human-computer studies, 58(6), 697-718. https://doi.org/10.1016/S1071-5819(03)00038-7 DOI: https://doi.org/10.1016/S1071-5819(03)00038-7
  23. Elshall, A. S., & Badir, A. (2025, June). Balancing AI-assisted learning and traditional assessment: the FACT assessment in environmental data science education. In Frontiers in Education (Vol. 10, p. 1596462). Frontiers Media SA. DOI: https://doi.org/10.3389/feduc.2025.1596462
  24. Embretson, S. E. (1983). Construct validity: Construct representation versus nomothetic span. Psychological bulletin, 93(1), 179-197. DOI: https://doi.org/10.1037//0033-2909.93.1.179
  25. Frankford, E., Sauerwein, C., Bassner, P., Krusche, S., & Breu, R. (2024, April). AI-tutoring in software engineering education. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training (pp. 309-319). DOI: https://doi.org/10.1145/3639474.3640061
  26. Gavira Durón, N., & Jiménez-Preciado, A. L. (2025). Exploring the role of AI in higher education: a natural language processing analysis of emerging trends and discourses. TQM journal. https://doi.org/10.1108/TQM-10-2024-0376. DOI: https://doi.org/10.1108/TQM-10-2024-0376
  27. Ghosh, S., Brooks, B., Ranmuthugala, D., & Bowles, M. (2020). Authentic Versus Traditional Assessment: An Empirical Study Investigating the Difference in Seafarer Students' Academic Achievement. Journal of navigation, 73(4), 797-812. https://doi.org/10.1017/S0373463319000894 DOI: https://doi.org/10.1017/S0373463319000894
  28. Ghosh, T., & Francia, G. (2021). Assessing Competencies Using Scenario-Based Learning in Cybersecurity. Journal of cybersecurity and privacy, 1(4), 539-552. https://doi.org/10.3390/jcp1040027 DOI: https://doi.org/10.3390/jcp1040027
  29. Gratchev, I. (2023). Replacing Exams with Project-Based Assessment: Analysis of Students’ Performance and Experience. Education sciences, 13(4), 408. https://doi.org/10.3390/educsci13040408 DOI: https://doi.org/10.3390/educsci13040408
  30. Han, B., Nawaz, S., Buchanan, G., & McKay, D. (2025). Students’ perceptions: exploring the interplay of ethical and pedagogical impacts for adopting AI in higher education. International Journal of Artificial Intelligence in Education, 1-26. https://doi.org/10.1007/s40593-024-00456-4 DOI: https://doi.org/10.1007/s40593-024-00456-4
  31. Haynes, S. R., Spence, L., & Lenze, L. (2009, October). Scenario-based assessment of learning experiences. In 2009 39th IEEE Frontiers in Education Conference (pp. 1–8). IEEE. https://doi.org/10.1109/FIE.2009.5350730 DOI: https://doi.org/10.1109/FIE.2009.5350730
  32. Huang, X. (2021). Aims for cultivating students’ key competencies based on artificial intelligence education in China. Education and information technologies, 26(5), 5127–5147. https://doi.org/10.1007/s10639-021-10530-2 DOI: https://doi.org/10.1007/s10639-021-10530-2
  33. Ibieta, A., Aguilera, S., Constanza-Medina, M., Ignacia-Sepúlveda, M., & Castellanos-Alvarenga, L. M. (2024, November). Academic Use of Artificial Intelligence Tools by University Students. In International Conference on New Media Pedagogy (pp. 343-353). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-95627-0_23 DOI: https://doi.org/10.1007/978-3-031-95627-0_23
  34. Iverson, K., & Colky, D. (2004). Scenario‐based E‐learning design. Performance Improvement, 43(1), 16-22. https://doi.org/10.1002/pfi.4140430105 DOI: https://doi.org/10.1002/pfi.4140430105
  35. Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of educational measurement, 50(1), 1-73. https://doi.org/10.1111/jedm.12000 DOI: https://doi.org/10.1111/jedm.12000
  36. Kirsh, D. (1995). The intelligent use of space. Artificial intelligence, 73(1-2), 31-68. https://doi.org/10.1016/0004-3702(94)00017-U DOI: https://doi.org/10.1016/0004-3702(94)00017-U
  37. Kliestik, T., Kral, P., Bugaj, M., & Durana , P. (2024). Generative artificial intelligence of things systems, multisensory immersive extended reality technologies, and algorithmic big data simulation and modelling tools in digital twin industrial metaverse. Equilibrium. Quarterly Journal of Economics and Economic Policy, 19(2), 429-461. https://doi.org/10.24136/eq.3108 DOI: https://doi.org/10.24136/eq.3108
  38. Kliestik, T., Nica, E., Durana, P., & Popescu, G. H. (2023). Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Industrial Internet of Things. Oeconomia Copernicana, 14(4), 1097-1138. https://doi.org/10.24136/oc.2023.033 DOI: https://doi.org/10.24136/oc.2023.033
  39. Krugman, P. R., Obstfeld, M., & Melitz, M. J. (2015). International economics: theory and policy (Tenth edition, global edition). Pearson Education.
  40. Koh, K. (2017). Authentic Assessment. Oxford Research Encyclopedia of Education. Retrieved 11 Aug. 2025, from https://oxfordre.com/education/view/10.1093/acrefore/9780190264093.001.0001/acrefore-9780190264093-e-22 DOI: https://doi.org/10.1093/acrefore/9780190264093.013.22
  41. Kozová, K., Grenčíková, A., & Habánik, J. (2024). Building a sustainable future: Gender, education & workforce needs of Gen Z. Economics and Sociology, 17(2), 209-223. doi:10.14254/2071-789X.2024/17-2/10 DOI: https://doi.org/10.14254/2071-789X.2024/17-2/10
  42. Lazaroiu, G., Gedeon, T., Valaskova, K., Vrbka, J., Šuleř, P., Zvarikova, K., Kramarova, K., Rowland, Z., Stehel, V., Gajanova, L., Horák, J., Grupac, M., Caha, Z., Blazek, R., Kovalova, E., & Nagy, M. (2024). Cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative artificial intelligence and cyber–physical manufacturing systems in the immersive industrial metaverse. Equilibrium. Quarterly Journal of Economics and Economic Policy, 19(3), 719-748. https://doi.org/10.24136/eq.3131 DOI: https://doi.org/10.24136/eq.3131
  43. Lazaroiu, G., & Rogalska, E. (2024). Generative artificial intelligence marketing, algorithmic predictive modeling, and customer behavior analytics in the multisensory extended reality metaverse. Oeconomia Copernicana, 15(3), 825-835. https://doi.org/10.24136/oc.3190 DOI: https://doi.org/10.24136/oc.3190
  44. Lee, H. P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025, April). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. In Proceedings of the 2025 CHI conference on human factors in computing systems (pp. 1-22) DOI: https://doi.org/10.1145/3706598.3713778
  45. Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
  46. Melisa, R., Ashadi, A., Triastuti, A., Hidayati, S., Salido, A., Luansi Ero, P. E., Marlini, C., Zefrin, Z., & Al Fuad, Z. (2025). Critical Thinking in the Age of AI: A Systematic Review of AI's Effects on Higher Education. Education process: international journal, 14(1). https://doi.org/10.22521/edupij.2025.14.31 DOI: https://doi.org/10.22521/edupij.2025.14.31
  47. Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons' responses and performances as scientific inquiry into score meaning. American psychologist, 50(9), 741-749. https://doi.org/10.1037/0003-066X.50.9.741 DOI: https://doi.org/10.1037//0003-066X.50.9.741
  48. Mishchuk, H., Samoliuk, N., Krol, V., & Rącka, I. (2025). Digital competences of university graduates: Implications for career success. Human Technology, 21(2), 274–292. https://doi.org/10.14254/1795-6889.2025.21-2.2
  49. Mislevy, R. J., Almond, R. G., & Lukas, J. F. (2003). A brief introduction to evidence‐centered design. ETS Research Report Series, 2003(1), i-29. https://doi.org/10.1002/j.2333-8504.2003.tb01908.x DOI: https://doi.org/10.1002/j.2333-8504.2003.tb01908.x
  50. Moravec, V., Hynek, N., Gavurova, B., & Kubak, M. (2024). Everyday artificial intelligence unveiled: Societal awareness of technological transformation. Oeconomia Copernicana, 15(2), 367-406. https://doi.org/10.24136/oc.2961 DOI: https://doi.org/10.24136/oc.2961
  51. O'Dea, X. (2024). Generative AI: is it a paradigm shift for higher education? Studies in higher education (Dorchester-on-Thames), 49(5), 811-816. https://doi.org/10.1080/03075079.2024.2332944 DOI: https://doi.org/10.1080/03075079.2024.2332944
  52. Paas, F., & Van Merrienboer, J. J. (2020). Cognitive-load theory: Methods to manage working memory load in the learning of complex tasks. Current Directions in Psychological Science, 29(4), 394-398. https://doi.org/10.1177/0963721420922183 DOI: https://doi.org/10.1177/0963721420922183
  53. Perkins, D. N. (1993). Person-plus: A distributed view of thinking and learning. In G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 88-110). Cambridge University Press.
  54. Rensfeldt, A. B., & Rahm, L. (2023). Automating teacher work? A history of the politics of automation and artificial intelligence in education. Postdigital Science and Education, 5(1), 25–43. https://doi.org/10.1007/s42438-022-00344-x DOI: https://doi.org/10.1007/s42438-022-00344-x
  55. Salomon, G., Perkins, D. N., & Globerson, T. (1991). Partners in cognition: Extending human intelligence with intelligent technologies. Educational researcher, 20(3), 2-9. https://doi.org/10.3102/0013189X020003002 DOI: https://doi.org/10.3102/0013189X020003002
  56. Sarker, P. K. (2022). Macroeconomic effects of artificial intelligence on emerging economies: Insights from Bangladesh. Economics, Management and Sustainability, 7(1), 59–69. https://doi.org/10.14254/jems.2022.7-1.5 DOI: https://doi.org/10.14254/jems.2022.7-1.5
  57. Southworth, J., Migliaccio, K., Glover, J., Glover, J. ’net, Reed, D., Mccarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and education. Artificial intelligence, 4, 100127. https://doi.org/10.1016/j.caeai.2023.100127 DOI: https://doi.org/10.1016/j.caeai.2023.100127
  58. Temerbulatova, Zh., Zhidebekkyzy, A., Sagiyeva, R., & Ludwiczak, A. (2025). Artificial intelligence as a driver of innovation and patent activity: An empirical analysis of cross-country data. Economics and Sociology, 18(3), 184-201. doi:10.14254/2071-789X.2025/18-3/11 DOI: https://doi.org/10.14254/2071-789X.2025/18-3/11
  59. van Berkum, M., Diederen, J., Buijsse, C. A. P., Boom, R. M., & den Brok, P. J. (2023). Competencies in higher education: identifying and selecting important competencies based on graduates & professionals in food technology. European Journal of Engineering Education, 49(3), 434–453. https://doi.org/10.1080/03043797.2023.2245768 DOI: https://doi.org/10.1080/03043797.2023.2245768
  60. Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert systems with applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167 DOI: https://doi.org/10.1016/j.eswa.2024.124167
  61. Whetzel, D. L., & McDaniel, M. A. (2009). Situational judgment tests: An overview of current research. Human Resource Management Review, 19(3), 188-202. https://doi.org/10.1016/j.hrmr.2009.03.007 DOI: https://doi.org/10.1016/j.hrmr.2009.03.007
  62. Wojciechowski, A., & Korjonen-Kuusipuro, K. (2023). How artificial intelligence affects education?. Human Technology, 19(3), 302–306. https://doi.org/10.14254/1795-6889.2023.19-3.0 DOI: https://doi.org/10.14254/1795-6889.2023.19-3.0
  63. Xia, Q., Weng, X., Ouyang, F. et al. A scoping review on how generative artificial intelligence transforms assessment in higher education. Int J Educ Technol High Educ 21, 40 (2024). https://doi.org/10.1186/s41239-024-00468-z DOI: https://doi.org/10.1186/s41239-024-00468-z
  64. Zafari, M., Bazargani, J. S., Sadeghi-niaraki, A., & Choi, S. -Mi. (2022). Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review. IEEE access, 10, 61905–61921. https://doi.org/10.1109/ACCESS.2022.3179356. DOI: https://doi.org/10.1109/ACCESS.2022.3179356

How to Cite

Balcerzak, A. P., Zinecker, M., & Mičánek, J. (2025). The impact of artificial intelligence on task performance and decision-making: Empirical evidence on generation Z. Human Technology, 21(3), 620–639. https://doi.org/10.14254/1795-6889.2025.21-3.7