CHATGPT as a knowledge-support tool: Perceptions of its professional use among working students in Poland
-
DOIhttp://dx.doi.org/10.21511/kpm.10(3).2026.06
-
Article InfoVolume 10 2026, Issue #3, pp. 80-99
- 8 Views
-
0 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Type of the article: Research Article
Generative artificial intelligence increasingly supports knowledge work in education and professional settings, yet its usefulness remains underexplored among people who combine study and employment. This study examines how working students perceive ChatGPT’s usefulness in selected professional applications. The study uses data from a quantitative online survey of 419 working students from five age groups conducted in Poland between April and June 2024. The questionnaire measured current and expected usefulness of ChatGPT in selected professional roles, motives for use, and the most frequently used version of the tool. The analysis used Kruskal-Wallis tests, post-hoc multiple comparisons, Cochran’s Q test, planned McNemar comparisons, Pearson’s chi-square tests, and effect-size measures. Most respondents used the Free version (n = 329), while smaller groups used Premium (n = 67) or Other variants (n = 23). Information retrieval was the most frequently reported motive for using ChatGPT (n = 298; 71.1%), followed by assistance in learning new skills (n = 249; 59.4%). Age-related differences appeared in the current assessment of ChatGPT as a customer relations specialist (p = 0.0018) and secretary (p = 0.0083), with respondents aged 40-50 assigning higher ratings in these roles. Younger respondents reported using ChatGPT for skill development more often than older respondents (p < 0.001). ChatGPT version also differentiated use: Premium users more often reported routine document preparation, code generation, and data analysis, while Other-version users more often reported code generation and data analysis. The findings show that working students perceive ChatGPT primarily as a knowledge-support tool rather than a substitute for professional roles.
- Keywords
-
JEL Classification (Paper profile tab)M15, O33, J24, I23
-
References28
-
Tables15
-
Figures3
-
- Figure 1. Data preparation and hypothesis testing procedure
- Figure 2. Use of ChatGPT for learning new skills by age group (%)
- Figure 3. Motives for using ChatGPT by ChatGPT version (%)
-
- Table 1. Characteristics of the research sample by age group and ChatGPT version
- Table 2. Characteristics of the research sample by sector of recent professional activity and ChatGPT version
- Table 3. Kruskal-Wallis test for the current assessment of ChatGPT’s usefulness in selected professional roles by age group
- Table 4. Post-hoc multiple comparisons for ChatGPT as a customer relations specialist (current assessment)
- Table 5. Post-hoc multiple comparisons for ChatGPT as a secretary (current assessment)
- Table 6. Descriptive statistics for roles with statistically significant age-related differences (current assessment)
- Table 7. Kruskal-Wallis test for the predicted future assessment of ChatGPT’s usefulness in selected professional roles by age group
- Table 8. Post-hoc multiple comparisons for ChatGPT as a customer relations specialist (predicted future assessment)
- Table 9. Descriptive statistics for ChatGPT as a customer relations specialist (predicted future assessment)
- Table 10. Frequency of motives for using ChatGPT and Cochran’s Q test results
- Table 11. Planned McNemar comparisons for motives for using ChatGPT
- Table 12. Pearson’s chi-square test for using ChatGPT to learn new skills by age group
- Table 13. Pearson’s chi-square tests for the association between ChatGPT version and motives for using ChatGPT
- Table 14. Pairwise comparisons of ChatGPT-use motives by ChatGPT version
- Table 15. Summary of hypothesis testing
-
- Al Naqbi, H. A., Bahroun, Z., & Ahmed, V. (2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability, 16(3), 1166.
- Al-Abdullatif, A. M. (2024). Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust. Education Sciences, 14(11), 1209.
- Belchior-Rocha, H., Casquilho-Martins, I., & Simões, E. (2022). Transversal competencies for employability: From higher education to the labour market. Education Sciences, 12(4), 255.
- Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Cooke, F. L., Decker, S., DeNisi, A., Dey, P. K., Guest, D., Knoblich, A. J., Malik, A., Paauwe, J., Papagiannidis, S., Patel, C., Pereira, V., Ren, S., . . . Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606-659.
- Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10, 60.
- Curto-Reverte, A., Peguera-Carré, M. C., Cobos-Rius, H., & Vidal-Marti, C. (2025). The role of work-integrated learning in the European Higher Education Area: A systematic review. Review of Education, 13, e70114.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
- Jackson, D. (2014). Employability skill development in work-integrated learning: Barriers and best practice. Studies in Higher Education, 40(2), 350-367.
- Jackson, D. (2016). Developing pre-professional identity in undergraduates through work-integrated learning. Higher Education, 74(5), 833-853.
- Jackson, D., & Rowe, A. (2022). Impact of work-integrated learning and co-curricular activities on graduate labour force outcomes. Studies in Higher Education, 48(3), 490-506.
- Kaczorowska-Spychalska, D., Kotula, N., Mazurek, G., & Sułkowski, Ł. (2024). Generative AI as source of change of knowledge management paradigm. Human Technology, 20(1), 131-154.
- Kanont, K., Pingmuang, P., Simasathien, T., Wisnuwong, S., Wiwatsiripong, B., Poonpirome, K., Songkram, N., & Khlaisang, J. (2024). Generative-AI, a learning assistant? Factors influencing Higher-Ed students’ technology acceptance. The Electronic Journal of e-Learning, 22(6), 18-33.
- Kocsis, Z., & Pusztai, G. (2025). The role of higher education through the eyes of Hungarian undergraduate students and graduates: a qualitative exploratory study. International Journal for Research in Vocational Education and Training, 12(1), 48-75.
- Korzyński, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., Wach, K., & Ziemba, E. (2023). Generative artificial intelligence as a new context for management theories: analysis of ChatGPT. Central European Management Journal, 31(1), 3-13.
- Nguyen, K. V. (2025). The use of Generative AI tools in higher education: ethical and pedagogical principles. Journal of Academic Ethics, 23(3), 1435-1455.
- Pang, Q., Zhang, M., Yuen, K. F., & Fang, M. (2024). When the winds of change blow: an empirical investigation of ChatGPT’s usage behavior. Technology Analysis and Strategic Management, 37(12), 3113-3127.
- Retkowsky, J., Hafermalz, E., & Huysman, M. (2024). Managing a ChatGPT-empowered workforce: Understanding its affordances and side effects. Business Horizons, 67(5), 511-523.
- Ritala, P., Ruokonen, M., & Ramaul, L. (2023). Transforming boundaries: how does ChatGPT change knowledge work? Journal of Business Strategy, 45(3), 214-220.
- Sallam, M., Elsayed, W., Al-Shorbagy, M., Barakat, M., Khatib, S. E., Ghach, W., Alwan, N., Hallit, S., & Malaeb, D. (2024). ChatGPT usage and attitudes are driven by perceptions of usefulness, ease of use, risks, and psycho-social impact: a study among university students in the UAE. Frontiers in Education, 9, 1414758.
- Şimşek, A. S., Cengiz, G. Ş. T., & Bal, M. (2025). Extending the TAM framework: Exploring learning motivation and agility in educational adoption of generative AI. Education and Information Technologies, 30(15), 20913-20942.
- Skjuve, M., Brandtzaeg, P. B., & Følstad, A. (2024). Why do people use ChatGPT? Exploring user motivations for generative conversational AI. First Monday, 29(1).
- Suárez, D., & García-Mariñoso, B. (2025). On the verge of a digital divide in the use of generative AI? Telecommunications Policy, 49(7), 102997.
- Succi, C., & Canovi, M. (2019). Soft skills to enhance graduate employability: comparing students and employers’ perceptions. Studies in Higher Education, 45(9), 1834-1847.
- Tang, F., Li, B., Yao, F., Zhang, Z., & Zhu, R. (2026). Generative AI usage and improvisation capability: The mediating role of technological affordances and the moderating role of task uncertainty. Technovation, 153, 103535.
- Van der Baan, N., Nuis, W., Beausaert, S., Gijselaers, W., & Gast, I. (2024). Developing employability competences through career coaching in higher education: supporting students’ learning process. Studies in Higher Education, 49(12), 2455-2474.
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
- Zhai, X. (2024). Transforming teachers’ roles and agencies in the era of generative AI: perceptions, acceptance, knowledge, and practices. Journal of Science Education and Technology, 34(6), 1323-1333.
- Zhou, T., & Li, S. (2024). Understanding user switch of information seeking: From search engines to generative AI. Journal of Librarianship and Information Science, 58(1).


