CHATGPT as a knowledge-support tool: Perceptions of its professional use among working students in Poland

  • 8 Views
  • 0 Downloads

Creative Commons License DMCA.com Protection Status
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.

 
view full abstract hide full abstract
    • 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
    • Conceptualization
      Kacper Sieciński, Marian Oliński
    • Methodology
      Kacper Sieciński, Marian Oliński
    • Software
      Kacper Sieciński
    • Formal Analysis
      Kacper Sieciński
    • Data curation
      Kacper Sieciński
    • Validation
      Kacper Sieciński, Marian Oliński
    • Writing – original draft
      Kacper Sieciński
    • Visualization
      Kacper Sieciński
    • Project administration
      Kacper Sieciński, Marian Oliński
    • Investigation
      Marian Oliński
    • Resources
      Marian Oliński
    • Writing – review & editing
      Marian Oliński
    • Supervision
      Marian Oliński