What drives economics students to use generative artificial intelligence?

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The increasing integration of Artificial Intelligence (AI) into education requires studying the motives for its use among students. This study aims to identify the key motivations for economics students to use AI and compare these motivations by grade level and gender. The study examines satisfaction with the use of AI and analyzes the number of AI tools used.

An anonymous empirical study was conducted among 264 students from the Faculty of Economics at Taras Shevchenko National University of Kyiv, Ukraine. Data analysis included descriptive statistical methods, non-parametric statistical methods, and exploratory factor analysis.

The study found that students’ main motivations for using AI are the automation of routine tasks (34.2%) and the need to save time (21.5%), while 18.7% use AI to compensate for lack of experience. Among Bachelor’s students, motivations such as automating routine tasks and saving time increased from 53% to 58% over the course of their studies, while lack of experience decreased from 22% to 15%. In contrast, Master’s students showed a decrease in routine automation (from 36% to 28%) but an increase in the need to compensate for lack of experience (from 15% to 28%) and to save time (from 18% to 25%). In terms of gender, men are more likely to use AI for learning and personal development, while women are slightly more likely to use AI for work. More than 38% of respondents say they need to use at least 2 AIs to achieve their goals.

Acknowledgment
This publication is based upon work from 24-PKVV-UM-002, ‘Strengthening the Resilience of Universities: Czech-Ukrainian Partnership for Digital Education, Research Cooperation, and Diversity Management,’ supported by the Czech Development Agency and the Ministry of Foreign Affairs under the initiative ‘Capacity Building of Public Universities in Ukraine 2024.’

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    • Figure 1. Quantity of AI tools used for different motivations
    • Figure 2. Motivation to use AI among Bachelor and Master students
    • Figure 3. Boxplot for AI usage by area
    • Figure 4. Box-plot of AI usage areas by gender
    • Figure 5. Factor loadings: Factor 1 vs Factor 2
    • Figure 6. Factor loadings: Factor 1 vs Factor 2 by gender
    • Table 1. Sociodemographic profile of the sample (n = 264)
    • Table 2. Students’ motivation in using AI, %
    • Table 3. Students’ average satisfaction with AI usage based on the number of AI tools (Heatmap)
    • Table 4. Descriptive statistics for AI usage by area
    • Table 5. Mean and median for AI usage by area across gender
    • Table 6. Factor analysis results
    • Table 7. Factor loadings
    • Table 8. Factor loadings by gender
    • Conceptualization
      Mariia Balytska, Martina Rašticová, Nataliia Versal, Nataliia Prykaziuk, Nataliia Tkalenko
    • Data curation
      Mariia Balytska, Ihor Honchar, Nataliia Prykaziuk, Nataliia Tkalenko
    • Methodology
      Mariia Balytska, Martina Rašticová, Nataliia Versal, Ihor Honchar, Nataliia Prykaziuk
    • Supervision
      Mariia Balytska, Nataliia Versal
    • Writing – original draft
      Mariia Balytska, Martina Rašticová, Nataliia Versal, Ihor Honchar, Nataliia Prykaziuk, Nataliia Tkalenko
    • Writing – review & editing
      Mariia Balytska, Nataliia Versal, Nataliia Prykaziuk, Nataliia Tkalenko
    • Project administration
      Martina Rašticová, Nataliia Tkalenko
    • Formal Analysis
      Ihor Honchar
    • Validation
      Ihor Honchar
    • Visualization
      Ihor Honchar
    • Software
      Nataliia Prykaziuk
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Knowledge and Performance Management
Knowledge and Performance Management