Beyond Automation: Understanding AI Adoption, Perception, and Ethics Among Management Students in Silesia – Poland

Abstract

This study explores integrating artificial intelligence (AI) tools in academic workflows among management faculty at universities in Poland’s Silesia region. Using a mixed-methods approach—combining quantitative surveys (n = 352) and qualitative interviews (n = 15)—the research examines how demographic factors such as age, academic rank, and prior technical experience influence the adoption, perceived benefits, and ethical concerns surrounding AI in scholarly work. Findings reveal substantial demographic disparities in AI usage. While 68% of participants report using AI tools for academic tasks, adoption is higher among younger (74% for ages 18–22) and male faculty (75.7%) compared to older (58% for ages 23–28) and female faculty (60.7%). Higher academic rank and technical proficiency also correlate with more advanced use of AI in research. Master’s-level faculty perceive more significant benefits from AI (β = 0.31, p < 0.01) than Bachelor’s-level peers. Concerns persist despite these benefits, such as improved efficiency in literature synthesis, data analysis, and manuscript preparation. Many respondents express ethical apprehensions regarding intellectual integrity, data privacy, and algorithmic bias. These are especially of concern to female academics and those utilizing qualitative methods. Institutional support is the most significant motivator of using AI responsibly. Participants cite the lack of ethical guidance, training, and policy within discipline fields as the hindrances. The study calls for explicit citation standards, targeted AI literacy programs, and comprehensive institutional frameworks to ensure the responsible integration of AI in academic settings. Three main hypotheses guide this research: younger students are more likely to use AI tools, master’s-level students perceive higher utility in AI, and better institutional support predicts more responsible AI use. These hypotheses are tested quantitatively and further illuminated through qualitative data, contributing to academic theory and practical policy-making for post-industrial regions like Silesia.

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