Bulletin of National Defence University of Ukraine

  • Received 31.03.2026,
  • Revised 25.07.2026,
  • Accepted 25.08.2026
  • Published 31.08.2026
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Volume 21, No. 4, 2026
  • cognitive style; intuitive thinking; rational thinking; locus of control; internality; trust in automated systems; OSINT
  • https://doi.org/10.33099/2617-6858-26-21-4-69-76
  • Pages 69-76

The active integration of artificial intelligence systems into information and analytical activities foregrounds the problem of investigating the psychological factors that determine the specificities of analysts’ interaction with automated systems and their susceptibility to cognitive biases in professional decision-making. The aim of this article was to examine the interrelationship between cognitive style, locus of control, and trust in artificial intelligence systems as factors contributing to the formation of intelligence pitfalls in the professional activity of intelligence analysts. The study combined a theoretical analysis of contemporary academic literature with an empirical investigation of the specificities of interaction with automated systems among 50 practising OSINT and information analysts. The Rational-Experiential Inventory by S. Epstein was employed to assess cognitive style, the level of trust in automated systems was determined using the Trust in Automation Questionnaire by M. Körber, and the locus of control was investigated via J. Rotter’s methodology. Analysis of the obtained data revealed the presence of correlations between the respondents’ thinking peculiarities, their level of subjective control, and their perception of algorithmic systems. Respondents with a predominance of an intuitive mode of information processing more frequently demonstrated a willingness to rely on automated recommendations and assessed the functional capabilities of AI systems more positively. In contrast, participants with a more pronounced analytical thinking style exhibited a tendency towards more thorough verification of algorithmic decisions and a lower level of acceptance without additional analysis. It was also established that a higher level of internal control is accompanied by a more positive perception of the reliability and competence of automated systems. The obtained results confirm the significance of individual psychological characteristics in shaping the specificities of human interaction with artificial intelligence technologies in professional activity. The research findings can be utilised in developing psychological training programmes for analysts and in fostering skills for critical and reflexive interaction with artificial intelligence systems

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