Trust in neural Networks Questionnaire: A psychometric assessment
DOI:
https://doi.org/10.33910/1992-6464-2026-219-123-133Keywords:
trust, artificial intelligence, neural networks, questionnaire adaptation, psychometric properties, emotional componentAbstract
Introduction. Artificial intelligence (AI), particularly generative neural networks, is rapidly entering multiple domains such as economics, education, and medicine. User trust is essential for the effective and sustainable use of these technologies. Trust determines not only the acceptance of AI-generated responses but also the willingness to rely on them in practical contexts. Insufficient trust may limit AI use despite high system accuracy, while excessive trust risks uncritical reliance on AI recommendations. Although research in this field is growing, assessing trust in AI remains a challenge. Existing questionnaires primarily focus on the cognitive aspects of trust, often neglecting the emotional dimension. This article presents an adaptation of A. Yu. Akimova’s Trust in Technology Questionnaire for neural networks and assesses its psychometric properties.
Materials and Methods. The study involved 218 university and high school students aged 15 to 37. The adaptation involved refining the wording of items to reflect specific features of interaction with neural networks, while preserving the original structure of the questionnaire. Psychometric testing included assessing item difficulty and discrimination, internal consistency (Cronbach’s α), test–retest reliability, and convergent and divergent validity.
Results. The adapted Trust in Neural Networks questionnaire demonstrated high internal consistency (α = 0.906), satisfactory item difficulty and discrimination, and stable test–retest reliability (r = 0.879). The findings also confirm the significance of the emotional component in the structure of trust, supporting its inclusion in the assessment model.
Conclusions. The adapted questionnaire is a reliable and valid instrument for measuring trust in neural networks. It can be applied in educational and professional research, as well as for intergroup comparisons. Future research should focus on expanding the sample, refining the factor structure, and examining the role of emotional experience in shaping trust in AI.
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