20.500.12556/DKUM-100103 Unjustified trust and satisfaction in explainable artificial intelligence the illusion of transparency persists across user expertise, even in objectively wrong outcomes Explainable artificial intelligence (XAI) is promoted as a means to enhance user trust, understanding, and transparency in high-risk domains such as education. Under regulatory frameworks like the European Union Artificial Intelligence (AI) Act, such transparency is legally required, yet it remains unclear whether explanations truly foster understanding or merely create an illusion of it. This study examined whether explanations accompanying AI-driven decisions affect users’ trust, satisfaction, and understanding when outcomes are objectively unjust. Ninety-six university students (63 non-experts and 33 AI experts) participated in a controlled experiment in which their exam grades were presented as outputs of an AI system with SHAP-like explanations. In reality, grading was performed by a professor, and half of the participants received artificially lowered, unjust grades paired with plausible explanations. Participants were surveyed on their trust, satisfaction, and objective understanding. Participants reported high trust and satisfaction regardless of whether grades were fair or unjust, with no statistically significant differences between experimental and control groups. Objective understanding was low, especially among non-experts, and unrelated to trust or satisfaction. Non-experts overestimated their self-reported AI literacy and performed worse on comprehension tasks when receiving unfair outcomes. Participants maintained high levels of trust and satisfaction even when outcomes were objectively unjust, indicating that such explanations can create an illusion of understanding rather than improve transparency. Expertise offered limited protection against this effect. Results highlight the need for stricter evaluation standards and caution against assuming that explanation-based transparency inherently safeguards users in high-stakes AI applications. explainable artificial intelligence trust satisfaction understanding artificial intelligence act razložljiva umetna inteligenca zaupanje zadovoljstvo razumevanje true false true Elsevier Ltd. Angleški jezik Slovenski jezik https://www.sciencedirect.com/journal/engineering-applications-of-artificial-intelligence © 2026 The Authors Članek v reviji 2026-09-04 08:49:15 2026-09-04 08:49:15 2026-09-05 03:09:30 0000-00-00 00:00:00 2026 0 0 20 str. part 2, [article no.] 115894 Vol. 182 15 Oct. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2026-01-31 2026-08-01 2026-08-20 004.8 289250307 10.1016/j.engappai.2026.115894 1873-6769 1-s2.0-S0952197626021780-main.pdf 1-s2.0-S0952197626021780-main.pdf 1 2FB815CFE6DC6B328D63DEFCECD72484 48ded4effc9170065691a7c8dfa6ed5ba88e371326d2f59e32b82c2d59489655 341bc0d7-a82d-11f1-b9ca-00155d0001be https://dk.um.si/Dokument.php?lang=slv&id=211513 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0