Students’ Trust in AI and Their Verification Strategies

A Case Study at Camilo José Cela University

Bibliographic Data

ID22043922
AuthorsDavid Martín-Moncunill (0000-0003-2422-9005, Universidad Politécnica de Madrid, corresponding author), Daniel Alonso Martínez (0000-0001-8162-0970, Universidad Politécnica de Madrid)
Year2025
Volume15
Issue10
Pages1307
Publication date2025-10-02
Open AccessYes
TypeARTICLE
VenueEducation Sciences (JOURNAL)
Journal identifiersISSN: 2227-7102 • E-ISSN: 2227-7102
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci15101307
OpenAlexW4414746586
LanguageEN
Citations received4
References cited29

Trust plays a pivotal role in individuals’ interactions with technological systems, and those incorporating artificial intelligence present significantly greater challenges than traditional systems. The current landscape of higher education is increasingly shaped by the integration of AI assistants into students’ classroom experiences. Their appropriate use is closely tied to the level of trust placed in these tools, as well as the strategies adopted to critically assess the accuracy of AI-generated content. However, scholarly attention to this dimension remains limited. To explore these dynamics, this study applied the POTDAI evaluation framework to a sample of 132 engineering and social sciences students at Camilo José Cela University in Madrid, Spain. The findings reveal a general lack of trust in AI assistants despite their extensive use, common reliance on inadequate verification methods, and a notable skepticism regarding professors’ ability to detect AI-related errors. Additionally, students demonstrated a concerning misperception of the capabilities of different AI models, often favoring less advanced or less appropriate tools. These results underscore the urgent need to establish a reliable verification protocol accessible to both students and faculty, and to further investigate the reasons why students opt for limited tools over the more powerful alternatives made available to them

Higher education · Skepticism · Big Data and Business Intelligence · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI

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