University Students' Trust in AI: Examining Reliance and Strategies for Critical Engagement

Abuzar, Muhammad; Mahmudulhassan; Muthoifin · 2025 · Crossref

DOI: 10.3991/ijim.v19i07.52875

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Summary

This mixed-method study investigates university students’ trust in artificial intelligence (AI) and their reliance on these tools for academic tasks, addressing concerns about uncritical acceptance and cognitive complacency. Motivated by the rapid integration of AI in education and the potential erosion of critical thinking skills, the research aims to identify factors driving blind trust and propose strategies for fostering critical engagement. The study focuses on Indonesian university students, examining how digital competencies and motivations influence AI usage and evaluating existing educational approaches to AI literacy. The methodology combines qualitative and quantitative data. The qualitative component involved semi-structured interviews with 18 students selected via purposive sampling to ensure diversity in gender, discipline, and educational level. Data saturation was achieved through thematic analysis using Braun and Clarke’s framework. The quantitative component utilized a structured online survey administered to 328 students across 18 universities, employing stratified random sampling to represent various faculties and academic levels. The survey used an adapted Trust in Technology Scale (TTS) to measure trust based on perceived competence, risk, and familiarity, with data analyzed using SPSS for descriptive statistics, Pearson correlation, multiple regression, and ANOVA. Findings indicate that students generally trust AI for its efficiency, evidenced by an average TTS score of 3.89. Key drivers of trust include perceived data validity, social influence, time-saving capabilities, and ease of use. However, confidence declines when tasks require nuanced human judgment. Qualitative results reveal significant risks associated with over-reliance, including reduced critical thinking, creativity, and academic rigor, as well as concerns regarding the accuracy and potential bias of AI-generated content. Despite these risks, many students mitigate blind trust by cross-referencing AI outputs with credible sources and recognizing AI as a supplementary tool rather than a replacement for human expertise. Students emphasized the importance of prompt formulation and the need for institutional policies and educational initiatives to guide ethical AI use. The study concludes that while AI enhances academic efficiency, uncritical reliance poses ethical and cognitive challenges. To mitigate these risks, the authors recommend enhancing AI literacy through workshops, promoting effective prompt crafting and output verification strategies, and implementing institutional policies that encourage critical engagement. Integrating AI literacy modules and reminder prompts into curricula can help ensure AI serves as a supportive tool that enriches educational outcomes without undermining independent judgment and critical thinking skills.

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• trust calibration

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• Empirical Findings: self report data