University Students' Trust in AI: Examining Reliance and Strategies for Critical Engagement - Abuzar, Muhammad; Mahmudulhassan; Muthoifin, (International Journal of Interactive Mobile Technologies (iJIM), 2025)
The rising use of artificial intelligence (AI) for academic tasks, such as exam preparation and assignment assistance, has increased concerns regarding students’ uncritical reliance on AI technologies in university settings. This mixed-method study investigates the factors driving this reliance, drawing on qualitative data from 18 Indonesian students and quantitative data from 328 students across 18 universities. It proposes strategies for promoting critical engagement with AI. Findings show that while students generally trust AI for its efficiency, evidenced by an average score of 3.89 on the trust in technology scale (TTS), their confidence declines when tasks demand nuanced human judgment. This over-reliance poses risks to critical thinking and may encourage cognitive complacency; however, many students counter this by cross-referencing AI-generated information with credible sources. To mitigate blind trust and promote responsible AI use, this study recommends enhancing AI literacy through workshops, promoting effective prompt crafting and output verification strategies, and implementing institutional policies to guide ethical AI use. By integrating reminder prompts and AI literacy modules into curricula, institutions can encourage critical engagement with AI, ensuring it enhances rather than undermines educational outcomes. These practical guidelines aim to enrich students’ academic experiences while addressing the ethical and cognitive challenges associated with uncritical AI reliance.