A Quantitative Analysis of Information Verification Habits and Cross-Checking Behaviors Toward AI Hallucinations Among College Students

This study investigated the information verification habits and cross-checking behaviors of Computer Studies students when encountering Generative Artificial Intelligence (AI) hallucinations. The primary objective was to determine how students' demographic and AI usage profiles influence their active verification methods, error detection success rates, and perceived reliability of AI tools. A quantitative descriptive-correlational design was adopted, utilizing a validated, scenario-based digital survey deployed via Google Forms to 200 Bachelor of Science in Information Technology (BSIT) and Bachelor of Science in Computer Science (BSCS) students at Quezon City University. The findings revealed that 84.5% of the respondents were highly active daily or weekly users of generative AI, primarily utilizing general web chatbots (58.5%) and dedicated Integrated Development Environment (IDE) assistants (41.5%) for debugging (45.5%) and code generation (33.5%). Students demonstrated a structured baseline of verification rigor (Composite Mean = 3.13, Agree), heavily prioritizing external validation such as manually confirming the existence of new libraries (Weighted Mean = 3.23) and cross-referencing official documentation (Weighted Mean = 3.20) rather than trusting the AI to self-verify its own outputs (Weighted Mean = 2.94). However, a significant cognitive vulnerability to automation bias was established, as students explicitly agreed that convenience and speed outweigh the risks of incorrect syntax (Weighted Mean = 3.11) and falsely perceived models as possessing deep contextual understanding (Weighted Mean = 3.20). Furthermore, a statistically significant negative correlation was found between the primary tool utilized and verification habits (r = -0.177, p = 0.012), proving that seamless inline code generation within dedicated IDE assistants suppresses manual auditing. Conversely, verification rigor remained entirely uniform across all academic year levels (F(3, 196) = 1.35, p = 0.261), overall usage frequencies (r = 0.053, p = 0.453), and task complexities (r = -0.097, p = 0.170). Ultimately, the study concludes that cognitive over-reliance is driven primarily by interface delivery friction rather than student seniority, designating an urgent institutional need to transition away from restrictive policies and proactively embed formal "AI Auditing" instruction, mandatory external citation protocols, and foundational mechanics of Large Language Models directly into the core programming curriculum.

AI Hallucinations, Automation Bias, Computer Studies, Generative AI, Information Verification

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