Google's AI Overview surfaces AI-generated summaries directly in search results, before any organic links. The efficiency gain is real. So is the risk. This study investigates whether AI-generated search summaries actually improve user experience, and what accuracy, bias, and transparency concerns they introduce into everyday information retrieval.
Structured online survey (n=50) across three demographic groups (high school students, university students, and working professionals) combined with in-depth qualitative interviews. Quantitative data analysed in SPSS using descriptive statistics, independent t-tests, one-way ANOVA, Pearson correlation, and multiple regression. Qualitative data thematically coded in NVivo. Hypotheses: H1. AI Overview significantly enhances user experience through efficient, accessible results; H2: AI Overview raises concerns about accuracy, bias, and transparency that influence user trust.
Both hypotheses were supported. AI Overview significantly enhances perceived search efficiency; users found it faster and less cognitively demanding for straightforward queries. However, concerns about accuracy and bias were consistently present across all demographic groups, particularly among working professionals who cited instances of outdated or oversimplified information presented with unearned confidence. Transparency was identified as the critical gap: users struggled to assess how AI Overview determined relevance and whether its sources were current. Efficiency gains and trust concerns coexist, the design implication is clear: AI sourcing and recency must be surfaced, not hidden.
The most valuable lesson: choosing the right statistical test matters, using ANOVA vs t-test is a claim about what kind of comparison you're making, and getting it wrong invalidates your findings.