Measuring university students’ satisfaction with traditional search engines and generative AI tools as information sources

Purpose This study examines university students’ levels of satisfaction with generative artificial intelligence (AI) tools and traditional search engines as academic information sources. Design/methodology/approach An electronic survey was distributed to students at US universities in late fall 2025, resulting in 236 valid responses. The survey collected demographic data and measured frequency of use and satisfaction with both generative AI tools and traditional search engines. Principal components analysis was used to identify underlying satisfaction constructs, followed by k-means cluster analysis to identify student usage profiles. Regression analysis examined predictors of satisfaction. Findings Students reported higher overall satisfaction with traditional search engines than with generative AI tools. Two primary student groups emerged: those highly satisfied with search engines but dissatisfied with AI, and those moderately to highly satisfied with both sources. Frequency of use was a strong predictor of satisfaction for both information sources. International and undergraduate students reported significantly higher satisfaction with AI tools than domestic and graduate students. Students who preferred AI tools generally viewed them as complementary rather than as replacements for traditional information sources. Originality/value This study provides empirical insight into evolving student information-seeking behaviors by directly comparing satisfaction with generative AI tools and traditional search engines. The findings may inform higher education stakeholders seeking to evaluate, support and integrate AI-driven information tools alongside established academic search technologies.