Measuring University Students Satisfaction with Traditional Search Engines and Generative AI Tools as Information Sources

Abstract

This study examines university students levels of satisfaction with generative artificial intelligence (AI) tools and traditional search engines as academic information sources. An electronic survey was distributed to students at U.S. universities in late fall 2025, with 236 valid responses received. In addition to demographic information about respondents, frequency of use and levels of satisfaction with both generative AI and traditional search engines were measured. Principal components analysis identified distinct constructs of satisfaction for each information source, while k-means cluster analysis revealed two primary student groups: those highly satisfied with search engines but dissatisfied with AI, and those moderately to highly satisfied with both. Regression analysis showed that frequency of use strongly predicts satisfaction, with international and undergraduate students reporting significantly higher satisfaction with AI tools than domestic and graduate students. Students generally expressed higher levels of satisfaction with traditional search engines over generative AI tools. Those who did prefer AI tools appear to see them more as a complementary source of information rather than a replacement for other sources. These findings stress evolving patterns of student information seeking and use behavior and offer meaningful insights for evaluating and integrating both traditional and AI-driven information sources within higher education.

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