Pith. sign in

REVIEW 2 cited by

ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2307.01135 v1 pith:H5UAOHCS submitted 2023-07-03 cs.AI cs.HCcs.IR

classification cs.AIcs.HCcs.IR
keywords chatgptsearchuserchatbotgoogleperformancetaskstools
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The advent of ChatGPT, a large language model-powered chatbot, has prompted questions about its potential implications for traditional search engines. In this study, we investigate the differences in user behavior when employing search engines and chatbot tools for information-seeking tasks. We carry out a randomized online experiment, dividing participants into two groups: one using a ChatGPT-like tool and the other using a Google Search-like tool. Our findings reveal that the ChatGPT group consistently spends less time on all tasks, with no significant difference in overall task performance between the groups. Notably, ChatGPT levels user search performance across different education levels and excels in answering straightforward questions and providing general solutions but falls short in fact-checking tasks. Users perceive ChatGPT's responses as having higher information quality compared to Google Search, despite displaying a similar level of trust in both tools. Furthermore, participants using ChatGPT report significantly better user experiences in terms of usefulness, enjoyment, and satisfaction, while perceived ease of use remains comparable between the two tools. However, ChatGPT may also lead to overreliance and generate or replicate misinformation, yielding inconsistent results. Our study offers valuable insights for search engine management and highlights opportunities for integrating chatbot technologies into search engine designs.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fair Document Valuation in LLM Summaries via Shapley Values

    cs.CL 2025-05 reject novelty 6.0 of 10

    Cluster Shapley groups semantically similar documents via embeddings and computes cluster-level Shapley values, claiming better efficiency-accuracy trade-offs than Monte Carlo and Kernel SHAP on Amazon review summarization.

  2. "Pragmatic Tools or Empowering Friends?" Discovering and Co-Designing Personality-Aligned AI Writing Companions

    cs.HC 2025-09 conditional novelty 5.0 of 10

    Writers grouped into four MBTI-based profiles showed divergent preferences for AI writing companion features, demonstrated by two contrasting prototypes in a small proof-of-concept study.

Pith tools