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COLLNET Journal of Scientometrics and Information Management cover

COLLNET Journal of Scientometrics and Information Management

(Since 2007)
Open Access ·Peer-reviewed·ISSN (Online): 2168-930X·ISSN (Print): 0973-7766

WoS  JIF 2026 : 0.6 (Q3)

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Half-Yearly Journal: Publishes research and articles on scientometrics and information management, including bibliometric analysis and quality assurance models.

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COLLNET Journal of Scientometrics and Information Management
Open Access Research Article

AI assistants in education and research : A sentiment-based comparative analysis of ChatGPT, Gemini, and Perplexity

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pp. 1–19Online FirstJuly 2026DOI: 10.47974/CJSIM-2025-09007XML
Published Online:
21 Jul 2026
Article type:
Research Article
Language:
EN
Article no.:
CJSIM-2025-09007
Pages:
1–19

Abstract

This study examines user perceptions of ChatGPT, Gemini, and Perplexity using Google Play Store reviews. Sentiment analysis was conducted using the Appbot analytics software, which enabled systematic classification of user opinions into positive, negative, and neutral categories.  The study adopts a comparative and descriptive research design to understand variations in user satisfaction across different AI assistants used in education and research contexts. User satisfaction was assessed through ratings, sentiment distribution, review volume, language diversity, and key thematic patterns. The analysis is based on large-scale, naturally occurring user-generated data, offering insights grounded in real-world usage rather than experimental settings. ChatGPT demonstrated the highest user engagement, with 81% positive reviews and the largest review volume. Gemini, although widely adopted, exhibited more polarized feedback, with 74% positive and 15% negative sentiment. Perplexity received fewer reviews but maintained 79% positive sentiment, indicating steady user satisfaction. Reviews of ChatGPT and Gemini reflected broad global language diversity, whereas Perplexity reviews were predominantly English. This variation suggests differences in global reach, adoption patterns, and localization strategies among the platforms. Across all three platforms, common themes included perceived helpfulness, creativity, and support for learning and research activities. At the same time, users reported concerns related to response speed, subscription costs, and technical limitations. These concerns highlight practical challenges that may affect long-term user trust and sustained adoption of AI assistants in academic environments. The findings offer comparative insights into how AI assistants are perceived by learners and researchers and underscore important implications for institutional decision-making regarding the responsible and sustainable integration of AI tools in higher education. The study contributes to emerging literature on AI-enabled educational technologies by positioning user sentiment as a critical dimension of technology acceptance, usability evaluation, and policy formulation.

Keywords

Artificial intelligenceChatGPTGeminiPerplexitySentiment analysisUser satisfactionMobile appAppbot

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