Exploring emotional and content influences on ChatGPT’s educational usability within the field of Human–Computer Interaction
This empirical study analyzes 26,399 education-focused user reviews of ChatGPT from the Google Play Store to investigate factors shaping perceptions of educational usability. Using quantitative methods including feature engineering, sentiment analysis, and ordinary least-squares regression, we exami...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
John Wiley and Sons
2026
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/123777/1/123777.pdf http://psasir.upm.edu.my/id/eprint/123777/ https://onlinelibrary.wiley.com/doi/10.1155/ahci/3363262 |
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| Summary: | This empirical study analyzes 26,399 education-focused user reviews of ChatGPT from the Google Play Store to investigate factors shaping perceptions of educational usability. Using quantitative methods including feature engineering, sentiment analysis, and ordinary least-squares regression, we examine how emotional sentiment, content quality, and review scores influence usability perceptions. Results reveal a counterintuitive emotional pattern: while surprise and anticipation enhance perceived usability, joy and disgust are associated with lower usability perceptions. Overall sentiment emerges as the strongest predictor of usability (coefficient = 0.8565, p ≤ 0.001), with content quality also exerting a significant positive effect (coefficient = 0.0334, p ≤ 0.001). In contrast, review scores show a small but significant negative relationship with usability (coefficient = −0.0210, p ≤ 0.001). Trust exhibits a slight negative effect, whereas fear, anger, and sadness show no significant associations. These findings highlight the critical role of emotional factors in educational AI usability, suggesting that developers should prioritize both high-quality content and positive emotional engagement. This research contributes to HCI and educational technology by providing a replicable framework for analyzing user perceptions of AI tools while emphasizing the importance of affective dimensions in technology adoption. |
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