An analysis of children’ interaction with an AI chatbot and its impact on their interest in reading
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The interaction with chatbots failed to make a statistically significant difference in engagement in the extensive reading activity.
“However, the interaction with chatbots failed to make a statistically significant difference in engagement in the extensive reading activity [38].”
From A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education (2023) · cites this paper as [38]
The citation lists haven’t been fetched yet
References 62 · Cited by 240
- Authors
- Liu, C.-C., Liao, M.-G., Chang, C.-H., & Lin, H.-M.
- Affiliations
- National Central University (TW)
- Minghsin University of Science and Technology (TW)
- Corresponding author
- Liu, Chen-Chung
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 189
- Pages
- 104576
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W4283733139
- Cited by
- 240
- References
- 62
- Citations by year
- 2026: 60 · 2025: 82 · 2024: 61 · 2023: 35 · 2022: 2
- FWCI
- 42.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers & Education (publishedVersion)
- Funders
- Ministry of Science and Technology
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- AI chatbots, book talks, reading interest, situational interest, student engagement, children's reading, reading promotion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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