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Record W4412934466 · doi:10.1080/03004430.2025.2539870

Comparing the effects of AR picture books and print picture books on preschoolers’ reading effect

2025· article· en· W4412934466 on OpenAlex
Lei Wu, Ying Ma

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEarly Child Development and Care · 2025
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsEducation and Early Childhood Development
FundersNational Social Science Fund of China
KeywordsPicture booksPsychologyReading (process)Developmental psychologyMathematics educationVisual artsLinguisticsArt

Abstract

fetched live from OpenAlex

Augmented reality (AR) technology can enrich preschoolers' picture book reading experiences. This study examined differences in reading quality and comprehension between children using AR and traditional paper picture books. Ninety 5- to 6-year-old children from City S, with no prior AR exposure, were randomly assigned to an AR group or a paper book group. Data were collected to assess reading ability and comprehension. Results showed that AR picture books significantly improved reading ability and enhanced story retelling. While both groups showed similar performance on explicit comprehension and logical plot inference, the AR group demonstrated significant advantages in responding to implicit questions requiring deeper insight. Specifically, children using AR books better understood character emotions, cause-and-effect relationships, and the main idea. These findings highlight the potential of AR to support early reading development by fostering higher-level comprehension skills in preschool-aged children.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.209
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it