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Record W4416501180 · doi:10.7759/cureus.97429

Impact of Screen Time on Language Development and Vocabulary Acquisition in Early Childhood: A Systematic Review

2025· review· en· W4416501180 on OpenAlexaff
Evangeline C Nwachukwu, Sandeep Sekar Lakshmisai, Priyanka Sakarkar, Roshitha S Bheemaneni, Iana Malasevskaia

Bibliographic record

VenueCureus · 2025
Typereview
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsTrinity College
Fundersnot available
KeywordsGrading (engineering)Critical appraisalVocabularyPsychological interventionLanguage developmentSystematic reviewEnglish languageInclusion (mineral)MEDLINE

Abstract

fetched live from OpenAlex

Language development in the first few years of life is critical for later academic and social success. With the increasing use of digital devices among preschoolers, there have been concerns about the potential impact of screen time on language outcomes. Existing research presents mixed findings, making it necessary to synthesize current evidence. This systematic review aimed to examine and synthesize empirical evidence on the relationship between screen time and language development in early childhood, with emphasis on factors such as device type, content quality, socioeconomic status, and parent-child interaction. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted in the Cochrane Library, PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), EBSCO Open Dissertations, ScienceDirect, and Clinicaltrials.gov for studies published between January 1, 2020, and February 17, 2025. Our inclusion criteria required studies published in the English language, assessed screen time exposure, and measured language outcomes in typically developing preschoolers. Data were extracted using a standardized form. The risk of bias was assessed using the Cochrane Risk of Bias 2 (RoB 2), the Risk Of Bias In Non-Randomized Studies of Interventions (ROBINS-I), and the Joanna Briggs Institute (JBI) Critical Appraisal tools. The overall certainty of the evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework. A total of eight studies met the inclusion criteria, mostly consisting of cross-sectional studies that involved diverse patient populations with sample sizes ranging from 83 to 4,907 participants. The participants were primarily children aged three to six years and/or their parents. These studies mainly focused on measuring the duration of screen time, which averaged between approximately 1.39 and 2.65 hours per day. One study analyzed 44 mobile applications for their learning goals and educational potential. The most commonly reported outcomes were related to language development and vocabulary acquisition, which were assessed through parental surveys and developmental scales. Synthesized evidence suggests that high levels of unsupervised or passive screen time are often linked to weaker language development outcomes in preschoolers. However, screen use that is interactive, educational, and involves caregiver participation appears to mitigate these potential effects. The overall certainty of this evidence, however, remains limited. Future research should prioritize consistent measurement approaches and explore long-term impacts.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.047
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.011
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.337
Teacher spread0.320 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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