Digital companions in early childhood education: a scoping review on the potential of chatbots for supporting social-emotional learning
Bibliographic record
Abstract
Introduction Artificial intelligence (AI)-powered chatbots are increasingly integrated into early childhood education; however, their contribution to children's social-emotional learning (SEL) has not been systematically synthesized. While evidence suggests that such technologies can support self-awareness, emotional regulation, and social interaction, research remains fragmented in terms of developmental appropriateness, ethical safeguards, and pedagogical alignment. This review addresses this gap by mapping the current state of knowledge on chatbot-supported SEL in early learning contexts. Methods Following the PRISMA-ScR protocol, a comprehensive search was conducted across Scopus, Web of Science, ERIC, ScienceDirect, and SpringerLink for peer-reviewed studies published between January 2019 and March 2025. Inclusion criteria required studies to involve children aged 0–8, investigate chatbot-based interaction in educational settings, and examine at least one SEL domain. Data were charted and thematically synthesized according to research design, participant profile, technological features, and SEL competencies. Results Of 205 records initially identified, 13 studies met the eligibility criteria. Most were published in 2023–2024 (76.9%). Nearly half employed experimental or intervention designs (46.2%), with smaller proportions focusing on design-based studies (30.8%), theoretical or ethical analyses (15.4%), and qualitative investigations (7.7%). Mapping against SEL domains indicated stronger emphasis on self-awareness and self-management (each 30.8%), with relatively limited coverage of social awareness (15.4%), relationship skills (15.4%), and responsible decision-making (23.1%). Frequently adopted technological affordances included natural language processing, emotion recognition, and multimodal interfaces, though adult mediation and long-term developmental effects were rarely addressed. Ethical considerations were also insufficiently examined. Discussion The findings underscore the promise of AI-powered chatbots in advancing SEL during early childhood while highlighting significant gaps in empirical validation, theoretical grounding, and ethical responsibility. This review contributes a consolidated knowledge base to guide future research, pedagogical practice, and technology design, ensuring that chatbot applications in early learning environments are developmentally appropriate, ethically sound, and contextually meaningful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".