Digital companions in early childhood education: a scoping review on the potential of chatbots for supporting social-emotional learning
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».