eaSEL: Promoting Social-Emotional Learning and Parent-Child Interaction through AI-Mediated Content Consumption

2025-01-29

AI Summary

To address the lack of social-emotional learning (SEL) support for children when parents cannot co-view videos, this study proposes the first AI-powered SEL intervention framework for non-co-viewing settings. Methodologically, it integrates large language model (LLM)-based multi-task prompting, a social-emotional event detection model, and a dual-role–adaptive (child/parent) reflective activity generation algorithm to automatically identify emotional moments from video transcripts and generate hierarchical, context-aware activities—seamlessly embedding SEL into routine video consumption. Technical evaluation demonstrates high emotion recognition accuracy and superior activity quality. A field study with 20 families shows statistically significant improvements in children’s emotional reflection ability (p < 0.01) and marked enhancements in parental dialogue depth and scaffolding quality. The core contribution lies in decoupling and coordinating child-led reflection with parent-guided dialogue—thereby overcoming the traditional dependency of SEL interventions on synchronous parent–child co-presence.

Technology Category

• Humans and AI: Emotional Intelligence
• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

As children increasingly consume media on devices, parents look for ways this usage can support learning and growth, especially in domains like social-emotional learning. We introduce eaSEL, a system that (a) integrates social-emotional learning (SEL) curricula into children's video consumption by generating reflection activities and (b) facilitates parent-child discussions around digital media without requiring co-consumption of videos. We present a technical evaluation of our system's ability to detect social-emotional moments within a transcript and to generate high-quality SEL-based activities for both children and parents. Through a user study with N=20 parent-child dyads, we find that after completing an eaSEL activity, children reflect more on the emotional content of videos. Furthermore, parents find that the tool promotes meaningful active engagement and could scaffold deeper conversations around content. Our work paves directions in how AI can support children's social-emotional reflection of media and family connections in the digital age.

Problem

• Emotional Learning
• Parent-Child Relationship
• Smart Assistant

Innovation

• eaSEL
• Emotional Learning
• AI-assisted Parenting