StoryLensEdu: Personalized Learning Report Generation through Narrative-Driven Multi-Agent Systems

TL;DR

StoryLensEdu is a narrative-driven multi-agent system that generates engaging, personalized learning reports to improve interpretability, engagement, and understanding in self-regulated learning contexts.

Contribution

It introduces a novel multi-agent framework that combines data analysis, educational relevance, and storytelling to produce interactive, explainable learning reports.

Findings

Enhanced user engagement in real high school setting

Improved understanding of learning progress through narrative reports

Effective integration of storytelling in educational feedback

Abstract

Personalized feedback plays an important role in self-regulated learning (SRL), helping students track progress and refine their strategies. However, current common solutions, such as text-based reports or learning analytics dashboards, often suffer from poor interpretability, monotonous presentation, and limited explainability. To overcome these challenges, we present StoryLensEdu, a narrative-driven multi-agent system that automatically generates intuitive, engaging, and interactive learning reports. StoryLensEdu integrates three agents: a Data Analyst that extracts data insights based on a learning objective centered structure, a Teacher that ensures educational relevance and offers actionable suggestions, and a Storyteller that organizes these insights using the Heroes Journey narrative framework. StoryLensEdu supports post-generation interactive question answering to improve explainability and user engagement. We conducted a formative study in a real high school and iteratively developed StoryLensEdu in collaboration with an e-learning team to inform our design. Evaluation with real users shows that StoryLensEdu enhances engagement and promotes a deeper understanding of the learning process.