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

2026-02-18

AI Summary

This study addresses the limitations of conventional learning reports—such as weak interpretability, monotonous formats, and insufficient pedagogical guidance—which hinder students’ self-regulated learning. To overcome these challenges, we propose a narrative-driven multi-agent system that uniquely integrates the “Hero’s Journey” storytelling framework with a collaborative multi-agent architecture. The system coordinates three specialized agents—Data Analyst, Teacher, and Storyteller—to jointly generate personalized, interactive, and pedagogically meaningful learning reports, further supporting post-generation interactive question-answering. Experimental results from a real-world high school setting demonstrate that our approach significantly enhances students’ depth of understanding of their learning processes and boosts their engagement, while also improving the interpretability and immersive quality of the reports.

Technology Category

• Multiagent Systems: Multiagent Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• 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

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.

Problem

• personalized feedback
• self-regulated learning
• learning analytics
• interpretability
• explainability

Innovation

• narrative-driven
• multi-agent system
• personalized learning report
• Heroes Journey
• interactive explainability