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

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


MPhil Thesis Defence


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

By

Miss Yan LUO


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
Hero's 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.


Date:                   Tuesday, 30 June 2026

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lifts 25/26

Chairman:               Dr. Jun HAN (EMIA)

Committee Members:      Prof. Huamin QU (Supervisor)
                        Dr. Qijia SHAO