Exploring Student Feedback Needs and Design Opportunities in Data Storytelling Education

TL;DR

This study investigates how AI-assisted feedback can support data storytelling education by exploring learner needs and designing effective feedback strategies through participatory methods.

Contribution

It introduces a participatory design process to develop and evaluate feedback strategies for an AI-supported storytelling tool tailored to student needs.

Findings

On-demand and process feedback are perceived as effective by learners and educators.

Automatic and outcome feedback are seen as more persuasive but less preferred.

Design implications for adaptive AI feedback modes in storytelling education.

Abstract

Data storytelling workflows ask learners to integrate analytical, design, and narrative skills, but instructors rarely have the capacity to provide detailed feedback at each step. Computational and AI-assisted storytelling offers opportunities to support student learning, but how feedback should be structured effectively remains unclear. To address this gap, we conducted a two-phase participatory design study. Through participant observations (N=8) and interviews (N=6), the first phase explored learners and educators' feedback needs and challenges in a data storytelling course. The second phase conducted two design workshops (N=8/10) to design and evaluate feedback strategies (frequency, seamlessness, accountability) for Story Studio: an AI-assisted narrative storytelling application. Our findings show that participants perceived on-demand and process feedback modes as effective, but automatic and outcome feedback as slightly more persuasive. We discuss implications for designing AI-augmented storytelling systems that adapt their feedback modes to the diverse needs and expectations of students.