Co-creating Generative Artificial Intelligence education resources using the ‘Storytelling Empirical Research Translation’ (SERT) approach

Abstract

This study aims to explore how underrepresented children engage with Generative Artificial Intelligence (GenAI) applying an innovative methodology - Storytelling Empirical Research Translation (SERT) - for translating empirical research into accessible, co-created educational resources. Eighteen secondary school students from underrepresented backgrounds participated in the study. Data were collected through a combination of practical activities, questionnaires and focus groups designed to capture students’ engagement, experiences, and emotional responses to GenAI. The research employed a novel methodology called Storytelling Empirical Research Translation (SERT), which blends empirical data, critical reflection, and creative storytelling to produce animated video cartoon stories.The SERT approach successfully transformed complex research insights into engaging and relatable narratives for an audience of children. It fostered active dialogue and participation, empowering students to express their perspectives on GenAI rather than passively consuming information. The use of animated storytelling enhanced comprehension of GenAI concepts. Co-creation of educational content with children is a powerful strategy for inclusive and responsible AI education. Empirical exploration of GenAI from children’s perspectives contributes meaningfully to the development of an informed and participatory AI educational ecosystem.