Enhancing Emotional Intelligence Through Generative AI‐Supported Digital Storytelling: A Mixed Methods Study Using Epistemic Network Analysis. - Hwang, Gwo‐Jen; Tu, Yun‐Fang; Lin, Yue‐I; Chen, Hsiu‐Ling (Journal of Computer Assisted Learning, 2026)
Background: Social–emotional learning (SEL) has gained increasing attention in recent years due to its importance for students' emotional regulation, motivation and learning engagement. Digital storytelling (DST) has been widely recognised as a promising approach for supporting SEL; however, students often encounter challenges related to creative expression and technical execution during the storytelling process. Objective: Although DST holds considerable promise for supporting SEL, students often encounter challenges related to creative expression when using DST, which may limit their learning effectiveness. To address this issue, this study proposed a generative‐AI (GAI)‐supported DST approach. Method: A quasi‐experimental design was adopted with 62 junior high school students. The experimental group (n = 30) learned using the GAI‐DST approach, while the control group (n = 32) adopted a conventional DST approach. Quantitative data were collected through pre‐ and post‐questionnaires on emotional intelligence and self‐efficacy. Qualitative interview data were further analysed using epistemic network analysis (ENA) to explore students' learning perceptions and cognitive‐emotional patterns. Results and Conclusions: Results showed that the GAI‐DST approach significantly enhanced students' self‐efficacy and the motivation dimension of emotional intelligence, while no significant differences were found in other emotional intelligence dimensions. The qualitative findings revealed that students in the GAI‐DST group demonstrated a stronger orientation towards practice‐based learning, tool‐supported problem solving and emotional engagement, whereas students in the control group focused more on mastering basic skills. These findings suggest that the GAI‐DST approach may exert selective effects on motivational and self‐efficacy‐related processes, rather than producing immediate, broad‐based improvements across all dimensions of emotional intelligence. By conceptualising GAI as a mediational learning tool that supports mastery experiences and reflective interaction, this study provides a theoretically grounded explanation of how GAI can support SEL and offers suggestions for the design of AI‐integrated instructional activities. Lay Description: What is already known about this topic ○Social–emotional learning (SEL) has been increasingly recognised for its importance in enhancing students' emotional intelligence and self‐efficacy.○Digital storytelling (DST) integrates technology with traditional narrative techniques to boost student engagement and learning interest. However, students often face technological and creative challenges when using DST, which may hinder learning effectiveness.○The emergence of generative artificial intelligence (GAI) offers potential solutions to these challenges by assisting students in overcoming technological and creative barriers and improving the quality of their DST work.What this paper adds ○This study proposes a GAI‐supported DST approach to prompting students' emotional intelligence.○An experiment was conducted to compare the effects of the GAI‐DST and conventional DST (C‐DST) learning approaches on students' self‐efficacy, emotional intelligence and learning perceptions using mixed methods, including epistemic network analysis.○Results show that the GAI‐DST approach significantly enhanced students' self‐efficacy and the motivation dimension of emotional intelligence. It also shifted students' focus towards practice‐oriented learning, emphasising the practical application of knowledge and emotional connection.Implications for practice and/or policy ○Findings suggest that GAI‐assisted DST can provide students with greater opportunities for practice‐oriented, emotionally connected learning, which may lead to more meaningful educational experiences.○This study offers insights for future research and SEL curriculum design.