Enhancing Emotional Intelligence Through Generative AI-Supported Digital Storytelling: A Mixed Methods Study Using Epistemic Network Analysis

IF 5.7 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2026-04-15 DOI:10.1002/jcal.70245

Gwo-Jen Hwang, Yun-Fang Tu, Yue-I Lin, Hsiu-Ling Chen

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引用次数: 0

Abstract

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.

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通过生成式人工智能支持的数字故事叙述提高情绪智力：使用认知网络分析的混合方法研究

近年来，社会情绪学习因其对学生情绪调节、学习动机和学习投入的重要作用而受到越来越多的关注。数字叙事（DST）已被广泛认为是支持SEL的一种有前途的方法；然而，在讲故事的过程中，学生们经常会遇到与创造性表达和技术执行相关的挑战。尽管DST在支持SEL方面具有相当大的前景，但学生在使用DST时经常遇到与创造性表达相关的挑战，这可能会限制他们的学习效果。为了解决这一问题，本研究提出了一种生成人工智能（GAI）支持的DST方法。方法采用准实验设计，对62名初中生进行问卷调查。实验组（n = 30）采用GAI-DST方法学习，对照组（n = 32）采用常规DST方法学习。通过问卷调查前和问卷调查后对情绪智力和自我效能感进行定量分析。采用认知网络分析（ENA）进一步分析定性访谈数据，探讨学生的学习知觉和认知-情绪模式。结果与结论结果表明，GAI-DST方法显著提高了学生的自我效能感和情绪智力动机维度，而在其他情绪智力维度上无显著差异。定性研究结果显示，ai - dst组的学生表现出更强的以实践为基础的学习、工具支持的问题解决和情感投入的倾向，而对照组的学生则更注重掌握基本技能。这些发现表明，GAI-DST方法可能对动机和自我效能相关的过程产生选择性影响，而不是在情商的所有维度上产生直接的、广泛的改善。通过将GAI概念化为一种支持掌握体验和反思性互动的中介学习工具，本研究提供了GAI如何支持SEL的理论基础解释，并为ai集成教学活动的设计提供了建议。

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

期刊介绍： The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope