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Exploring nursing students' experiences of using generative AI for emotional intelligence learning: a qualitative
Xiaohui Fan1, Jingjing Sun2, Rui Xu2
1Biomedical Research Center, School of Medicine, Xijing University, Xi'an, China.
Background:
Emotional intelligence is a core competency for nursing students; however, traditional nursing education faces challenges in fostering emotional intelligence, including curricular imbalances, limited teaching methods, and delayed assessment and feedback. The rapid development of generative artificial intelligence (GenAI) offers new possibilities for addressing the above issues; however, existing research has not yet thoroughly explored how nursing students learn emotional intelligence through GenAI or their experiences in this process.
Objective:
Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Teaching Pedagogical Content Knowledge (TPACK) framework, and using Goleman's five-dimensional model of emotional intelligence as a reference, this study explores the experiences of undergraduate nursing students in using generative artificial intelligence for emotional intelligence learning, as well as the factors that facilitate this process.
Method:
This study employed a qualitative descriptive research design and Reflexive Thematic Analysis (RTA). Using purposive sampling, 19 undergraduate nursing students (from freshman to senior year) were selected for one-on-one semi-structured in-depth interviews. All participants had experience using GenAI tools (such as DeepSeek and Doubao) for communication practice, case discussions, or sentiment analysis. The interview data were analyzed using the RTA six-stage process, with NVivo 12.0 software used to assist with data management.
Results:
We identified and synthesized 238 initial codes into three core themes. Theme 1, "From Efficiency-Driven to Precise Empathy," presents the differing descriptions of empathy-related experiences among students in different grade levels: lower-grade students more frequently reported efficiency-driven usage experiences; some middle-grade students described difficulties caused by technical barriers; and upper-grade students more often described the recognition of patients' emotions in conjunction with clinical experience. Theme 2, "From Mimicking AI to Autonomous Expression," focuses on students' reported experiences related to social skills, including direct application, recognition and adjustment, autonomous expression, and the simulation gap that persisted throughout the process. Theme 3, "From Technical Frustration to Mental Rehearsal," integrates experiences related to self-management, self-awareness, and self-motivation. The three themes correspond to different dimensions of emotional intelligence, with students in different grade levels reporting distinct experiential characteristics.
Conclusion:
Nursing undergraduates in different grades exhibit distinct experiential characteristics when using generative artificial intelligence to learn about emotional intelligence. Students' reports repeatedly highlighted moderate tension around technological motivation, the ability to critically integrate technology, and the perceived value of technological friction. Based on these exploratory findings, we propose several preliminary educational implications-including grade-level-specific support, curriculum exploration, and education in critical technological literacy and ethics-for consideration in future research and practice.
