Actionable companion · Assessment and evidence of learning

From evidence to district action: Exploring nursing students’ experiences of using generative AI for emotional intelligence learning: a qualitative study

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

Original authors’ abstract, reproduced under the linked source license:

Abstract 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. Keywords: emotional intelligence, generative artificial intelligence, qualitative research, TPACK framework, undergraduate nursing students, UTAUT model

A 90-day district pilot

1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
2. Days 16–30 · Design. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. Name an accountable owner and set the baseline, success threshold and budget before implementation.
3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
4. Days 61–90 · Decide. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Xiaohui Fan; Jingjing Sun; Rui Xu; Linghui Zhang; Ruixiao Ou; Ruihua Jin; Hui Yang; Panpan Huai. Exploring nursing students’ experiences of using generative AI for emotional intelligence learning: a qualitative study. Frontiers in Public Health (2026-10-12). https://doi.org/10.3389/fpubh.2026.1967799

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.