Effects of a generative AI-assisted ADPIE Framework on critical thinking, learning self-efficacy, and clinical reasoning in nursing education: a quasi-experimental study.
The nursing process is a systematic, patient-centered framework essential for clinical reasoning and decision-making in nursing education. It is commonly operationalized through the ADPIE (Assessment, Diagnosis, Planning, Implementation, and Evaluation) model, which guides nurses in identifying patient needs, developing care plans, implementing interventions, and evaluating outcomes in a structured and iterative manner. Although the nursing process is fundamental to nursing education and practice, some students may experience difficulties in applying ADPIE effectively, particularly the aspects of clinical reasoning and reflective practice.
Methods
This was a quasi-experimental study conducted to compare the effects of a Generative Artificial Intelligence (GenAI)-assisted nursing process approach with traditional instruction on nursing students' evidence-based critical thinking, learning self-efficacy, and self-reported clinical reasoning. Moreover, the study integrated the intervention into a second-year nursing course (i.e., nursing process via obstetric nursing internship) as part of formal curriculum activities.
Results
Students who participated in the GenAI-assisted nursing process approach demonstrated higher levels of evidence-based critical thinking, improved learning self-efficacy, and enhanced self-reported clinical reasoning ability compared with those in the traditional instruction group. The GenAI system provided adaptive feedback, real-time scaffolding, and iterative case refinement beyond traditional case-based learning.
Conclusions
Integrating GenAI into the ADPIE framework within nursing education may enhance students' critical thinking, learning self-efficacy, and self-reported clinical reasoning by supporting structured and reflective learning processes, offering a promising pedagogical approach for nursing education.
Introduction: The integration of Evidence-Based Practice (EBP) into nursing education faces challenges in linking theory to clinical application in complex family health contexts. Students struggle with efficiently accessing, appraising, and applying evidence influenced by sociocultural factors. Artificial intelligence (AI) offers transformative potential but requires pedagogical design to foster critical thinking and ethical use beyond technical skills.Method: An action research with mixed methods was conducted with 100 nursing students. The intervention had four phases: participatory family diagnosis, AI-assisted evidence retrieval and validation, community educational workshops design and execution, and multi-level evaluation.Results: A significant shift in AI use from basic to strategic, with a 40% reduction in literature search time. Qualitative data revealed enhanced critical awareness and ethical reasoning, while quantitative results indicated 90% of students improved critical appraisal skills and 70% felt more confident in evidence-based decisions. The project impacted 100 families, with 90% trusting evidence-based recommendations.Conclusions: Integrating AI in experiential pedagogies like Design Thinking and Service-Learning effectively develops nursing competencies, ensuring technology adoption supports context-sensitive family health learning outcomes.
Maria Graciela Villalba-Condori, Carla Cuya-Zevallos· Publicaciones· 0 citations
BACKGROUND
Equipping nursing students with 21st-century skills such as critical thinking, lifelong learning, and innovativeness is vital for safe and high-quality patient care. Although 21st-century competencies are increasingly emphasized as essential outcomes of nursing education, project planning courses remain insufficiently integrated into nursing curricula. Furthermore, there is a paucity of empirical evidence regarding its effects on individual innovativeness, critical thinking motivation, and lifelong learning among nursing students.
OBJECTIVE
This study aimed to examine the effect of a structured project planning course on nursing students' critical thinking motivation, individual innovativeness, and lifelong learning levels.
METHODS
This quasi-experimental study was conducted with second-year nursing students (intervention group: n = 19; control group: n = 19). The intervention group participated in a 14-week structured project planning course. Pre- and post-intervention measurements were conducted using the Critical Thinking Motivation Scale, Individual Innovativeness Scale, and Lifelong Learning Scale. Data were analyzed using paired and independent t-tests as well as repeated measures ANOVA.
RESULTS
Significant improvements were observed in the intervention group's total IIS scores (η2 = 0.024, p = 0.032), particularly in the subdimensions of opinion leadership (p = 0.012) and risk-taking (η2 = 0.082, p = 0.021). The qualitative shift in innovation profiles, supported by graphical analysis, showed a transition from passive adopter categories to "Innovators" and "Early Adopters" by the end of the course.
CONCLUSION
This study demonstrated the effectiveness of a structured project planning course in enhancing nursing students' individual innovativeness, especially in areas such as opinion leadership and risk-taking. While the impact on critical thinking motivation and lifelong learning was limited, emerging trends point to the potential for improvement with broader and more sustained application. These findings support the integration of structured project planning course into nursing education, emphasizing the value of combining theoretical instruction with practical research experiences.
M. Tunçer, Nurten Özen· Nurse Education Today· 0 citations
: Clinical reasoning is a core competence in nursing education. It helps nursing students collect patient cues, interpret clinical information, make safe decisions, and reflect on nursing actions. In recent years, technology-enhanced learning has been widely used in nursing education, including simulation, virtual simulation, virtual patients, online learning, virtual reality, and artificial intelligence-supported learning. These methods may provide safe, repeatable, and interactive learning contexts for students to practise clinical reasoning before entering real clinical settings. This systematic review aims to summarize current evidence on clinical reasoning in technology-enhanced nursing education. The review focuses on three questions: what technologies have been used, how these technologies support clinical reasoning, and what problems still exist in current research. The reviewed literature shows that simulation-based learning and virtual simulation are the most common approaches. These methods can improve students’ clinical reasoning, knowledge retention, self-efficacy, confidence, and satisfaction. Structured debriefing and reflection are important mechanisms that link technology use with reasoning development. However, current studies still have limitations, such as small samples, short intervention periods, inconsistent outcome measures, and limited long-term follow-up. Future research should use stronger designs, clearer theoretical models, and more direct measures of clinical reasoning.
Nanxiang Zhang· Curriculum and Teaching Meth...· 0 citations
BACKGROUND
Undergraduate pediatric nursing education must prepare students to integrate clinical information with developmental interpretation, family perspectives, communication, and uncertainty. Although case-based learning supports knowledge application, its conventional framing may underrepresent the relational, linguistic, and information-rich conditions in which pediatric judgment is formed.
AIM
To propose and justify a socio-clinical, case-based model of pediatric nursing clinical reasoning for undergraduate education.
METHODOLOGICAL APPROACH
A structured conceptual development process combined theory derivation and theory synthesis, guided by conceptual framework analysis. Sources from nursing science, educational theory, social theory, and contemporary information environments were selected for explanatory relevance. Concepts were retained according to theoretical relevance, definitional clarity, disciplinary compatibility, explanatory distinctiveness, and pedagogical operationalizability.
FINDINGS
The resulting framework integrates four mutually reinforcing dimensions-phenomenological-clinical, dialogical-relational, sociotechnical-critical, and linguistic-epistemic-and translates them into three interacting pedagogical layers: instructional, cognitive-clinical, and sociotechnical-relational. Linguistic mediation operates across the model to make reasoning visible, shareable, and assessable.
CONCLUSION
Pediatric nursing clinical reasoning is reframed as situated, developmentally informed, family-centered, and communicatively accountable judgment rather than as individual problem solving alone.
IMPLICATIONS FOR PRACTICE
The model offers educators a course-level architecture for sequencing authentic cases, focused conceptual scaffolding, structured dialogue, reasoning with evolving information, and assessment tasks that require students to interpret, prioritize, justify, and communicate decisions. It provides a testable basis for faculty development, curricular implementation, and evaluation across comparable undergraduate pediatric nursing settings.
María Antuña-Casal, Marta Rios· Journal of pediatric nursing· 0 citations
BACKGROUND
Traditional lecture-based teaching in nursing education often leads to limited student engagement, poor knowledge retention, and low self-confidence, particularly in complex fields such as mental health nursing, where students must navigate abstract clinical presentations and develop interpersonal skills that cannot be reduced to algorithmic protocols. Game-based learning (GBL) has emerged as an interactive strategy to enhance motivation, critical thinking, self-directed learning (SDL), and academic self-efficacy. This study aimed to evaluate the effectiveness of GBL integrated into a mental health nursing internship on SDL and academic self-efficacy among undergraduate nursing students.
METHODS
A quasi-experimental, two-group, pretest-posttest design with a one-month follow-up was employed. Forty-six sixth-semester nursing students from Abadan University of Medical Sciences (Iran) were enrolled and allocated to an intervention group (n = 23) or a control group (n = 23) based on the pre-existing scheduling of their clinical internship rotations. After accounting for attrition, the final analytical sample comprised 40 participants (intervention: n = 20; control: n = 20). The intervention group received GBL integrated with standard instruction, while the control group received conventional internship training only. The intervention involved a custom-designed board game with scenario-based challenges delivered over 10 sessions (40 h total). Outcomes were measured using the Self-Directed Learning Readiness Scale (SDLRS) and the College Academic Self-Efficacy Scale (CASES) at pretest, posttest, and one-month follow-up. Data were analyzed using repeated measures ANOVA with Bonferroni-corrected pairwise comparisons for within-group changes, independent t-tests for between-group comparisons, and Cohen's d for effect sizes. A sensitivity analysis for attrition bias was conducted using a pattern-mixture model under best-case, worst-case, and intermediate imputation scenarios.
RESULTS
Groups were homogeneous at baseline (P > 0.05 for all demographic and outcome variables). In the intervention group, repeated measures ANOVA revealed significant main effects of time for both SDL (F(2,38) = 38.74, P < 0.001, partial η²=0.67) and academic self-efficacy (F(2,38) = 42.51, P < 0.001, partial η²=0.69). Post-intervention, the GBL group showed significant improvements in SDL (mean difference: 16.25 ± 8.03, P < 0.001) and academic self-efficacy (mean difference: 12.50 ± 7.14, P < 0.001), with large effect sizes (Cohen's d = 1.25 and 1.56, respectively). These gains were maintained at the one-month follow-up, with no significant decline from posttest to follow-up (SDL: P = 0.784; self-efficacy: P = 0.821). The control group exhibited no significant changes across any time points (P > 0.05). Between-group differences were significant at posttest (SDL: P = 0.037, d = 0.68; self-efficacy: P = 0.029, d = 0.72) and at follow-up (SDL: P = 0.043, d = 0.66; self-efficacy: P = 0.034, d = 0.70). Sensitivity analyses confirmed that the findings were robust to plausible patterns of missing data due to attrition, with the treatment effect remaining significant even under worst-case imputation scenarios.
CONCLUSION
GBL integrated into a mental health nursing internship significantly enhanced SDL and academic self-efficacy, with effects sustained at one-month follow-up. These findings support the integration of game-based strategies into nursing curricula as a student-centered approach to bridge theory-practice gaps and foster durable self-regulatory capacities. Further research with larger samples, multi-site designs, and extended follow-up periods of six to twelve months is recommended to confirm the generalizability and long-term durability of these effects.
Alireza Rafi, Maryam Heidari, Seyed Mohammad Salehi Behbahani et al.· BMC Medical Education· 0 citations