Aug 2026· Frontiers in Medicine· 0 citations· 30 references
Abstract
Artificial intelligence (AI) is quickly revolutionizing higher education; however, there is high heterogeneity regarding the adoption of AI amongst the students. In the context of nursing education, where successful learning, critical thinking skills, and professional accountability are crucial, it becomes essential to understand how the students interact with the AI applications. The current study was conducted to determine the AI-use profiles among nursing students and how they relate to academic outcomes and demographics.
We used a cross-sectional survey among nursing students. AI use for learning purposes was assessed using four binary indicators capturing use for understanding concepts, summarizing content, drafting assignments, and language support. Study engagement was measured using selected items from the Online Student Engagement framework and collapsed into three ordinal categories. We assessed academic performance using three self-reported items. We performed a latent class analysis to identify distinct student profiles based on AI use and engagement indicators. Multinomial logistic regression examined associations between demographic factors and class membership. Multiple linear regression assessed differences in academic performance across classes.
The best model was a three-profile solution with a good classification accuracy. The profiles included strategic engagers, moderate users, and passive or low engagers. As expected, strategic engagers were characterized by high levels of engagement with all kinds of behaviors studied, whereas Passive used consistently reported low levels of engagement. Usage indicators of AI revealed little variation among classes. The performance scores of strategic engagers were significantly better compared with those of passive users (β = 2.39, 95% CI = 2.08–2.69). No significant association was found for demographic characteristics.
Nursing students can be categorized into distinct profiles based on their patterns of AI use and study engagement. Our findings showed that study engagement, but not AI use alone, was the key factor associated with academic performance. These findings highlight the importance of promoting effective learning strategies alongside AI integration. Educational interventions should focus on guiding students toward strategic and responsible use of AI to enhance learning outcomes.
This study examines the impact of AI-powered adaptive learning on students’ academic achievement and engagement in secondary schools in Nigeria. It also explores the moderating effects of gender and technology acceptance using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A quantitative survey research design was adopted, and data were collected from secondary school students in private schools with access to the uLesson AI adaptive learning tool. A structured questionnaire, consisting of the Adaptive Learning Engagement Scale (ALES) (r = .75) and the Technology Acceptance and Academic Performance Scale (r =.78), was used to gather responses. Descriptive and inferential statistics, including multiple regression analysis, were employed to analyze the data. Findings indicate a significant positive relationship between AI-powered adaptive learning and students’ academic achievement and engagement. Also, technology acceptance (UTAUT factors) significantly influenced students’ learning outcomes, with performance expectancy and effort expectancy emerging as key predictors. While gender had a notable effect on academic performance, its impact on engagement was not statistically significant. The study highlights the importance of integrating AI-driven learning tools with Supportive technology acceptance strategies to enhance student outcomes. It is recommended that educators and policymakers promote AI adoption, provide necessary training, and develop policies that foster a technology-friendly learning environment in Nigerian secondary schools.
A. A. Adedokun, Toyibat Wuraola Makanjuola· European Journal of Educatio...· 0 citations
Artificial intelligence (AI) is increasingly being integrated into higher education, yet its educational value depends not only on access to technological tools but also on how these tools are pedagogically embedded within teaching and learning. Although prior studies have reported positive associations between educational technologies and learning outcomes, fewer studies have examined whether AI tool usage explains perceived learning effectiveness beyond established engagement dimensions and instructional integration quality. This study investigates the relationships among AI tool usage, behavioural, cognitive and emotional engagement, instructional integration quality, and perceived learning effectiveness in higher education. Using a quantitative cross-sectional survey design, data were collected from 214 participants, including faculty members and undergraduate students, through a structured Likert-scale questionnaire. Descriptive, correlational, regression and group-comparison analyses were conducted to examine associative and predictive patterns. The findings indicate that AI tool usage was positively associated with all engagement dimensions and with perceived learning effectiveness. In the regression model, AI tool usage remained a significant independent predictor of perceived learning effectiveness even after controlling for engagement dimensions and instructional integration quality, while instructional integration quality also showed a significant positive effect. In addition, AI users reported significantly higher levels of engagement and learning effectiveness than non-users, whereas no significant differences were observed between faculty and students in engagement dimensions or learning outcomes. These findings suggest that AI-supported instruction provides additional explanatory value beyond engagement alone and that its effectiveness depends on coherent pedagogical integration. The study contributes to the AI-in-education literature by offering empirical evidence on the role of AI-supported instruction within an engagement-based framework and by highlighting implications for instructional design, faculty development, and institutional AI integration in higher education.
Hajar Mohamad, Yousef Qawqzeh· Journal of Information &...· 0 citations
While medical terminology is commonly taught to healthcare or science-based students, business and management students in health-related programs often struggle due to the complexity of medical word roots, prefixes, and suffixes. The incorporation of diverse teaching approaches helps bridge the learning gap by supporting students in mastering essential medical terms that are critical for their future roles in health administration and management. The objectives of this study were to assess the satisfaction, engagement, and academic achievement of students in medical terminology course using Team Based Learning (TBL) and artificial intelligence (AI) powered learning versus lecture-based method. Methods: A cross-sectional study was conducted from March to July 2025 within a health administration undergraduate program at a major public university in Malaysia, evaluating three instructional delivery modes across a 14-week course using paired sample t tests. A total of 79 students taking the Health Terminological course participated in the study. Results: Academic achievement and engagement were significantly higher under TBL and AI powered learning compared to traditional lectures. However, these gains were not reflected in the affective domain, as overall student satisfaction levels remained comparable across all three instructional methods. Conclusion: These findings reveal that a significant engagement-satisfaction trade-off is at play. To capture the full value of active methods, instructional design must address this imbalance, prioritizing not only cognitive engagement but also efforts to strategically mitigate the perception of excessive workload so that increased rigor fully translates into positive affective and learning outcomes.
Nor Azmaniza Azizam, Dilla Syadia Ab Latiff, Nor Intan Shamimi Abdul Aziz et al.· International journal of res...· 0 citations
Background: Artificial intelligence (AI) is increasingly integrated into nursing education; however, its use in pediatric nursing courses remains underexplored.
Aim: This study aimed to examine the use of artificial intelligence tools among nursing students enrolled in a pediatric nursing course and to identify homogenous student profiles based on their usage characteristics and theoretical adoption patterns.
Methods: This cross-sectional study included 241 nursing students enrolled in a pediatric nursing course. Data were collected using the AI in Pediatric Nursing Course Questionnaire and the General Attitude Toward Artificial Intelligence Scale (GAAIS). K-means cluster analysis was conducted, guided by Rogers’ Diffusion of Innovations Theory, to classify students into distinct profiles based on their GAAIS subscale scores, which were considered to reflect their fundamental attitudinal orientations toward AI.
Results: Cluster analysis identified three distinct student profiles: Optimists (n=95), Realists (n=102), and Traditionalists (n=44). Optimists demonstrated the highest levels of AI use, whereas Traditionalists demonstrated the lowest levels in both clinical practice (p=0.002) and preparation for clinical visits (p=0.039). Additionally, Optimists and Realists reported more positive perceptions of AI’s impact on examination success (p=0.002) and learning (p=0.001), whereas Traditionalists expressed significantly more negative attitudes toward AI overall (p<0.001) and reported the lowest levels of trust in the technology (p=0.040).
Conclusion: Optimists and Realists appear to actively integrate AI tools into clinical practice and examination preparation and generally perceive them as beneficial for learning outcomes. These findings highlight the importance of adopting differentiated pedagogical approaches rather than a one-size-fits-all curriculum.
Pelin Karataş, Demet Öztürk· Journal of Education and Res...· 0 citations
Generative artificial intelligence (GenAI) is increasingly explored as a cognitive tool with the potential to reshape learning practices in higher education. In professional disciplines such as nursing education, where experiential learning is essential, GenAI has the potential to enhance knowledge creation, interaction, reflection, and active engagement. Guided by Kolb’s experiential learning theory and organized by the Triple I Framework, we conceptualize individual factors with the Technology Acceptance Model (TAM) and Bandura’s self-efficacy theory, interpersonal factors with social support theory and Vygotsky’s social constructivism, and institutional factors with Rogers’ diffusion of innovations. While adopting a digital communication lens that treats GenAI as a communicative medium, this study examined multi-level factors influencing GenAI-supported experiential learning behavior. A cross-sectional survey was conducted with 300 undergraduate nursing students using the developed questionnaire. The data were analyzed using descriptive statistics, Pearson’s correlations, and multiple regression analysis. Results indicated that perceived usefulness, AI usage confidence, peer support, and institutional readiness significantly predicted this learning behavior, which together explained 67.1% of the variance. In contrast, perceived ease of use, family support, instructor support, and curriculum integration were not significant predictors. Theoretically, this study extends Kolb’s experiential learning theory by positioning GenAI technologies as cognitive tools in the experiential learning cycle. Practically, the findings highlight the importance of building AI confidence, fostering peer collaboration, and strengthening institutional readiness for the effective integration of GenAI into nursing education. Together, these insights advance educational communication scholarship by illustrating how emerging AI technologies are reshaping the communicative ecology of experiential learning.
Nualyai Pitsachart, Vitsanu Nittayathammakul, Tepin Craivanich et al.· Journal of Education and Tra...· 0 citations
BACKGROUND
Nursing educators play a pivotal role in the effective integration of artificial intelligence (AI) into nursing education. Investigating the heterogeneity of their AI literacy and its impact on attitude and behavior is a prerequisite for developing targeted interventions.
OBJECTIVE
This study aimed to identify latent profiles of AI literacy among nursing educators and to examine the mediating role of AI attitude in the relationship between literacy and behavior.
METHODS
A cross-sectional survey was conducted with 339 nursing educators in China. Latent profile analysis (LPA) was performed in Mplus to identify distinct AI literacy subgroups. Mediation analyses were performed using both variable-centered and person-centered approaches.
RESULTS
Three distinct AI literacy profiles were identified: foundational (12.4%), developing (47.5%), and proficient (40.1%). The variable-centered analysis revealed that AI attitude partially mediated the relationship between AI literacy and behavior. Person-centered analysis further indicated that this mediating effect was significant only for the proficient literacy profile, but not for the developing profile, highlighting subgroup-specific mechanisms in the literacy-behavior pathway.
CONCLUSION
The literacy-profile dependent mediation identified highlights differentiated literacy-attitude-behavior pathwaysand underscores the necessity of adopting tailored, profile-specific professional development strategies instead of one-size-fits-all approaches.
Hongrui Zhu, Zhishan Xie, Qinyan Wang et al.· Nurse Education Today· 0 citations