Zhang Hui, Yang Zifei, Zhao Yi, et al. Exploration of integrating artificial intelligence with thinking-guided teaching in the physical examination component of the Health Assessment course[J]. Chinese Journal of Medical Education Research, 2026, 25(4): 417-423.
Zhang Hui, Yang Zifei, Zhao Yi, et al. Exploration of integrating artificial intelligence with thinking-guided teaching in the physical examination component of the Health Assessment course[J]. Chinese Journal of Medical Education Research, 2026, 25(4): 417-423. DOI: 10.3760/cma.j.cn116021-20251130-02251.
Exploration of integrating artificial intelligence with thinking-guided teaching in the physical examination component of the Health Assessment course
To explore the effects of a generative artificial intelligence (AI) combined with thinking-guided teaching approach on nursing student learning outcomes and thinking abilities in the physical examination component of the Health Assessment course.
Methods
2
A quasi-experimental design was adopted to involve a total of 136 students in two existing classes. Based on the arrangement of teaching resources
one class (
n
=69) received the generative AI combined with thinking-guided teaching approach
while the other class (
n
=67) received conventional instruction. Data were collected using a Health Assessment theoretical knowledge test
a physical examination skills assessment checklist
a learning outcome quality evaluation form for physical examination
and a shortened version of the Critical Thinking Disposition Inventory. Statistical analyses were performed using SPSS 27.0.
Results
2
①The total score for learning outcome quality in the generative AI combined with thinking-guided teaching group was significantly higher than that in the conventional teaching group (74.13±9.18 vs. 66.80±11.91
t
=2.91
P
=0.005). Moreover
the generative AI combined with thinking-guided teaching group scored higher than the conventional teaching group in the dimensions of process integrity and accuracy (14.78±0.54 vs. 13.66±2.21
t
=2.92
P
=0.006)
understanding of clinical significance (14.08±1.59 vs. 13.03±2.61
t
=2.05
P
=0.045)
summarization and extension (12.97±1.98 vs. 11.57±2.77
t
=2.46
P
=0.016)
and application and analysis (11.97±2.81 vs. 10.11±3.59
t
=2.43
P
=0.018). ②The physical examination skills score of the generative AI combined with thinking-guided teaching group was significantly higher than that of the conventional teaching group (93.76±3.64 vs. 91.77±6.29
t
=2.04
P
=0.045). However
there was no statistically significant difference in theoretical knowledge scores between the two groups (84.38±7.87 vs. 82.48±9.07
t
=1.30
P
=0.194). ③No statistically significant differences were observed in the total score of critical thinking or its dimension-specific scores between the two groups (123.94±11.62 vs. 124.22±12.44
t
=-1.53
P
=0.130).
Conclusions
2
The generative AI combined with thinking-guided intervention promoted the teaching outcomes of the physical examination component of the Health Assessment course. However
the teaching value of AI as a teaching-assistant tool to optimize the learning process still needs to be validated in more courses.
关键词
Keywords
references
Sharma M , Sharma S . A holistic approach to remote patient monitoring, fueled by ChatGPT and metaverse technology: the future of nursing education [J ] . Nurse Educ Today , 2023 , 131 : 105972 . DOI: 10.1016/j.nedt.2023.105972 http://dx.doi.org/10.1016/j.nedt.2023.105972 .
Lin MY , Huang MZ , Lai PC . Effect of virtual reality training on clinical skills of nursing students: a systematic review and meta-analysis of randomized controlled trials [J ] . Nurse Educ Pract , 2024 , 81 : 104182 . DOI: 10.1016/j.nepr.2024.104182 http://dx.doi.org/10.1016/j.nepr.2024.104182 .
Chen PJ , Liou WK . ChatGPT-driven interactive virtual reality communication simulation in obstetric nursing: a mixed-methods study [J ] . Nurse Educ Pract , 2025 , 85 : 104383 . DOI: 10.1016/j.nepr.2025.104383 http://dx.doi.org/10.1016/j.nepr.2025.104383 .
Srinivasan M , Venugopal A , Venkatesan L , et al . Navigating the pedagogical landscape: exploring the implications of AI and chatbots in nursing education [J ] . JMIR Nurs , 2024 , 7 : e52105 . DOI: 10.2196/52105 http://dx.doi.org/10.2196/52105 .
Alenazi L , Alhalal E . Factors affecting artificial intelligence usage intention among nursing students: unified theory of acceptance and use of technology [J ] . Nurse Educ Today , 2025 , 152 : 106780 . DOI: 10.1016/j.nedt.2025.106780 http://dx.doi.org/10.1016/j.nedt.2025.106780 .
Tam W , Huynh T , Tang A , et al . Nursing education in the age of artificial intelligence powered chatbots (AI-chatbots): are we ready yet [J ] . Nurse Educ Today , 2023 , 129 : 105917 . DOI: 10.1016/j.nedt.2023.105917 http://dx.doi.org/10.1016/j.nedt.2023.105917 .
Gordon M , Daniel M , Ajiboye A , et al . A scoping review of artificial intelligence in medical education: BEME Guide No. 84 [J ] . Med Teach , 2024 , 46 ( 4 ): 446 - 470 . DOI: 10.1080/0142159X.2024.2314198 http://dx.doi.org/10.1080/0142159X.2024.2314198 .
Wang X , Dong L , Lyu W , et al . Teaching health assessment symptomatology using a flipped classroom combined with scenario simulation [J ] . J Nurs Educ , 2020 , 59 ( 8 ): 448 - 452 . DOI: 10.3928/01484834-20200723-05 http://dx.doi.org/10.3928/01484834-20200723-05 .
Cucci F , Marasciulo D , Romani M , et al . The contribution of artificial intelligence in nursing education: a scoping review of the literature [J ] . Nurs Rep , 2025 , 15 ( 8 ): 283 . DOI: 10.3390/nursrep15080283 http://dx.doi.org/10.3390/nursrep15080283 .
Jembu JP , Lee S . The effectiveness of artificial intelligence (AI) in learning outcomes of nursing students: a meta-analysis of randomized controlled trials (RCTs) [J ] . Steme , 2025 , 5 ( 4 ): 546 - 563 . DOI: 10.3934/steme.2025026 http://dx.doi.org/10.3934/steme.2025026 .
Khlaif ZN , Salameh N , Ajouz M , et al . Using generative AI in nursing education: students' perceptions [J ] . BMC Med Educ , 2025 , 25 ( 1 ): 926 . DOI: 10.1186/s12909-025-07416-z http://dx.doi.org/10.1186/s12909-025-07416-z .
Preiksaitis C , Rose C . Opportunities, challenges, and future directions of generative artificial intelligence in medical education: scoping review [J ] . JMIR Med Educ , 2023 , 9 : e48785 . DOI: 10.2196/48785 http://dx.doi.org/10.2196/48785 .
Allen KA , Costner-Lark A , Serratt T , et al . Artificial intelligence in advanced practice nursing education: opportunities and challenges [J ] . J Am Assoc Nurse Pract , 2026 , 38 ( 2 ): 102 - 107 . DOI: 10.1097/JXX.0000000000001169 http://dx.doi.org/10.1097/JXX.0000000000001169 .
Effectiveness evaluation of a constructivism-based three-stage human-artificial intelligence collaborative teaching model in psychological nursing practice education
Advances in application of data intelligence technologies in nursing education
Construction and practice of a teaching model for Geriatric Nursing based on metacognitive strategy
Application of checklist teaching method in nephrology nursing teaching
PDCA-guided Nursing Management course design and reform
Related Author
Yang Haoshuang
Liu Qin
Zhang Lihua
Liu Na
Zhang Lanfang
Che Jie
Hua Yan
Zhao Yanhao
Related Institution
Department of Nursing, Air Force Medical University
Teaching and Research Office of Geriatric Nursing, School of Nursing, Sun Yat-sen University
Department of Nephrology, The First Affiliated Hospital of Chongqing Medical University
Department of Nursing, The First Affiliated Hospital of Chongqing Medical University
Department of Nursing Management, School of Nursing, Army Medical University