Nurse education in practice · 2026
Generative AI and intelligent tutoring systems in nursing education: A systematic review of impacts on higher-order thinking skills and clinical competency.
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Nasirun Hafizs, Mulyono Sigit, Hapsari Ayu Widowati Dwi, Wiradinata Hendri Hardi, Ratu Mustika
Аннотация
AIMS: This review synthesizes the impact of Artificial Intelligence (AI) on nursing students' clinical competency, Higher-Order Thinking Skills (HOTS) and educational outcomes to differentiate between procedural efficiency and deep cognitive retention.
BACKGROUND: Generative AI and Intelligent Tutoring Systems (ITS) offer personalized scaffolding. However, their comparative efficacy against conventional pedagogical approaches, such as face-to-face lectures and standard non-AI simulations, remains controversial, particularly regarding the potential trade-off between skill acquisition and cognitive depth.
DESIGN: A systematic literature review guided by PRISMA 2020 guidelines.
METHODS: A comprehensive search across seven databases (PubMed, EBSCOhost, Scopus, ScienceDirect, ProQuest, IEEE Xplore, JSTOR) from 2015 to 2025 identified 14 eligible studies (7 RCTs, 7 Quasi-experiments) which were synthesized across East Asia, Europe and Africa. Quality was appraised using JBI tools, with the protocol registered in PROSPERO.
RESULTS: Synthesis of 14 studies (n = 1107) reveals a critical divergence. AI interventions significantly improved psychomotor application, procedural skills and communication mechanics compared with traditional instructor-led teaching methods. However, findings on HOTS and retention were mixed; while GenAI enhanced inquiry-based problem solving, it occasionally compromised critical reflection and long-term knowledge retention compared with human-led instruction. Furthermore, a "gap in algorithmic empathy" was evident, with AI failing to effectively enhance cultural awareness.
CONCLUSION: AI functions as a robust "pedagogical scaffold" for procedural simulation but risks inducing "cognitive offloading" if used passively. Educators must adopt a Hybrid-Scaffolded Model: leveraging AI for iterative drills while prioritizing human facilitation for deep cognitive consolidation, ethics and cultural safety.
BACKGROUND: Generative AI and Intelligent Tutoring Systems (ITS) offer personalized scaffolding. However, their comparative efficacy against conventional pedagogical approaches, such as face-to-face lectures and standard non-AI simulations, remains controversial, particularly regarding the potential trade-off between skill acquisition and cognitive depth.
DESIGN: A systematic literature review guided by PRISMA 2020 guidelines.
METHODS: A comprehensive search across seven databases (PubMed, EBSCOhost, Scopus, ScienceDirect, ProQuest, IEEE Xplore, JSTOR) from 2015 to 2025 identified 14 eligible studies (7 RCTs, 7 Quasi-experiments) which were synthesized across East Asia, Europe and Africa. Quality was appraised using JBI tools, with the protocol registered in PROSPERO.
RESULTS: Synthesis of 14 studies (n = 1107) reveals a critical divergence. AI interventions significantly improved psychomotor application, procedural skills and communication mechanics compared with traditional instructor-led teaching methods. However, findings on HOTS and retention were mixed; while GenAI enhanced inquiry-based problem solving, it occasionally compromised critical reflection and long-term knowledge retention compared with human-led instruction. Furthermore, a "gap in algorithmic empathy" was evident, with AI failing to effectively enhance cultural awareness.
CONCLUSION: AI functions as a robust "pedagogical scaffold" for procedural simulation but risks inducing "cognitive offloading" if used passively. Educators must adopt a Hybrid-Scaffolded Model: leveraging AI for iterative drills while prioritizing human facilitation for deep cognitive consolidation, ethics and cultural safety.