AI-Assisted Adaptive Simulation in Physiology Education (PBL)
20 mai 2026 mis à jour par: Jeevarathinam Thirumalai, Saveetha University
Effect of Adaptive AI-Supported Simulation on Physiology Learning Outcomes Among Medical Students: A Randomized Controlled Trial
This randomized controlled trial evaluated whether an AI-assisted, rule-based adaptive screen-based simulation module could improve physiology learning outcomes among undergraduate health science students compared with conventional instruction.
A total of 672 students from Physiotherapy, Occupational Therapy, Nursing, and Allied Health Sciences were randomly assigned in a 1:1 ratio to either the adaptive simulation group or the conventional teaching group.
The intervention used web-based clinical physiology cases with algorithm-supported case sequencing, automated formative feedback, and structured faculty-led debriefing, while the control group received standard lectures, textbook reading, tutorial sessions, and laboratory practicals.
The primary outcomes were physiological knowledge and reasoning ability, and the secondary outcomes were conceptual understanding, engagement, cognitive load, and academic self-efficacy.
Assessments were performed at baseline, immediately after the 12-week intervention, and again at four-week follow-up.
Aperçu de l'étude
Statut
Complété
Les conditions
Description détaillée
This study was designed as a prospective, two-arm, parallel-group randomized controlled trial with repeated-measures assessment at three time points: baseline, immediately post-intervention, and four weeks after the intervention. It was conducted at Saveetha Institute of Basic Medical Sciences, India, between August 2025 and January 2026, and received institutional ethical approval before enrollment. Participants were undergraduate health science students aged 18 to 25 years who were enrolled in a Human Physiology course and had access to an internet-enabled personal device. Students with prior formal exposure to simulation-based physiology instruction or adaptive digital learning platforms were excluded. After baseline assessment, participants were randomized in a 1:1 ratio to the intervention or control group, with allocation concealment and blinded outcome assessment.
The intervention group received physiology instruction through a screen-based adaptive simulation environment over 12 weeks. The module was intentionally designed as a bundled educational strategy integrating adaptive case sequencing, automated formative feedback, and faculty-led debriefing. The adaptive component used predefined rule-based logic to personalize learning by adjusting case difficulty and feedback pathways according to learner performance; it did not use autonomous generative artificial intelligence or clinical decision-making. Participants completed structured simulation sessions for two hours per week, including pre-briefing, individual case-based simulation, and facilitated debriefing. The control group received conventional curriculum-based physiology instruction over the same 12-week period, including didactic lectures, prescribed textbook readings, tutorial sessions, and laboratory practicals.
The study prioritized objective learning outcomes. Physiological knowledge was measured using a 40-item multiple-choice test, physiological reasoning ability using a scenario-based rubric-scored assessment, and conceptual understanding using a physiology concept inventory. Secondary outcomes included student engagement measured with the USEI, cognitive load measured with NASA-TLX, and academic self-efficacy measured with an adapted CASES scale. Outcomes were collected at baseline, post-intervention, and follow-up using the same instruments across all time points.
Type d'étude
Interventionnel
Inscription (Réel)
672
Phase
- N'est pas applicable
Contacts et emplacements
Cette section fournit les coordonnées de ceux qui mènent l'étude et des informations sur le lieu où cette étude est menée.
Lieux d'étude
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Tamil Nadu
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Chennai, Tamil Nadu, Inde, 602105
- Saveetha Institute of Basic Medical Sciences (SIBMS), Saveetha Institute of Medical and Technical Sciences (SIMATS)
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Critères de participation
Les chercheurs recherchent des personnes qui correspondent à une certaine description, appelée critères d'éligibilité. Certains exemples de ces critères sont l'état de santé général d'une personne ou des traitements antérieurs.
Critère d'éligibilité
Âges éligibles pour étudier
- Adulte
Accepte les volontaires sains
Oui
La description
Inclusion Criteria:
- Undergraduate students enrolled in Health Science programs including Physiotherapy, Occupational Therapy, Nursing, and Allied Health Sciences
- Registered for a Human Physiology course during the study period
- Age between 18 and 25 years
- Proficiency in English language
- Access to an internet-enabled personal device capable of supporting web-based educational applications
- Willingness to provide written informed consent for participation
Exclusion Criteria:
- Prior formal exposure to structured simulation-based physiology instruction
- Prior exposure to adaptive digital learning platforms related to physiology education
- Inability to access or use internet-enabled educational applications required for the intervention
- Declined or withdrew informed consent for participation
Plan d'étude
Cette section fournit des détails sur le plan d'étude, y compris la façon dont l'étude est conçue et ce que l'étude mesure.
Comment l'étude est-elle conçue ?
Détails de conception
- Objectif principal: Science basique
- Répartition: Randomisé
- Modèle interventionnel: Affectation parallèle
- Masquage: Seul
Armes et Interventions
Groupe de participants / Bras |
Intervention / Traitement |
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Expérimental: I-Assisted Adaptive Simulation Group
Participants received AI-assisted algorithm-supported adaptive screen-based physiology simulation over a 12-week period.
The intervention included adaptive case sequencing, automated formative feedback, interactive clinical reasoning activities, animated physiological visualization, and structured faculty-led debriefing sessions aligned with physiology curriculum objectives.
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The intervention consisted of an AI-assisted algorithm-supported adaptive screen-based physiology simulation delivered over 12 weeks.
Participants engaged in structured web-based simulation sessions involving interactive clinical case scenarios, animated physiological visualizations, adaptive case sequencing, automated formative feedback, and faculty-led debriefing.
The adaptive instructional system operated through predefined rule-based educational algorithms that adjusted case difficulty, feedback pathways, and learning progression according to participant performance within faculty-defined parameters.
Sessions included pre-briefing, individual simulation-based clinical reasoning activities, adaptive feedback, and reflective debriefing.
The intervention was implemented in alignment with the INACSL Healthcare Simulation Standards of Best Practice and focused on improving physiological knowledge, conceptual understanding, and clinical reasoning skills.
Autres noms:
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Comparateur actif: Conventional Instruction Group
Participants received standard curriculum-based physiology instruction over a 12-week period, including didactic lectures, prescribed textbook readings, faculty-guided tutorial sessions, and scheduled laboratory practicals covering core physiological systems.
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Participants received standard curriculum-based physiology instruction over a 12-week period according to institutional teaching guidelines.
Conventional instruction included didactic lectures, prescribed textbook readings, faculty-guided tutorial sessions, and scheduled laboratory practicals covering cardiovascular, respiratory, renal, neurological, endocrine, gastrointestinal, musculoskeletal, and integumentary physiology.
Tutorial sessions focused on instructor-led clarification of physiological concepts, small-group discussion, and question-and-answer interactions.
Laboratory practicals included supervised physiological measurements, observation of physiological demonstrations, interpretation of experimental findings, and guided analysis of physiological responses.
The control condition did not include adaptive simulation, automated formative feedback, algorithm-supported instructional adaptation, or structured simulation-based clinical reasoning activities.
Autres noms:
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Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
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Physiological Reasoning Ability
Délai: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Physiological reasoning ability was assessed using a scenario-based assessment requiring hypothesis generation, interpretation of physiological data, and application of physiological mechanisms to management decisions.
Responses were scored using a standardized four-point analytic rubric assessing reasoning and clinical interpretation skills.
Higher scores indicate better physiological reasoning ability.
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Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Physiological Knowledge
Délai: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Physiological knowledge was assessed using a faculty-developed 40-item multiple-choice assessment designed to evaluate conceptual understanding and applied physiological reasoning across eight core physiological systems, including cardiovascular, respiratory, renal, neurological, endocrine, gastrointestinal, musculoskeletal, and integumentary physiology.
Higher scores indicate better physiology knowledge performance.
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Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Conceptual Understanding
Délai: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Conceptual understanding was assessed using a faculty-developed Physiology Concept Inventory designed to evaluate deep conceptual understanding, integration of physiological mechanisms across systems, and identification of common physiological misconceptions.
Higher scores indicate better conceptual understanding of physiology concepts.
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Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Mesures de résultats secondaires
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
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Student Engagement
Délai: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Student engagement was assessed using the University Student Engagement Inventory (USEI), which evaluates behavioral, emotional, and cognitive dimensions of learner engagement.
Higher scores indicate greater learner engagement during physiology learning activities.
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Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Cognitive Load
Délai: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Cognitive load was assessed using the NASA Task Load Index (NASA-TLX), a multidimensional measure evaluating perceived cognitive workload and task demand during learning activities.
Higher scores indicate greater perceived cognitive workload.
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Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Academic Self-Efficacy
Délai: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Academic self-efficacy was measured using an adapted version of the College Academic Self-Efficacy Scale (CASES) to evaluate learner confidence in physiology-related academic tasks and simulation-based learning activities.
Higher scores indicate greater academic self-efficacy.
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Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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Collaborateurs et enquêteurs
C'est ici que vous trouverez les personnes et les organisations impliquées dans cette étude.
Parrainer
Publications et liens utiles
La personne responsable de la saisie des informations sur l'étude fournit volontairement ces publications. Il peut s'agir de tout ce qui concerne l'étude.
Publications générales
- Sim JJM, Rusli KDB, Seah B, Levett-Jones T, Lau Y, Liaw SY. Virtual Simulation to Enhance Clinical Reasoning in Nursing: A Systematic Review and Meta-analysis. Clin Simul Nurs. 2022 Aug;69:26-39. doi: 10.1016/j.ecns.2022.05.006. Epub 2022 Jun 15.
- Elven M, Welin E, Wiegleb Edstrom D, Petreski T, Szopa M, Durning SJ, Edelbring S. Clinical Reasoning Curricula in Health Professions Education: A Scoping Review. J Med Educ Curric Dev. 2023 Oct 25;10:23821205231209093. doi: 10.1177/23821205231209093. eCollection 2023 Jan-Dec.
- Parodis I, Andersson L, Durning SJ, Hege I, Knez J, Kononowicz AA, Lidskog M, Petreski T, Szopa M, Edelbring S. Clinical Reasoning Needs to Be Explicitly Addressed in Health Professions Curricula: Recommendations from a European Consortium. Int J Environ Res Public Health. 2021 Oct 25;18(21):11202. doi: 10.3390/ijerph182111202.
Dates d'enregistrement des études
Ces dates suivent la progression des dossiers d'étude et des soumissions de résultats sommaires à ClinicalTrials.gov. Les dossiers d'étude et les résultats rapportés sont examinés par la Bibliothèque nationale de médecine (NLM) pour s'assurer qu'ils répondent à des normes de contrôle de qualité spécifiques avant d'être publiés sur le site Web public.
Dates principales de l'étude
Début de l'étude (Réel)
1 août 2025
Achèvement primaire (Réel)
31 janvier 2026
Achèvement de l'étude (Réel)
31 janvier 2026
Dates d'inscription aux études
Première soumission
15 mai 2026
Première soumission répondant aux critères de contrôle qualité
20 mai 2026
Première publication (Réel)
27 mai 2026
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
27 mai 2026
Dernière mise à jour soumise répondant aux critères de contrôle qualité
20 mai 2026
Dernière vérification
1 mai 2026
Plus d'information
Termes liés à cette étude
Mots clés
Autres numéros d'identification d'étude
- 24/032/2025/SR/SIBMS
Plan pour les données individuelles des participants (IPD)
Prévoyez-vous de partager les données individuelles des participants (DPI) ?
NON
Description du régime IPD
Individual participant data (IPD) will not be publicly shared because the dataset contains institution-linked educational performance information and participant-level academic assessment data.
De-identified data may be considered for academic collaboration upon reasonable request to the corresponding author, subject to institutional ethical approval and data-sharing regulations.
Informations sur les médicaments et les dispositifs, documents d'étude
Étudie un produit pharmaceutique réglementé par la FDA américaine
Non
Étudie un produit d'appareil réglementé par la FDA américaine
Non
Ces informations ont été extraites directement du site Web clinicaltrials.gov sans aucune modification. Si vous avez des demandes de modification, de suppression ou de mise à jour des détails de votre étude, veuillez contacter register@clinicaltrials.gov. Dès qu'un changement est mis en œuvre sur clinicaltrials.gov, il sera également mis à jour automatiquement sur notre site Web .