AI-Assisted Adaptive Simulation in Physiology Education (PBL)

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.

Study Overview

• Status: Completed
• Conditions: Health Professions Education; Simulation-Based Learning; Physiology Education; v; Artificial Intelligence in Education
• Intervention / Treatment: Behavioral: AI-Assisted Algorithm-Supported Adaptive Simulation; Behavioral: Conventional Physiology Instruction

Detailed Description

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.

• Study Type: Interventional
• Enrollment (Actual): 672
• Phase: Not Applicable

Contacts and Locations

Study Locations

• India
  • Tamil Nadu
    • Chennai, Tamil Nadu, India, 602105
      • Saveetha Institute of Basic Medical Sciences (SIBMS), Saveetha Institute of Medical and Technical Sciences (SIMATS)

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: Yes

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Basic Science
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: 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.	Behavioral: AI-Assisted Algorithm-Supported Adaptive Simulation; 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.; Other Names: Adaptive Screen-Based Simulation; AI-Assisted Adaptive Simulation; Rule-Based Adaptive Simulation; Adaptive Physiology Simulation Platform
Active Comparator: 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.	Behavioral: Conventional Physiology Instruction; 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.; Other Names: Standard Curriculum-Based Teaching; Conventional Teaching; Didactic Physiology Education; Traditional Physiology Instruction

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Physiological Reasoning Ability	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.	Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
Physiological Knowledge	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.	Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
Conceptual Understanding	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.	Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Student Engagement	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.	Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
Cognitive Load	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.	Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
Academic Self-Efficacy	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.	Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)

Collaborators and Investigators

• Sponsor: Saveetha University

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (Actual): August 1, 2025
• Primary Completion (Actual): January 31, 2026
• Study Completion (Actual): January 31, 2026

Study Registration Dates

• First Submitted: May 15, 2026
• First Submitted That Met QC Criteria: May 20, 2026
• First Posted (Actual): May 27, 2026

Study Record Updates

• Last Update Posted (Actual): May 27, 2026
• Last Update Submitted That Met QC Criteria: May 20, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: Educational technology; Clinical reasoning; Cognitive load; AI-assisted adaptive simulation; Screen-based simulation; Physiology learning; Undergraduate health sciences
• Other Study ID Numbers: 24/032/2025/SR/SIBMS

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: 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.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No