AI-Assisted Adaptive Simulation in Physiology Education

NCT07608315 | Completed DMC

• 1 other identifier: 24/032/2025/SR/SIBMS
• Study Type: interventional
• Target: 672
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Physiology Education; Simulation-Based Learning; v; Artificial Intelligence in Education; Health Professions Education

Keywords

AI-assisted adaptive simulation; Screen-based simulation; Physiology learning; Clinical reasoning; Educational technology; Undergraduate health sciences; Cognitive load

Outcome Measures

Primary Outcomes (3)

• 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 Outcomes (3)

• Student Engagement

  Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)

• Cognitive Load

  Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)

• Academic Self-Efficacy

  Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)

Study Arms (2)

I-Assisted Adaptive Simulation Group

EXPERIMENTAL

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

Conventional Instruction Group

ACTIVE COMPARATOR

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

Interventions

AI-Assisted Algorithm-Supported Adaptive Simulation BEHAVIORAL

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.

Also known as: Adaptive Screen-Based Simulation, AI-Assisted Adaptive Simulation, Rule-Based Adaptive Simulation, Adaptive Physiology Simulation Platform

I-Assisted Adaptive Simulation Group

Conventional Physiology Instruction BEHAVIORAL

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.

Also known as: Standard Curriculum-Based Teaching, Conventional Teaching, Didactic Physiology Education, Traditional Physiology Instruction

Conventional Instruction Group

Eligibility Criteria

• Age: 18 Years - 25 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Saveetha University: lead

Study Sites (1)

Saveetha Institute of Basic Medical Sciences (SIBMS), Saveetha Institute of Medical and Technical Sciences (SIMATS)

Chennai, Tamil Nadu, 602105, India

Location

Related Publications (3)

• 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.

  PMID: 35754937 BACKGROUND

• 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.

  PMID: 37900617 BACKGROUND

• 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.

  PMID: 34769721 BACKGROUND

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Masking Details: Outcome assessors and data analysts were blinded to group allocation throughout the study. Allocation concealment was maintained using sequentially numbered, opaque, sealed envelopes prepared by an independent researcher not involved in recruitment or assessment.
• Purpose: BASIC SCIENCE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: May 15, 2026
• First Posted: May 27, 2026
• Study Start: August 1, 2025
• Primary Completion: January 31, 2026
• Study Completion: January 31, 2026
• Last Updated: May 27, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

IN (1)