이 페이지는 자동 번역되었으며 번역의 정확성을 보장하지 않습니다. 참조하십시오 영문판 원본 텍스트의 경우.

AI-Assisted Adaptive Simulation in Physiology Education (PBL)

2026년 5월 20일 업데이트: 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.

연구 개요

상태

완전한

정황

개입 / 치료

상세 설명

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.

연구 유형

중재적

등록 (실제)

672

단계

  • 해당 없음

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 장소

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

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 성인

건강한 자원 봉사자를 받아들입니다

예

설명

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

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

  • 주 목적: 기초 과학
  • 할당: 무작위
  • 중재 모델: 병렬 할당
  • 마스킹: 하나의

무기와 개입

참가자 그룹 / 팔
개입 / 치료
실험적: 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.
행동: 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.
다른 이름들:
  • Adaptive Screen-Based Simulation
  • AI-Assisted Adaptive Simulation
  • Rule-Based Adaptive Simulation
  • Adaptive Physiology Simulation Platform
활성 비교기: 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.
행동: 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.
다른 이름들:
  • Standard Curriculum-Based Teaching
  • Conventional Teaching
  • Didactic Physiology Education
  • Traditional Physiology Instruction

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
Physiological Reasoning Ability
기간: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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
기간: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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
기간: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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)

2차 결과 측정

결과 측정
측정값 설명
기간
Student Engagement
기간: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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
기간: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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
기간: Baseline (Week 1), post-intervention (Week 13), and follow-up (Week 17)
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)

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

스폰서

Saveetha University

간행물 및 유용한 링크

연구에 대한 정보 입력을 담당하는 사람이 자발적으로 이러한 간행물을 제공합니다. 이것은 연구와 관련된 모든 것에 관한 것일 수 있습니다.

일반 간행물

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (실제)

2025년 8월 1일

기본 완료 (실제)

2026년 1월 31일

연구 완료 (실제)

2026년 1월 31일

연구 등록 날짜

최초 제출

2026년 5월 15일

QC 기준을 충족하는 최초 제출

2026년 5월 20일

처음 게시됨 (실제)

2026년 5월 27일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 5월 27일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 5월 20일

마지막으로 확인됨

2026년 5월 1일

추가 정보

이 연구와 관련된 용어

키워드

기타 연구 ID 번호

  • 24/032/2025/SR/SIBMS

개별 참가자 데이터(IPD) 계획

개별 참가자 데이터(IPD)를 공유할 계획입니까?

아니요

IPD 계획 설명

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.

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

아니

이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .

구독하다