Clinical Reasoning in Nursing EducationNCT07838792
Theory-Guided Socratic AI Scaffolding for Clinical Reasoning in Nursing Students
A study testing Tanner-Structured Socratic AI Scaffolding and General-Purpose Generative AI in people with Clinical Reasoning in Nursing Education, run by Chang Gung University of Science and Technology at 1 site in Taiwan.
Trial summary
The goal of this study was to compare two approaches to using generative artificial intelligence (AI) to support clinical reasoning in undergraduate nursing students. The study examined whether a theory-guided Socratic AI scaffold based on Tanner's Clinical Judgment Model could better support clinical reasoning… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Clinical Reasoning in Nursing Education
- What is being tested?
- Tanner-Structured Socratic AI Scaffolding and General-Purpose Generative AI
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 127 participants
- Where
- 1 siteTaiwan
- Study dates
- 15 Sep 2025to 5 Feb 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Undergraduate nursing students enrolled in the required Pediatric Nursing course.
- Completion of core medical-surgical nursing courses.
- No prior practicum experience in specialized pediatric units.
Who cannot join
- Students repeating the Pediatric Nursing course.
- Students with prior pediatric specialty rotation experience.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Effects of Integrating a Reasoning Grid and ChatGPT on Clinical Reasoning in Nursing Students
The goal of this study was to compare two approaches to using generative artificial intelligence (AI) to support clinical reasoning in undergraduate nursing students. The study examined whether a theory-guided Socratic AI scaffold based on Tanner's Clinical Judgment Model could better support clinical reasoning, case-based knowledge, and confidence than the naturalistic use of general-purpose generative AI.
Participants were undergraduate nursing students enrolled in a pediatric nursing course. Before the intervention, students' perceived barriers to clinical reasoning were identified and used to inform the theory-guided AI scaffold. Classes were then assigned to either Tanner-Structured Socratic AI Scaffolding or General-Purpose Generative AI. Both groups worked with the same pediatric fever case for the same amount of time.
Read more
Students in the Tanner-Structured Socratic AI Scaffolding group received step-by-step guidance through Noticing, Interpreting, Responding, and Reflecting using Socratic questions, hints, feedback, and prompts for reflection. Students in the General-Purpose Generative AI group used freely available generative AI tools as they normally would for learning.
The study compared the two groups on clinical reasoning performance, case-based knowledge, and confidence in clinical reasoning.
- Conditions
- Clinical Reasoning in Nursing Education
- Being tested
- Tanner-Structured Socratic AI Scaffolding, General-Purpose Generative AI
- Keywords
- clinical reasoning, nursing education, Generative Artificial Intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Tanner-Structured Socratic AI ScaffoldingOther
Participants used a research-team-developed AI chatbot structured around Tanner's Clinical Judgment Model. The chatbot guided participants sequentially through Noticing, Interpreting, Responding, and Reflecting using Socratic questions, graduated hints, metacognitive prompts, and constructive feedback rather than providing direct answers. The scaffolding was informed by learner-identified barriers to clinical reasoning.
General-Purpose Generative AIOther
Participants used freely accessible general-purpose generative AI tools as they normally would for learning. They formulated their own task-focused queries and received no Tanner-structured sequence or standardized Socratic prompts.
| Study group | Role | What they receive |
|---|---|---|
| Tanner-Structured Socratic AI Scaffolding | Experimental | Participants used a theory-guided AI chatbot structured around Tanner's Clinical Judgment Model. The chatbot guided participants through Noticing, Interpreting, Responding, and Reflecting using Socratic questions, graduated hints, metacognitive prompts, and constructive feedback rather than providing direct answers. Receives: Other: Tanner-Structured Socratic AI Scaffolding |
| General-Purpose Generative AI | Active comparator | Participants used freely accessible general-purpose generative AI tools as they normally would for learning. They formulated their own task-focused queries without a Tanner-structured sequence or standardized Socratic prompts. Receives: Other: General-Purpose Generative AI |
What is being measured
The primary outcome is the main question the study is designed to answer.
Clinical Reasoning Performance
Clinical reasoning performance was assessed using the Clinical Reasoning Performance Rubric (CRPR), a 15-point criterion-referenced rubric. Participants identified three priority nursing problems (0-3 points), ranked them in order of urgency (0-3 points), and proposed three appropriate nursing interventions for each problem (0-9 points). Total scores ranged from 0 to 15, with higher scores indicating better clinical reasoning performance.
Measured: Immediately after the intervention
Secondary outcomes (2)
Case-Based Knowledge
Case-based knowledge was assessed using a five-item, scenario-based, single-best-answer knowledge test covering infant fever management across five domains: assessment, problem identification, goal setting, intervention, and evaluation. Each correct answer was awarded 1 point, and incorrect answers received 0 points. Total scores ranged from 0 to 5, with higher scores indicating greater case-based knowledge.
Measured: Baseline and immediately after the intervention
Clinical Reasoning Confidence
Clinical reasoning confidence was assessed using a single-item, self-reported 5-point rating scale measuring participants' confidence in clinical reasoning and decision-making related to a pediatric fever case. Scores ranged from 1 (very low confidence) to 5 (very high confidence), with higher scores indicating greater clinical reasoning confidence.
Measured: Baseline and immediately after the intervention
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Sep 2026
- Study start15 Sep 2025
- Primary completion5 Feb 2026
- Study completion5 Feb 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Clinical Reasoning in Nursing EducationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Investigator, Outcomes assessor
- Enrolment
- 127 participants (actual)
Sponsor and investigators
- Lead sponsor
- Chang Gung University of Science and Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ying-Mei Liu, Chang Gung University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07838792
- Sponsor's protocol ID
- 202401895B0
- Status last verified
- Oct 2026
- First submitted
- 18 Sep 2026
- Last updated
- 22 Oct 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effects of Integrating a Reasoning Grid and ChatGPT on Clinical Reasoning in Nursing Students
- ClinicalTrials.gov ID
- NCT07838792
- Acronym / sponsor ID
- 202401895B0
- Conditions
- Clinical Reasoning in Nursing Education
- Keywords
- clinical reasoning, nursing education, Generative Artificial Intelligence
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 127 participants (actual)
- Lead sponsor
- Chang Gung University of Science and TechnologyUniversity, hospital or other
- Study start
- 15 Sep 2025 (actual)
- Primary completion
- 5 Feb 2026 (actual)
- Study completion
- 5 Feb 2026 (actual)
- First posted
- 24 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The goal of this study was to compare two approaches to using generative artificial intelligence (AI) to support clinical reasoning in undergraduate nursing students. The study examined whether a theory-guided Socratic AI scaffold based on Tanner's Clinical Judgment Model could better support clinical reasoning, case-based knowledge, and confidence than the naturalistic use of general-purpose generative AI.
Participants were undergraduate nursing students enrolled in a pediatric nursing course. Before the intervention, students' perceived barriers to clinical reasoning were identified and used to inform the theory-guided AI scaffold. Classes were then assigned to either Tanner-Structured Socratic AI Scaffolding or General-Purpose Generative AI. Both groups worked with the same pediatric fever case for the same amount of time.
Students in the Tanner-Structured Socratic AI Scaffolding group received step-by-step guidance through Noticing, Interpreting, Responding, and Reflecting using Socratic questions, hints, feedback, and prompts for reflection. Students in the General-Purpose Generative AI group used freely available generative AI tools as they normally would for learning.
The study compared the two groups on clinical reasoning performance, case-based knowledge, and confidence in clinical reasoning.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (investigator, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Tanner-Structured Socratic AI Scaffolding | Experimental | Other: Tanner-Structured Socratic AI ScaffoldingParticipants used a theory-guided AI chatbot structured around Tanner's Clinical Judgment Model. The chatbot guided participants through Noticing, Interpreting, Responding, and Reflecting using Socratic questions, graduated hints, metacognitive prompts, and constructive feedback rather than providing direct answers. |
| General-Purpose Generative AI | Active comparator | Other: General-Purpose Generative AIParticipants used freely accessible general-purpose generative AI tools as they normally would for learning. They formulated their own task-focused queries without a Tanner-structured sequence or standardized Socratic prompts. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (3 listed)
- Undergraduate nursing students enrolled in the required Pediatric Nursing course.
- Completion of core medical-surgical nursing courses.
- No prior practicum experience in specialized pediatric units.
Key exclusion criteria (2 listed)
- Students repeating the Pediatric Nursing course.
- Students with prior pediatric specialty rotation experience.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Tanner-Structured Socratic AI ScaffoldingParticipants used a research-team-developed AI chatbot structured around Tanner's Clinical Judgment Model. The chatbot guided participants sequentially through Noticing, Interpreting, Responding, and Reflecting using Socratic questions, graduated hints, metacognitive prompts, and constructive feedback rather than providing direct answers. The scaffolding was informed by learner-identified barriers to clinical reasoning. | Other | Tanner-Structured Socratic AI Scaffolding |
| General-Purpose Generative AIParticipants used freely accessible general-purpose generative AI tools as they normally would for learning. They formulated their own task-focused queries and received no Tanner-structured sequence or standardized Socratic prompts. | Other | General-Purpose Generative AI |
Primary outcome
Clinical Reasoning Performance
Time frame: Immediately after the intervention
Clinical reasoning performance was assessed using the Clinical Reasoning Performance Rubric (CRPR), a 15-point criterion-referenced rubric. Participants identified three priority nursing problems (0-3 points), ranked them in order of urgency (0-3 points), and proposed three appropriate nursing interventions for each problem (0-9 points). Total scores ranged from 0 to 15, with higher scores indicating better clinical reasoning performance.
2 secondary outcomes
Case-Based Knowledge
Time frame: Baseline and immediately after the intervention
Clinical Reasoning Confidence
Time frame: Baseline and immediately after the intervention
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Oct 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Tanner-Structured Socratic AI Scaffolding and General-Purpose Generative AI in people with Clinical Reasoning in Nursing Education, run by Chang Gung University of Science and Technology at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 22 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinical Reasoning Performance (time frame: Immediately after the intervention). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Oct 2026.