ChatGPT-Driven Blended Teaching for Pain Management in Nursing Students: A Randomized Controlled Trial

NCT07552363 | Not Yet Recruiting

• 1 other identifier: IR.SBMU.PHARMACY.REC.1405.046
• Study Type: interventional
• Target: 156
• Locations: 1 country
• Sites: 1

Brief Summary

Pain management is a core competency in nursing practice, yet nursing students consistently demonstrate insufficient knowledge, unfavorable attitudes, limited competence, and low self-efficacy in this area. Artificial intelligence (AI)-based educational tools, particularly ChatGPT, have emerged as promising resources in nursing education; however, rigorous experimental evidence on their effectiveness remains scarce. This study is a two-arm, parallel-group randomized controlled trial (RCT) that aims to evaluate the effect of a ChatGPT-driven blended teaching model for pain management on nursing students' knowledge and attitudes toward pain, nursing competence, and learning self-efficacy. Eligible nursing students at Shahid Beheshti University of Medical Sciences (Tehran, Iran) will be randomly assigned in a 1:1 ratio to either:

• Intervention group: ChatGPT-assisted blended clinical nursing rounds (8 sessions over 4 weeks, each 90 minutes, combining bedside rounds with AI-assisted pre- and post-round activities)
• Control group: Traditional clinical nursing rounds (same number and duration of sessions, without any AI tools) Outcomes will be measured at baseline (1 week before intervention), immediate post-test (1 week after intervention), and 3-month follow-up using validated instruments: the Nurses' Knowledge and Attitudes Survey Regarding Pain (NKASRP), the Nursing Student Competence Scale (NSCS), and the Nursing Students' Learning Self-Efficacy instrument (NLSE). Findings will provide empirical evidence to guide educational policy and curriculum design in nursing programs, with the goal of improving pain management education and patient care outcomes.

Conditions

Pain Management; Nursing Education; Knowledge, Attitudes, Practice

Keywords

ChatGPT; Artificial Intelligence; Blended Learning; Pain Management; Nursing Students; Clinical Competence; Randomized Controlled Trial

Outcome Measures

Primary Outcomes (1)

• Knowledge and Attitudes Toward Pain

  Measured using the Nurses' Knowledge and Attitudes Survey Regarding Pain (NKASRP), a 39-item instrument comprising 22 true/false questions, 13 multiple-choice questions, and 2 case studies. Each correct answer scores 1 point (range: 0-39); results expressed as percentage of correct responses. Higher scores indicate better knowledge and attitudes toward pain management. A Persian forward-backward translation was performed, with face and content validity confirmed by a nursing faculty panel.

  Baseline (1 week before intervention), immediate post-test (1 week after intervention), and 3-month follow-up

Secondary Outcomes (2)

• Nursing Competence

  Baseline (1 week before intervention), immediate post-test (1 week after intervention), and 3-month follow-up

• Learning Self-Efficacy

  Baseline (1 week before intervention), immediate post-test (1 week after intervention), and 3-month follow-up

Study Arms (2)

ChatGPT-Driven Blended Teaching Group

EXPERIMENTAL

Participants receive a blended teaching intervention integrating ChatGPT-assisted instruction with traditional in-person clinical nursing rounds focused on pain management. The intervention consists of 8 sessions over 4 weeks (2 sessions per week), each approximately 90 minutes, comprising three phases: pre-round preparation (30 min) using standardized ChatGPT prompts and case-based learning; bedside nursing round (30 min) with patient assessment and instructor feedback; and post-round activities (30 min) using ChatGPT to resolve uncertainties and complete case reports. Students are organized in groups of 4-6 and rotate through medical, surgical, and chronic pain clinical departments.

Behavioral: ChatGPT-Driven Blended Teaching Model for Pain Management

Traditional Clinical Nursing Rounds Group

ACTIVE COMPARATOR

Participants receive traditional clinical nursing rounds without any ChatGPT or AI components. The instructor selects and introduces clinical cases; students review resources and prepare reports. During rounds, the instructor directs all activities, including case presentation, assessment, nursing diagnosis, intervention, and outcome evaluation. The control group receives an identical number and duration of sessions (8 sessions over 4 weeks, each approximately 90 minutes) in the same clinical departments to ensure exposure consistency.

Behavioral: Traditional Clinical Nursing Rounds

Interventions

ChatGPT-Driven Blended Teaching Model for Pain Management BEHAVIORAL

A blended teaching model integrating ChatGPT with in-person clinical nursing rounds for pain management education. Delivered over 4 weeks (8 sessions × 90 minutes). Each session includes: (1) pre-round preparation using standardized ChatGPT prompts for case analysis and evidence retrieval; (2) bedside nursing rounds with pain assessment, patient education, and instructor feedback; and (3) post-round activities using ChatGPT to resolve clinical uncertainties and complete case reports. All ChatGPT outputs were reviewed by supervising faculty for accuracy. Students used pre-designed, standardized prompts based on the WHO analgesic ladder and national clinical protocols.

Also known as: AI-Assisted Blended Clinical Nursing Rounds

ChatGPT-Driven Blended Teaching Group

Traditional Clinical Nursing Rounds BEHAVIORAL

Standard clinical nursing rounds without AI tools. The instructor directs all activities including case introduction, bedside assessment, nursing diagnosis, intervention planning, and outcome evaluation. Students primarily observe and respond to instructor questions. Sessions match the intervention group in number, duration, and clinical setting (8 sessions × 90 minutes over 4 weeks).

Traditional Clinical Nursing Rounds Group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Undergraduate nursing students in their fourth semester or higher, or master's or doctoral nursing students engaged in clinical training involving direct patient care
• Provision of electronic informed consent
• Access to the internet and a personal device (computer, tablet, or smartphone) for the asynchronous components of the blended teaching model
• No participation in a formal comprehensive pain management course within the previous 12 months

You may not qualify if:

• Inability to attend at least one face-to-face session or to complete online activities (e.g., due to repeated absences)
• Any self-reported or university-documented cognitive or mental health condition that prevented completion of questionnaires or participation in training
• Voluntary withdrawal at any stage of the study

Sponsors & Collaborators

• Shahid Beheshti University of Medical Sciences: lead

Study Sites (1)

Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences

Tehran, Tehran Province, Iran

Location

MeSH Terms

Conditions

Agnosia; Behavior

Interventions

Pain Management

Condition Hierarchy (Ancestors)

Perceptual Disorders; Neurobehavioral Manifestations; Neurologic Manifestations; Nervous System Diseases; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Therapeutics; Disease Management; Patient Care Management; Health Services Administration

Central Study Contacts

Sogand Sarmadi

CONTACT

+989198599908; sogand.sarmadi@ymail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Masking Details: Due to the nature of the educational intervention, blinding of participants and instructors was not feasible. Outcome assessors responsible for administering and scoring questionnaires, and the data analyst, remained blinded to group allocation throughout the study. All questionnaires were distributed using participant identification codes rather than names.
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Shahid Beheshti University of Medical Sciences

Model Details: Two-arm parallel-group design with 1:1 allocation ratio. Participants are randomly assigned to either a ChatGPT-driven blended teaching group or a traditional clinical nursing rounds group. Both groups receive the same number and duration of sessions (8 sessions over 4 weeks, each approximately 90 minutes) in the same clinical departments.

Study Record Dates

• First Submitted: April 20, 2026
• First Posted: April 27, 2026
• Study Start (Estimated): September 1, 2026
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): January 1, 2027
• Last Updated: April 27, 2026

Record last verified: 2026-04

Locations

IR (1)