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Completed

NCT Number: NCT06942130

The Application of Artificial Intelligence in Dental Education

Clinical procedural skills training is a cornerstone of preclinical education for dental students. While technological aids such as virtual reality and simulators are increasingly integrated into training, direct instructor guidance remains fundamental to skill development. Artificial intelligence（AI）， as an emerging technology, is now gradually being adopted in medical education. This medical education trial, aimed at evaluating the potential of artificial intelligence in dental education, seeks to address two key questions: 1) Can AI assistance enhance the skill performance of dental students? 2) How can AI be effectively integrated into modern medical education? Researchers will compare the effects of AI and video-based instruction on skill acquisition, assessing differences in skill performance outcomes. Participants will engage in skill acquisition using either AI or video-based instruction over a one-week period.

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Key information

Conditions

Medical Education

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

School & Hospital of Stomatology, Wuhan University

Wuhan, Hubei, 430079, China

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • They must have completed courses in endodontics, periodontology, prosthodontics, and oral and maxillofacial surgery; participants should have little to no prior training in the skills related to the experiment; and they must voluntarily agree to participate in the study and sign an informed consent form.

Exclusion criteria

  • Participants will be excluded if they meet any of the following criteria: failure in final exams of the four courses (endodontics, periodontology, prosthodontics, and oral and maxillofacial surgery); transfer to another major, suspension, or withdrawal from the current academic year; refusal to join the study or withdrawal midway; failure to meet the calibration standards in the 9-point eye-tracking calibration; use of OpenAI-related software or applications in the control group; or failure to complete the skill test.

Treatment and study plan

Artificial intelligence

Other

Incorporating artificial intelligence as an aid to video-based skill learning

video

Other

Skill learning solely through videos

Primary outcomes

  1. Operational test scores

    Time frame: through study completion, an average 1 week

    Operational testing is a common method used to measure participants' proficiency in skill operation. At the conclusion of the experiment, participants completed a 15-minute operational test in a desktop VR environment. Scores were automatically calculated by the VR system, ensuring objectivity in the results.

  2. spatial ability

    Time frame: Baseline

    Spatial ability, also known as visual-spatial ability (VSA) encompasses critical cognitive functions that enable individuals to understand, interpret, and manipulate spatially related information. The Purdue Spatial Visualization Test: Rotations (PSVT: R) - served as the measurement tool for analyzing subjects' mental rotation skills. This standardized evaluation contains 30 items that measure how effectively individuals can manipulate imagined three-dimensional figures. Each question includes an example and five choices, with only one being correct. Participants were tasked to rotate the given figure mentally in a similar manner as it was rotated in the example and to select the appropriate choice.

  3. cognitive load

    Time frame: through study completion, an average 1 week

    Cognitive load refers to the resources required by working memory for a particular task. Eye-tracking technology has gained increasing popularity in investigating learners' visual attention distribution and cognitive load. As one of the key indicators, pupil diameter has been used to measure cognitive load during learning. In this study, we used eye-tracking devices to record the pupil diameter of participants during learning to quantify cognitive load.

Secondary outcomes

  1. learning motivation

    Time frame: through study completion, an average 1 week

    Motivation is a motivational tendency guiding and sustaining a student's learning behavior toward specific academic goals.This study employed questionnaires to measure it. The questionnaire contains five items focusing on the motivation domain . Each item was coded according to a five-point Likert rating scale (1 = "strongly disagree", 2 = "disagree", 3 = "neutral", 4 = "agree", 5 = "strongly agree").

  2. self-efficacy

    Time frame: through study completion, an average 1 week

    Self-efficacy, a concept first developed by Bandura, refers to people's subjective judgments regarding their ability to successfully perform an achievement behavior. The questionnaire contains five items focusing on the motivation domain . Each item was coded according to a five-point Likert rating scale (1 = "strongly disagree", 2 = "disagree", 3 = "neutral", 4 = "agree", 5 = "strongly agree").

Sponsors and collaborators

Lead sponsor

Hospital of Stomatology, Wuhan University

Other

Registry information

Official study title

Model Construction for AI-assisted Teaching

Important dates

Study start
2024
Primary completion
2024
Study completion
2024
First posted
Apr 24, 2025
Registry last updated
Apr 24, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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