Evidence map›Paper›PMID 41014093›Full record

ArticleEuropean journal of dental education : official journal of the Association for Dental Education in Europe2026

Evaluating Dental Students' Perspectives on Artificial Intelligence (AI)-Driven Large Language Models in Education in Saudi Arabia.

Khalifa S Al-Khalifa, Walaa Magdy Ahmed, Amr Ahmed Azhari, Amir I O Ibrahim, Reham S Al-Saljah, Ramy Moustafa Moustafa Ali, Sultan Ainoosah, Amal Alfaraj

Abstract read
In one paragraph

Article in European journal of dental education : official journal of the Association for Dental Education in Europe, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Evaluating Dental Students' Perspectives on Artificial Intelligence (AI)-Driven Large Language Models in Education in Saudi Arabia.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026
    Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Khalifa S Al-KhalifaDepartment of Preventive Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.ORCID https://orcid.org/0000-0001-8160-9288
Walaa Magdy AhmedDepartment of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.ORCID https://orcid.org/0000-0003-1810-8733
Amr Ahmed AzhariDepartment of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
Amir I O IbrahimDepartment of Restorative Dental Sciences, King Faisal University, College of Dentistry, Al Aahsa, Saudi Arabia.
Reham S Al-SaljahDepartment of Preventive Dental Sciences, King Faisal University, College of Dentistry, Al Aahsa, Saudi Arabia.
Ramy Moustafa Moustafa AliDepartment of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia.
Sultan AinoosahDepartment of Substitutive Dental Science, College of Dentistry, Taibah University, Madinah, Saudi Arabia.
Amal AlfarajDepartment of Prosthodontics, King Faisal University, College of Dentistry, Al Aahsa, Saudi Arabia.ORCID https://orcid.org/0000-0002-9892-2358

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study explores the perspectives of dental students in Saudi Arabia regarding the integration of large language models (LLMs) in dental education. It aims to understand their familiarity, utilisation and perceptions of these tools, while addressing the potential benefits, risks, and ethical considerations associated with their use.

methodsA cross-sectional survey was conducted between January and March 2024, involving 1370 dental students from various institutions across Saudi Arabia. The survey included multiple-choice questions and Likert scale items, assessing familiarity, usage patterns, and perceptions of LLMs. Statistical analyses were performed to identify significant associations between demographic variables and students' familiarity, utilisation, and perceptions of LLMs.

resultsThe survey revealed broad familiarity with LLMs, with 58.1% of participants being aware of their capabilities. Usage patterns varied, with high engagement in tools like ChatGPT and Google Translate. Significant gender, educational level and regional differences were observed, with males and postgraduate residents showing higher familiarity and utilisation rates. However, ethical concerns, including the potential for plagiarism and academic dishonesty, were prevalent, with only 15% of students reporting that their institutions had specific guidelines for LLM use.

conclusionThe study highlights the need for a controlled and ethically informed approach to integrating LLMs into dental education. While LLMs offer potential benefits, their use must be regulated to prevent misuse and ensure that educational outcomes are enhanced rather than compromised. Institutions should develop clear guidelines, provide ethical training and emphasise the importance of critical evaluation when using LLMs.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelEducation, DentalLarge Language ModelsStudents, DentalAdultCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansMaleSaudi ArabiaSurveys and Questionnairesartificial intelligencedental educationethicsplagiarism

Identifiers

PMID41014093
PMCPMC13383361

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.