Evidence map›Paper›PMID 42802725›Full record

ArticleF1000Research2026

Effectiveness of a Mobile Application with an AI-Powered Chatbot to Enhance Nursing Students' Awareness of Substance Abuse Prevention: An Interventional Study.

Hasan Abualruz, Eman A Shokr, Salahdein Aburuz

Abstract read
In one paragraph

Article in F1000Research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Hasan AbualruzAl -Zaytoonah University of Jordan, Faculty of Nursing, Amman, Jordan, Amman, Jordan.ORCID https://orcid.org/0000-0003-1523-6772
Eman A ShokrAl -Zaytoonah University of Jordan, Faculty of Nursing, Amman, Jordan, Amman, Jordan.ORCID https://orcid.org/0000-0002-3035-7073
Salahdein AburuzUnited Arab Emirates University, Abu Dhabi, 00000, United Arab Emirates.ORCID https://orcid.org/0000-0002-2478-3914

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Substance abuse among university students has emerged as a significant problem from a public health perspective, with specific patterns observed among the population in Jordan. Chatbots developed using artificial intelligence are effective instruments in health education delivery. This research sought to investigate the efficacy of a mobile app that incorporates AI chatbots in enhancing nursing students' knowledge about substance abuse prevention. Methods: This study employed a quasi-experimental nonequivalent control group pretest-posttest design, involving of an intervention group and a control group. The study was conducted in the largest Faculty of Nursing in Jordan with more than 2500 students' capacity, from October 2025 to February 2026. A total of 170 nursing students participated in the study; 85 students were allocated to the control group, and 85 students were assigned to the intervention group. The main study variables included Knowledge, Attitude, and Practice (KAP) domain scores, as well as Substance Use Risk Profile Scale (SURPS) scores. Results: The study results revealed that groups did not differ significantly from each other in terms of demographic variables. In the control group, there were no statistically significant changes found on all study variables, before and after the intervention. Conversely, in the experimental group, there were statistically significant changes found on the students' knowledge, attitude, and practice concerning the prevention of substance abuse (p < .001) and lower scores for all SURPS subscales p < .001. Sub-group analyses also found no statistically significant differences between the sub-groups in their responses to the intervention program (all p > .05). Multiple regression analysis also found that KAP predicted post-test SURP scores (p < .001). Conclusions: The AI-based chatbot intervention resulted in significant positive changes in substance abuse knowledge, attitudes, and preventive behaviors, as well as in acceptance of personality risks.

Indexed as

Health Knowledge, Attitudes, PracticeMobile ApplicationsStudents, NursingSubstance-Related DisordersAdultFemaleHumansMaleYoung AdultSubstance abuse prevention; AI chatbot; mobile health; nursing students; Knowledge; Attitude; Practice.

Identifiers

PMID42802725
PMCPMC13615354

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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 OpenQuestion graph.