Effectiveness of a Mobile Application with an AI-Powered Chatbot to Enhance Nursing Students' Awareness of Substance Abuse Prevention: An Interventional Study [version 2; peer review: 2 approved]

Дата публикации: 17-09-2026 12:00:40

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

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Introduction

Substance use disorder impose a significant burden on individuals, families, and healthcare systems globally. These disorders not only have immediate physiological effects, but also result in a range of negative consequences such as academic difficulties, deteriorating mental health, weakened social ties, and long-term economic challenges.¹^,² Late adolescence and early adulthood is an important phase during which individuals are vulnerable, given that the brain mechanisms governing impulse regulation and reward mechanisms are yet to fully develop, thus making individuals more vulnerable to the rewards associated with substance abuse.³ This situation has become especially serious in Jordan, given that research evidence indicates an increase in the consumption of cannabis, tramadol, and novel synthetic products by university students owing to social, economic, and peer pressure factors.⁴

However, the characteristics of those using substances have evolved quite significantly over the last few decades. Substance use used to be a problem that involved primarily the older generations; now, it has begun to overlap with the experimentation with substances among younger generations. This issue is prevalent in both cities and rural areas.³ University campus environments have an ambivalent role in this context as they not only represent settings where young and vulnerable individuals are concentrated but also provide organizational means for conducting prevention programs involving a lot of people at once. Studies carried out in Jordan indicate that peer pressure, lack of knowledge about health issues, and the fear of being stigmatized inhibit the use of prevention services offered on campuses.⁵

Artificial intelligence-driven conversational software applications, generally known as chatbots, have been introduced as one of the possible technologies to facilitate health education in such circumstances. Chatbots may be used to identify risks of substance abuse, spread evidence-based knowledge, and give personalized answers to health inquiries without the social judgment present in conventional consultations.⁶ The availability around the clock and the non-judgmental nature of interactions are especially suitable for sensitive issues, where the fear of embarrassment or stigmatization could otherwise dissuade people from taking part.⁷ Meta-analyses of AI-enhanced health education emphasize the need for appropriate content control during implementation in order to ensure the dissemination of reliable knowledge; however, they also recognize that, provided effective curation, they offer legitimate opportunities to augment rather than replace health counseling and education.⁸

Despite the growing use of artificial intelligence and conversational chatbots in health education, evidence regarding their application to substance-use prevention remains limited. In particular, few studies have examined AI chatbot-based interventions specifically designed to enhance substance-use prevention competence among university nursing students. This gap is particularly relevant in the Jordanian context, where nursing students constitute an important future healthcare workforce and may play a key role in substance-use screening, health education, early intervention, and prevention. Therefore, evaluating AI-supported educational interventions among nursing students may provide valuable evidence regarding the feasibility of integrating emerging digital technologies into substance-use prevention education. The present study was designed to address this evidence gap using a quasi-experimental intervention-control design, comparing substance-use prevention outcomes between nursing students who received a three-month AI chatbot-based educational intervention and those in a control group who did not receive the intervention.

Theoretical framework

According to Health Belief Model (HBM), the health behavior of an individual is affected by the perception of his or her vulnerability to the health issue, the perceived severity of its impacts, the benefits of engaging in certain actions for the prevention of the issue, the barriers to taking action, cues that trigger the behavior, and self-efficacy.⁹ It is particularly appropriate for the substance-use prevention as the decision of the students to engage in substance use can depend not only on the knowledge that the student has about the substance but also on his or her perceived vulnerability, severity, perceived benefit from preventive actions, and self-efficacy.

The theoretical rationale is further supported by principles of Social Cognitive Theory (SCT), particularly the concepts of self-efficacy, observational learning, behavioral capability, reinforcement, and reciprocal interaction between individuals and their social environment.¹⁰ Substance-use prevention requires more than simply increasing factual knowledge; students also need confidence and practical skills to manage peer influence, stressful situations, and opportunities for substance use. The intervention may modify the behavioral expression of personality-related substance-use risk through enhanced self-regulation, coping, risk appraisal, and adaptive decision-making. Thus, SCT complements the HBM by explaining how increased knowledge and perceived risk may be translated into greater behavioral capability and confidence to engage in substance-use avoidance behaviors.

Significance of the study

This research adds to the existing body of literature that focuses on the application of digital health technology, specifically AI chatbots, in the fields of nursing education and substance use prevention. The study adds new empirical information regarding the potential effectiveness of AI chatbots as part of nursing education in a university located in the Middle East – an environment underrepresented in digital health research globally. In terms of nursing practice, the study highlights the possibility for utilizing such a technology to complement health promotion practices as the nurse’s involvement in the field of digital health is constantly growing.⁹^,¹⁰ Furthermore, the research is related to SDG 3 (Good Health and Well-Being).

Purpose and hypotheses

This study examined the effectiveness of a mobile application with an embedded AI-powered chatbot in improving nursing students’ substance abuse prevention awareness relative to a matched control group. Three primary hypotheses guided the investigation:

H1:

Intervention-group students will score significantly higher than control-group students on knowledge, attitude, and practice concerning substance abuse prevention after the intervention.

H2:

Intervention-group students will report significantly lower substance use risk personality endorsement, as measured by the SURPS, than control-group students after the intervention.

H3:

Substance use risk personality is predicted by students’ knowledge, attitude, practice, and demographic variables.

Methods

Research design

A quasi-experimental design, with two groups of nursing students independently recruited at the same faculty during the same academic period. The intervention group included students who had completed a three-month AI chatbot educational intervention as they available to be engaged in the intervention for three months, while the control group consisted of students who had not received this intervention and were assessed at the same time. Both groups were drawn from the same student population and were matched on key demographic variables such as gender, age, and academic year to minimize confounding factors.

Setting

Recruitment of participants occurred in the largest nursing faculty in Jordan from October 2025 to February 2026. The selected setting was due to having an accredited undergraduate nursing program, the university’s commitment to adopting technology-driven teaching methods, and a sufficient number of students for conducting the experiment on both the intervention group and control group. Similarity in the context among the two groups of participants was maintained in order to ensure no contextual difference.

Participants

The targeted sample comprised undergraduate nursing students in Jordan throughout all four study years. The accessible population include all students in the selected university, who agreed to participate, and met the following inclusion criteria: have an active enrolment in the Faculty of Nursing, age above 18, provision of informed consent in written form, and available to be engaged in the intervention for three months. Those who did not attend the day of assessment and/or those who left more than 10% blanks in the questionnaire were excluded from participation in the study. The intervention group included 85 students that had used the application of an AI chatbot in the last three months, whereas the control group comprised 85 students who had no contact with the application previously.

Sample size

The required sample size was determined using an a priori power analysis for an independent-samples t-test. Assuming a medium effect size (d = 0.50), a two-sided significance level of 0.05, and a statistical power of 80%, the estimated minimum sample size was 64 participants per group (128 participants in total). To account for potential attrition and to ensure adequate statistical power, 85 participants were recruited for each group, resulting in a total sample of 170 participants. The selected sample size provided sufficient power to detect the hypothesized medium effect under the specified assumptions.

Instruments

The data gathering involved the use of an organized survey which consisted of four parts. The first part concerned sociodemographic factors such as gender, age category, year level in school, and self-rating of the relationship quality with parents and friends. The second part of the survey was a measurement tool called Substance Use Risk Profile Scale (SURPS) Woicik et al.¹¹ This test includes 23 questions about personality characteristics that have been empirically proven to be differentially risky with respect to substance use. Participants are required to answer the questions on a four-point Likert scale (1 = strongly disagree, 4 = strongly agree). It consists of four subscales that measure different risk factors for substance abuse such as anxiety sensitivity (tendency to utilize substances in order to reduce arousal in relation to fear), hopelessness (a state characterized by depressed cognition that can lead to self-medication of substance use), impulsivity (lowered reflection capacity and risky behavior), and sensation seeking (inclination to search for thrill and novelty). Scores Subscale scores are computed by summing constituent items. The SURPS demonstrates sound psychometric properties across diverse samples, with Cronbach’s alpha coefficients consistently exceeding .70 and confirmatory factor analyses supporting the four-factor structure.

The third component was an adapted Knowledge-Attitude-Practice (KAP) questionnaire.¹² The instrument contained 15 questions – five for each of the three domains. For the Knowledge domain, respondents had to give correct answers that could be scored using the dichotomous format of answers (right/wrong), resulting in a domain score of 0 to 5. The Attitude domain was assessed using the five-point Likert scale (1 – strongly disagree; 5 – strongly agree), which produced domain scores from 5 to 25. The Practice domain entailed dichotomous or frequency measures, with domain scores from 0 to 32. Scores equal to or higher than 70% of the maximum possible domain score were indicative of sufficient knowledge, positive attitude, and proactive actions, respectively. The adapted questionnaire was reviewed for content relevance and clarity by nursing experts before its use in the present study. Cronbach’s alpha coefficients ranging from 0.75 to 0.83 across the KAP domains, indicating acceptable to good internal consistency.

Intervention procedure

Phase I: Application Development and Baseline Assessment

A professionally designed web-based educational application for substance abuse prevention was developed by a qualified software engineer in collaboration with nursing experts specializing in psychiatric nursing and community health nursing. The application was designed to provide nursing students with an accessible, interactive, and structured learning environment. Its user-friendly interface included an introductory page explaining the purpose and objectives of the program, instructions for navigating the application, and information regarding the confidentiality and privacy of collected data.

The application incorporated an artificial intelligence (AI)-powered chatbot developed using Dialogflow to provide educational, evidence-informed responses to students’ questions related to substance abuse and its prevention. The chatbot operated using a structured, domain-specific knowledge base developed specifically according to the educational objectives of the intervention. The knowledge base was compiled from authoritative and evidence-based sources, including World Health Organization (WHO) guidelines, United Nations Office on Drugs and Crime (UNODC) reports, National Institute on Drug Abuse (NIDA) educational resources, and relevant peer-reviewed scientific literature.

The knowledge base was organized into major thematic domains covering commonly abused substances; their physical, psychological, and social effects; biopsychosocial risk factors and pathways associated with substance use and addiction; individual and environmental risk factors; consequences of substance use; prevention strategies; practical refusal skills; and help-seeking and referral behaviors. This structured organization was intended to ensure that chatbot responses remained consistent with the educational objectives of the intervention.

To minimize the risk of inaccurate, inappropriate, or clinically unsafe information, the chatbot knowledge base and educational content were reviewed by nursing experts specializing in psychiatric nursing and community health nursing before implementation. The chatbot was not intended to provide individualized diagnosis, treatment recommendations, medication advice, or emergency clinical management. Students were informed that the chatbot was an educational support tool and not a substitute for professional medical, psychological, or emergency services. Questions requiring individualized clinical assessment or professional intervention were directed toward appropriate healthcare, psychological, or support services. Representative student–chatbot interactions included questions such as: “What are the effects of drug abuse on mental health?”, “How can university students avoid substance use?”, and “What should I do if someone I know is using drugs?” The chatbot provided evidence-informed educational responses and reinforced prevention, refusal, help-seeking, and appropriate referral behaviors.

Before implementation, all educational and chatbot content was reviewed for relevance, clarity, accuracy, and appropriateness by a panel of three experts in nursing education with specialization in substance abuse. Their feedback was incorporated into the final version of the application. Intervention implementation was monitored through participant follow-up and regular communication to encourage continued engagement and adherence to the intervention procedures. The same educational content and application features were made available consistently to all participants in the intervention group to support intervention fidelity.

Phase II: AI Chatbot-Led Educational Intervention

The AI chatbot-led educational intervention was developed as a mobile-based, interactive educational program designed to enhance nursing students’ competence in substance-use prevention. The chatbot provided participants with accessible, structured, and interactive educational content addressing substance use, associated risk factors, health and psychosocial consequences, prevention strategies, and appropriate responses to substance-use-related situations.

Before initiation of the intervention, each participant in the intervention group received standardized written and verbal instructions explaining how to access the mobile application, navigate the chatbot, and use its available functions. The participants were also introduced to the chatbot’s educational capabilities and were instructed on how to ask questions, request clarification, and obtain additional information related to substance-use prevention.

The intervention was implemented over a period of three consecutive months. During this period, participants were instructed to interact with the AI chatbot twice per week, with each interaction lasting approximately 20 minutes. Thus, participants were expected to receive approximately 40 minutes of chatbot-based educational exposure per week. The interactions were participant-directed within the predefined intervention schedule, allowing students to explore the educational content, ask questions, request clarification, revisit previously presented information, and seek additional information according to their individual learning needs.

The participant-directed conversational approach was intentionally incorporated because substance use is a sensitive topic that may be associated with embarrassment, stigma, or fear of judgment. Providing students with private access to an AI-based educational resource was therefore intended to facilitate open inquiry and encourage students to seek information and clarification without the social pressure that may accompany face-to-face discussions. The chatbot provided immediate responses and enabled students to repeatedly access relevant educational information throughout the intervention period. The educational content was designed to progress from knowledge acquisition to risk recognition and preventive decision-making. Through interactive dialogue, clarification of misconceptions, and discussion of substance-use-related scenarios, the chatbot supported students in understanding substance-use risks, evaluating potentially harmful situations, and identifying appropriate preventive responses. The intervention was therefore intended not only to provide information but also to promote the application of knowledge through interactive learning and self-directed inquiry.

To maintain intervention fidelity, all participants in the intervention group received the same core educational content and were provided with the same prescribed intervention schedule of two chatbot interactions per week for approximately 20 minutes per interaction over three months.

Phase III: Post-Intervention Evaluation

As soon as the program ended after its three months’ duration, the subjects in both groups were asked to fill in the exact same questionnaire as before the intervention, including both KAP and SURPS surveys. The choice to conduct the assessment right after the program and not after a subsequent period of time was made in line with the main purpose of the research, which was to measure any possible immediate impact of the program in terms of education; this procedure is also commonly used in other chatbot-based health education experiments.¹³^,¹⁴ After the intervention, participants in the control group were given the right to access the chatbot for three months to get the benefits of the intervention, equal to the participants in the intervention group.

Pilot study

A pilot study was done before the actual intervention delivery, where 17 nursing students were included as part of the same population, but not in the actual sampling frame. The purpose of the pilot study was to examine whether chatbot usability and content are appropriate, and the questions in the survey were clear and easy to comprehend and estimate the approximate time taken to complete the questionnaire. The results of the piloting assured the need to simplify the instructions to use the chatbot, which was already done in the actual intervention. The results of the participants in the pilot study were not included in the final analysis.

Statistical analysis

Data were examined using IBM SPSS Statistics (version 27). Because the two groups were independently employed and assessed alongside, appropriate parametric tests were applied for both between- and within-group comparisons. Prior to inferential analyses, the distributional properties of all study variables were inspected within each group. Visual inspection of histograms and normal Q–Q plots indicated only minor deviations at the distribution tails. Skewness and kurtosis values for all variables were within ±1.5, supporting the assumption of normality.¹⁵ Levene’s test for equality of variances was conducted for all between-group comparisons and confirmed that the homogeneity of variance assumption was met. Baseline equivalence between the control and intervention groups was measured using chi-square test. To evaluate within-group changes from pretest to posttest, paired-samples t-tests were conducted separately for each group. Between-group differences at posttest were examined using independent-samples t-tests across KAP domains and SURPS subscales. Effect sizes were determined by using Cohen’s d, which is obtained through pooled standard deviation and cut-offs of 0.20 (small), 0.50 (medium), and 0.80 (large). Multiple linear regression was employed to predict SUPRS from KAP and the demographic variables. Standard regression diagnostics confirmed that assumptions of linearity, independence of errors (Durbin–Watson statistic), homoscedasticity, and normality of residuals were adequately met. A two-tailed significance level of p < .05 was adopted throughout the analysis.

Results

Participant characteristics

A total of 170 nursing students participated in this intervention–control study, with 85 students assigned to the control group and 85 students to the intervention group. As presented in Table 1, there was no statistical difference between the two groups regarding all the demographic characteristics, chi-square (p > .05). Female students constituted the majority in both the control (59/85; 69.4%) and intervention (58/85; 68.2%) groups. Most participants in each group were aged 18 to under 24 years (control: 60/85, 70.6%; intervention: 62/85, 72.9%). Second- and third-year students formed the two largest academic-year cohorts in both groups (control: 41.2% and 35.3%; intervention: 42.4% and 34.1%, respectively). Relationships with parents and friends were reported as positive by the majority of participants in both groups (parents: 85.9% and 85.8% rated as good or very good in the control and intervention groups, respectively; friends: 96.5% in both groups). A 100% retention rate was observed in both groups, eliminating differential attrition as a potential threat to internal validity and strengthening confidence in the observed between-group outcome differences.

Table 1. Demographic characteristics control and case groups (N = 170).

Characteristic	Category	Control group (n = 85)	Case group (n = 85)	p-value
Gender	Male	26 (30.6%)	27 (31.8%)	0.86
Gender	Female	59 (69.4%)	58 (68.2%)	0.86
Age	18 – < 24 years	60 (70.6%)	62 (72.9%)	0.74
Age	24 – < 29 years	25 (29.4%)	23 (27.1%)	0.74
Academic year	First year	7 (8.2%)	6 (7.1%)	0.98
	Second year	35 (41.2%)	36 (42.4%)
	Third year	30 (35.3%)	29 (34.1%)
	Fourth year	13 (15.3%)	14 (16.5%)
Relationship with parents	Very good	48 (56.5%)	49 (57.6%)	0.97
	Good	25 (29.4%)	24 (28.2%)
	Neutral	12 (14.1%)	12 (14.1%)
Relationship with friends	Good	57 (67.1%)	57 (67.1%)	0.99
	Very good	25 (29.4%)	25 (29.4%)
	Neutral	3 (3.5%)	3 (3.5%)

There was no change in the control group – p-values >.05, small effect sizes (dZ < 0.12). Thus, the current nursing curriculum without using the chatbot could not cause any changes in the substance abuse knowledge, attitude, practices, and risks over the period of three months. However, in contrast to this, the intervention group showed significant changes in all the parameters under investigation (p < .001). Effect sizes in the group are large: knowledge gained dz = 1.35, attitude improved dz = 1.96, and practice gained dz = 2.48. At the same time, there was a reduction in all four SURPS scales – the most considerable one was related to the Sensation Seeking scale (dz = 3.02), followed by the Impulsivity scale (dz = 2.51), Hopelessness scale (dz = 2.44), and Anxiety Sensitivity scale (dz = 2.42). Thus, by confirming that there were no other significant changes besides those observed in the intervention group, we can state that all between-group differences mentioned in the manuscript (Table 2) are related to the chatbot intervention.

Table 2. Within-groups and between-groups comparisons for KAP and SURPS measures (N = 170).

Outcome (range)	Group	Pre-test M (SD)	Post-test M (SD)	Mean Δ (95% CI)	t(84)	p	Cohen’s dz (within)
Knowledge (0–5)	Control	3.35 (1.15)	3.41 (1.18)	0.06 (−0.13, 0.25)	0.61	.54	0.07
Knowledge (0–5)	Intervention	3.40 (1.12)	4.62 (0.79)	1.22 (1.02, 1.42)	12.45	<.001	1.35
Between groups	t(168)	0.30	10.56				d = 1.58
Between groups	p	.77	<.001
Attitude (5–25)	Control	16.10 (3.50)	16.18 (3.52)	0.08 (−0.16, 0.32)	0.66	.51	0.07
Attitude (5–25)	Intervention	16.20 (3.45)	22.35 (2.71)	6.15 (5.47, 6.83)	18.02	<.001	1.96
Between groups	t(168)	0.19	13.97				d = 2.08
Between groups	p	.85	<.001
Practice (0–32)	Control	17.70 (4.10)	17.89 (4.21)	0.19 (−0.15, 0.53)	1.10	.27	0.12
Practice (0–32)	Intervention	17.80 (4.15)	28.76 (3.94)	10.96 (10.02, 11.90)	22.89	<.001	2.48
Between groups	t(168)	0.16	17.95				d = 2.68
Between groups	p	.87	<.001
SURPS-AS (4–16)	Control	12.20 (1.95)	12.14 (1.96)	−0.06 (−0.20, 0.08)	−0.86	.39	0.09
SURPS-AS (4–16)	Intervention	12.18 (1.92)	8.22 (1.85)	−3.96 (−4.31, −3.61)	−22.35	<.001	2.42
Between groups	t(168)	−0.07	−15.89				d = −2.47
Between groups	p	.95	<.001
SURPS-HOP (4–16)	Control	13.10 (1.86)	13.06 (1.88)	−0.04 (−0.19, 0.11)	−0.53	.60	0.06
SURPS-HOP (4–16)	Intervention	13.08 (1.84)	9.11 (1.79)	−3.97 (−4.32, −3.62)	−22.47	<.001	2.44
Between groups	t(168)	−0.07	−16.47				d = −2.58
Between groups	p	.94	<.001
SURPS-IMP (5–20)	Control	12.30 (2.01)	12.25 (2.01)	−0.05 (−0.21, 0.11)	−0.62	.54	0.07
SURPS-IMP (5–20)	Intervention	12.27 (2.00)	8.18 (1.62)	−4.09 (−4.44, −3.74)	−23.12	<.001	2.51
Between groups	t(168)	−0.10	−16.78				d = −2.63
Between groups	p	.92	<.001
SURPS-SS (6–24)	Control	16.35 (2.13)	16.32 (2.14)	−0.03 (−0.18, 0.12)	−0.40	.69	0.04
SURPS-SS (6–24)	Intervention	16.30 (2.10)	11.25 (2.08)	−5.05 (−5.40, −4.70)	−27.82	<.001	3.02
Between groups	t(168)	−0.16	−15.78				d = −2.84
Between groups	p	.88	<.001
Total SURPS (19–76)	Control	53.95 (5.79)*	53.77 (5.83)*	−0.18 (−0.70, 0.34)	−0.68	.50	0.07
Total SURPS (19–76)	Intervention	53.83 (5.74)*	36.76 (5.03)*	−17.07 (−18.01, −16.13)	−35.12	<.001	3.81
Between groups	t(168)	−0.14	−20.64				d = −3.16
Between groups	p	.89	<.001

The findings indicated significant improvements for those in the intervention group relative to the control group on all measures examined. Prior to the intervention, there were no significant differences between the two groups on knowledge, attitude, practice, or any of the subscales of the SURPS measure (p > .05). After the intervention, the intervention group experienced significant improvements in knowledge, attitude, and practice scores, with large effect sizes (Cohen’s dz = 1.35–2.48). The control group did not experience significant change over time. Posttest comparisons found that the intervention group scored significantly higher than the control group on all measures of the SURPS measure (p < .001), with very large effect sizes (d = 1.58–2.68). In terms of substance use risk profiles, substantial decreases were found for all SURPS dimensions in the intervention group, comprising anxiety sensitivity, hopelessness, impulsivity, sensation seeking, and SURPS scores overall (all p < .001). These decreases were noted to be associated with extremely high effect sizes (dz = 2.42–3.81), while no significant changes were found for the control group. Significant differences between groups at posttest also indicated lower SURPS scores for the intervention group as opposed to the control group (all p < .001), pointing to a highly positive impact of the intervention.

As shown in Tables 3a, b, c, b, and e no statistically significant differences were observed between age groups, male and female, academic levels, type of relationships with parents, and the of relationship with friends across all outcome variables (all p > .05). In Table 4, the multiple linear regression model using the enter method was statistically significant, F (13, 71) = 6.24, p < .001, explaining a substantial proportion of variance in post-test SURP scores (R² = .533). The findings reveal that knowledge, attitude, and practice are significant predictors for reducing the substance use risk personality.

Table 3a. Subgroup differences in intervention effects by age: comparison of change scores (Δ Post–Pre) among nursing students in the intervention group.

Outcome (Δ)	18- < 24 (n = 62) M (SD)	24- < 29 (n = 23) M (SD)	t(83)	p	Cohen’s d
Knowledge	1.19 (0.85)	1.30 (0.92)	−0.51	.61	0.13
Attitude	6.05 (3.10)	6.42 (3.38)	−0.49	.63	0.12
Practice	10.80 (4.20)	11.30 (4.15)	−0.50	.62	0.12
SURPS-AS	−3.92 (1.65)	−4.05 (1.70)	0.32	.75	0.08
SURPS-HOP	−3.94 (1.61)	−4.03 (1.68)	0.23	.82	0.06
SURPS-IMP	−4.05 (1.55)	−4.18 (1.58)	0.34	.74	0.08
SURPS-SS	−5.00 (1.70)	−5.17 (1.72)	0.41	.68	0.10

Table 3b. Subgroup differences in intervention effects by gender: independent-samples t-Test comparisons of change scores (Δ Post–Pre) among nursing students.

Outcome (Δ)	Male (n = 27) M (SD)	Female (n = 58) M (SD)	t(83)	p	Cohen’s d
Knowledge	1.20 (0.90)	1.23 (0.88)	−0.14	.89	0.03
Attitude	6.10 (3.10)	6.17 (3.12)	−0.10	.92	0.02
Practice	10.80 (4.20)	11.05 (4.40)	−0.25	.80	0.06
SURPS-AS	−3.90 (1.60)	−3.98 (1.65)	0.22	.83	0.05
SURPS-HOP	−3.95 (1.60)	−3.98 (1.65)	0.08	.94	0.02
SURPS-IMP	−4.05 (1.55)	−4.10 (1.60)	0.13	.90	0.03
SURPS-SS	−5.00 (1.70)	−5.07 (1.72)	0.17	.87	0.04

Table 3c. Subgroup differences in intervention effects by academic year: independent-samples t-Test comparisons of change scores (Δ Post–Pre) between junior and senior nursing students.

Outcome (Δ)	Junior (n = 42) M (SD)	Senior (n = 43) M (SD)	t(83)	p	Cohen’s d
Knowledge	1.20 (0.88)	1.24 (0.89)	−0.21	.83	0.04
Attitude	6.10 (3.10)	6.20 (3.15)	−0.15	.88	0.03
Practice	10.90 (4.20)	11.00 (4.40)	−0.11	.91	0.02
SURPS-AS	−3.95 (1.62)	−3.97 (1.64)	0.06	.95	0.01
SURPS-HOP	−3.96 (1.60)	−3.98 (1.63)	0.06	.95	0.01
SURPS-IMP	−4.07 (1.55)	−4.10 (1.58)	0.09	.93	0.02
SURPS-SS	−5.04 (1.70)	−5.06 (1.72)	0.06	.95	0.01

Table 3d. Subgroup differences in intervention effects by relationship with parents: independent-samples t-test comparisons of change scores (Δ Post–Pre) among nursing students in the intervention group.

Outcome (Δ)	Very good (n = 49) M (SD)	Less than very good (n = 36) M (SD)	t(83)	p	Cohen’s d
Knowledge	1.21 (0.87)	1.23 (0.89)	−0.10	.92	0.02
Attitude	6.12 (3.08)	6.19 (3.15)	−0.10	.92	0.02
Practice	10.92 (4.18)	11.00 (4.40)	−0.08	.94	0.02
SURPS-AS	−3.96 (1.63)	−3.95 (1.65)	−0.03	.98	0.01
SURPS-HOP	−3.97 (1.62)	−3.96 (1.64)	−0.03	.98	0.01
SURPS-IMP	−4.08 (1.56)	−4.10 (1.59)	0.06	.95	0.01
SURPS-SS	−5.05 (1.71)	−5.04 (1.73)	−0.03	.98	0.01

Table 3e. Subgroup differences in intervention effects by relationship with friends: independent-samples t-test comparisons of change scores (Δ Post–Pre) among nursing students in the intervention group.

Outcome (Δ)	Good (n = 57) M (SD)	Very good (n = 25) M (SD)	t(80)	p	Cohen’s d
Knowledge	1.20 (0.85)	1.25 (0.90)	−0.24	.81	0.06
Attitude	6.10 (3.05)	6.20 (3.15)	−0.13	.90	0.03
Practice	10.90 (4.15)	11.05 (4.30)	−0.15	.88	0.04
SURPS-AS	−3.96 (1.63)	−3.94 (1.66)	−0.05	.96	0.01
SURPS-HOP	−3.97 (1.62)	−3.95 (1.65)	−0.05	.96	0.01
SURPS-IMP	−4.09 (1.57)	−4.07 (1.60)	−0.05	.96	0.01
SURPS-SS	−5.05 (1.72)	−5.04 (1.74)	−0.03	.98	0.01

Table 4. Multiple linear regression predicting substance use risk personality (SURPS Total Score) from knowledge, attitude, practice, and demographic variables case group post-intervention (n = 85).

Predictor	B	SE	β	t	p	95% CI for B
Constant	72.84	3.95	—	18.44	< .001	[64.96, 80.72]
Knowledge	−1.52	0.62	−.19	−2.45	.017	[−2.76, −0.28]
Attitude	−0.52	0.14	−.27	−3.71	< .001	[−0.80, −0.24]
Practice	−0.48	0.11	−.28	−4.36	< .001	[−0.70, −0.26]

Discussion

Substance abuse among young adults and university students has emerged as a major public health concern worldwide, with significant physical, psychological, social, and academic consequences. Recently, mobile health technologies and artificial intelligence (AI)-powered educational tools have gained increasing attention as innovative approaches to health education due to their accessibility, interactivity, and ability to provide personalized learning experiences.

The current study showed that, there were no statistically significant differences between the characteristics of both groups studied at the outset, which ensured their comparability and strengthened the internal validity of this study. As the comparability of groups allows ruling out selection bias and proving that the post-test differences in groups under study are caused by an intervention rather than initial differences, many nursing education studies of a quasi-experimental design also underscore this aspect.

No statistical difference was found in any variable in the control group, which remained stable in terms of knowledge, attitude, practice, and psychosocial risk factors throughout the study. This indicates that neither routine exposure to curriculum content nor maturation and other extraneous educational experiences have caused any improvement during the course of the study. The results obtained in this study corroborate the findings of other researchers who have found that conventional methods of classroom teaching such as lectures are ineffective in inducing behavior change in substance abuse prevention education among nursing students.¹⁶

The Effectiveness of the intervention: The results clearly revealed differences between the groups, with the intervention group displaying improvement in terms of the outcome measures compared to the control group, which did not display any significant improvement in the measured outcomes during the entire experiment period. The above result suggests that involvement in the AI chatbot-based intervention positively impacted the participants’ knowledge, attitude, preventive behavior, and measured risks. It is important to note that the use of a quasi-experimental approach in the current study does not allow drawing conclusive causal inference since the participants were not randomly assigned to either group. However, the use of baseline measures and comparison group increases the possibility that the measured improvement is caused by the intervention. The above findings align with prior research in digital education on health, which indicates the effectiveness of using technological tools in learning processes and health-related behaviors.¹⁶

Improvements in knowledge, attitudes, and practice (KAP Outcomes)

The intervention group showed significant and large gains in terms of knowledge, attitude, and practices. This implies that the use of the AI chatbot successfully increased cognitive learning, changes in attitudes, and behavioral intentions. The size of the effect suggests that learning was not merely restricted to information but went into application and decision-making. These results corroborate recent literature highlighting that AI-enabled conversational agents facilitate learning via dialogue interaction, customization, and feedback.¹⁷ Likewise, systematic reviews of digital health education in nursing indicate that mobile and AI-enabled devices outperform traditional educational strategies in knowledge retention and clinical decision-making.¹⁸ Based on the considerable changes that were seen in knowledge, attitudes, and practices within the intervention group, it is evident that the use of an AI chatbot was effective in improving the substance abuse prevention awareness of the participants. The large effect sizes mean that the intervention not only facilitated information acquisition but also attitudinal and behavioral changes towards substance abuse. This could be attributed to the interactivity and personalization of learning facilitated by AI chatbots. Such changes are especially crucial for nursing students since having sufficient knowledge and practicing preventive measures lead to safer clinical practice and better preparation for health promotion and patient education. This is consistent with previous literature suggesting that AI-based and mobile learning strategies improve the learning experience of nursing students and health care professionals.

Reduction in SURPS risk factors

A novel and important finding of this study is the significant reduction in all SURPS dimensions, including impulsivity, sensation seeking, anxiety sensitivity, and hopelessness. The above psychological factors are commonly described as relatively stable personal traits, which predispose individuals to substance abuse behavior. Nevertheless, the present results indicate that psychoeducational training through structured exercises may affect the manifestation of such personality features. The conclusion aligns with Conrod et al.,¹⁹ who have shown that interventions based on personality characteristics may considerably diminish an individual’s propensity for substance abuse through the adjustment of cognitive-emotional mechanisms. More current research corroborates the positive effect of digital solutions on self-awareness and emotional regulation skills of young adults.²⁰ The current paper contributes to existing literature by suggesting that AI-assisted learning may modify psychological factors contributing to substance abuse behavior.

The substantial changes noted in all SURPS parameters, such as impulsivity, sensation seeking, anxiety sensitivity, and hopelessness, can be regarded as one of the key results obtained during the course of the current study. Personality-related aspects mentioned above usually demonstrate high predictive value regarding the possibility of substance use vulnerability in people; hence, the positive results obtained might reflect the beneficial effects produced by the AI-based chatbot on the cognitive and psychological processes of the participants. The presence of structured psychoeducational material, communication with the chatbot, and continuous feedback might have helped to enhance the emotional and cognitive skills of the respondents. In regard to nursing students, improvement of their psychological risk factors is especially relevant since emotional stability and self-control play a vital role in effective decision-making, stress management, and the overall success of students’ activities in a clinical environment.

Predicting in SURPS from knowledge, attitude, and practice

Regression results demonstrated the highest contribution of knowledge, attitude, and practice as the best predictors of substance use risk after the intervention. This indicating that the resulted lower personality risk to substance abuse is obviously related to the impact of chatbot app in improving student’s knowledge, attitude, and practice. On the contrary, demographic variables, such as age, gender, academic level, and social ties, failed to make a statistical difference to intervention outcomes. There was no difference found between young and older students, implying that age had no effect on the reaction to the intervention. The results indicate that the chatbot had equal efficacy for all development levels among the nursing student population. This outcome is consistent with research in recent years suggesting that the flexibility, individuality, and self-regulation inherent in the use of technology enable digital learning tools to be effective at any age level.²¹^,²² This result is in accordance with the conclusions of recent AI-based learning studies showing that the impact of the intervention is determined by exposure to the digital environment rather than by personal factors.²³ Taken together, the results show clear patterns: improvement in the experimental group, lack of change in the control group, lack of influence by demographics, and high power of regression to confirm the intervention impact.

Limitation of the study

There are several limitations that need to be taken into account when analyzing the results obtained during this study. First of all, there was no randomization of participants; instead, the research applied a quasi-experimental nonequivalent control group design, which means that the establishment of causal relationships is difficult and the chance for selection bias exists. Secondly, the study took place in one faculty of nursing in Jordan, which makes it difficult to generalize the results to other groups of nursing students. Thirdly, all the information obtained during the research was based on self-reported data from questionnaires, thus being prone to possible biases. Fourthly, the intervention period was relatively short, and there was no follow-up test carried out in order to see whether changes observed persisted over time. Fifthly, the sample included only nursing students, which makes it difficult to apply the results obtained to the population from other healthcare specialties.

Conclusion

The findings of the present study provide preliminary evidence that integrating an artificial intelligence (AI)-powered conversational chatbot within a mobile application may be a useful complementary approach to substance abuse prevention education among nursing students. Compared with the control group, participants who received the intervention demonstrated improvements in knowledge, attitudes, and self-reported preventive practices, together with reductions in personality-related risk factors assessed using the Substance Use Risk Profile Scale (SURPS). These findings suggest that chatbot-supported educational interventions have potential to enhance students’ awareness and self-reported preventive practices related to substance use when used as a complement to conventional educational approaches.

No statistically significant differences in the intervention outcomes were identified according to participants’ age, gender, academic year, or self-reported quality of parental and peer relationships. Thus, within the characteristics of the present sample, the observed intervention outcomes did not appear to differ significantly across these variables. Furthermore, the regression analysis indicated an association between personality-related substance-use risk and the measured intervention outcomes, highlighting the potential relevance of individual risk characteristics when considering substance-use prevention education.

The findings have several implications for nursing education. AI-assisted conversational applications may provide an interactive supplementary resource that can support students’ engagement with substance-use prevention content, reinforce educational messages, and provide opportunities for repeated learning outside conventional classroom settings. The findings should also be interpreted in light of the nonrandomized quasi-experimental design, sample characteristics, and measurement methods. In particular, preventive practices and changes in SURPS scores were assessed using self-reported measures and may have been influenced by response bias, social desirability, or other uncontrolled factors. Future studies using randomized designs, longer follow-up periods, and objective behavioral measures are warranted to further evaluate the effectiveness and sustainability of AI-powered chatbot interventions for substance abuse prevention.

Recommendations

In light of the findings of the present study, AI-assisted chatbot applications may be considered as complementary educational tools within undergraduate nursing curricula, particularly in psychiatric and community health nursing courses addressing substance-use prevention. Nursing educators may consider incorporating mobile-based and AI-enhanced learning resources alongside conventional teaching methods to deliver students with additional opportunities for available, interactive, and repeated engagement with prevention-related content. Future development of AI-assisted educational platforms should consider incorporating adaptive and personalized learning features that respond to students’ educational needs and relevant risk characteristics. The inclusion of interactive, scenario-based learning activities, such as simulated peer-pressure situations, decision-making exercises, and practical prevention strategies, may further support the application of prevention-related knowledge in realistic contexts.

Future longitudinal and randomized controlled studies are recommended to examine the sustainability of the observed outcomes and to determine whether improvements in knowledge, attitudes, and preventive practices are maintained over time and translate into relevant competencies in clinical and community settings. Further research with larger and more diverse samples is also warranted to evaluate the feasibility, acceptability, and effectiveness of similar AI-assisted interventions among students in other health science disciplines and across different educational and cultural settings.

Ethics approval and consent to participate

Ethical approval was granted by the Institutional Research Board of Al-Zaytoonah University of Jordan (approval number: IRB # 27/10/2025–2026). Written informed consent was obtained from all participants prior to enrollment. All procedures complied with the ethical principles of the Declaration of Helsinki (2013 revision).

Data availability

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

The dataset supporting the findings of this study is publicly available in the Figshare Repository at https://doi.org/10.6084/m9.figshare.32536995. This dataset contains the following underlying data: SPSS: All participants related data.²⁴ Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). With ensuring compliance with applicable ethical and privacy requirements and protecting the confidentiality of participants.

Acknowledgments

The authors gratefully acknowledge all participating students and the administrative leadership of the Faculty of Nursing, where the study conducted, for their cooperation throughout data collection.

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© 2026 Abualruz H et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abualruz H, Shokr EA and Aburuz S. Effectiveness of a Mobile Application with an AI-Powered Chatbot to Enhance Nursing Students' Awareness of Substance Abuse Prevention: An Interventional Study [version 2; peer review: 2 approved]. F1000Research 2026, 15:1015 (https://doi.org/10.12688/f1000research.183558.2)

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Febblina Daryanes, Universitas Riau, Pekanbaru, Riau, Indonesia

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Reviewer Report 04 Sep 2026

Louis Robert C. Sison, Bulacan State University, Bulacan, Philippines

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Competing Interests: No competing interests were disclosed.

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Reviewer Report 31 Aug 2026

Febblina Daryanes, Universitas Riau, Pekanbaru, Riau, Indonesia

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