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Artificial Intelligence-Powered Learning Technologies in Pharmacy Education: Knowledge, Attitudes, and Practices of Undergraduate Pharmacy Students
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Received: ,
Accepted: ,
How to cite this article: Garba AM, Mudenda S, Okoro RN. Artificial Intelligence-Powered Learning Technologies in Pharmacy Education: Knowledge, Attitudes, and Practices of Undergraduate Pharmacy Students. Sci Tech Nex. doi: 10.25259/STN_5_2026
Abstract
Objectives
Artificial intelligence (AI) is increasingly integrated into health professions education, including pharmacy training. Understanding students’ knowledge, attitudes, and practices (KAP) toward AI-powered learning tools is essential for effective curricular integration.
Materials and Methods
A descriptive cross-sectional study was conducted among undergraduate pharmacy students at a Nigerian university during the 2024/2025 academic session from August 2025 to November 2025. Data were collected using a validated self-administered questionnaire adapted from previous studies. KAP scores were categorised using modified Bloom’s cut-offs. Descriptive statistics, Spearman’s correlation, and multivariable logistic regression were applied.
Results
A total of 456 students participated (response rate: 81.3%), with a median age of 23 years (IQR: 20–25). Overall, 69.7% demonstrated good knowledge, 72.4% showed positive attitudes, and 50.4% exhibited good practices regarding AI-powered learning tools. Knowledge showed a weak but statistically significant positive correlation with attitude (Spearman’s rho = 0.248, p < 0.001). Year of study was the only significant predictor of poor knowledge, with second-year students more likely and fifth-year students less likely to have poor knowledge.
Conclusion
Pharmacy students demonstrated generally modest engagement with AI-powered learning tools despite limited formal training. Integrating structured AI education into pharmacy curricula may enhance students’ knowledge and promote responsible use.
Keywords
Artificial intelligence
Attitudes
Knowledge
Pharmacy students
Practices
1. INTRODUCTION
Artificial intelligence (AI) refers to the development of computer systems capable of performing tasks that traditionally require human intelligence, including learning, reasoning, and decision-making, through techniques such as machine learning, natural language processing, and robotics.[1] Since the mid-20th century, advances in computing have enabled machines to undertake increasingly complex functions, laying the foundation for the rapid expansion of AI applications across multiple sectors.[2] Today, AI is widely recognised as a transformative technology, with particularly profound implications for education and healthcare.[3]
In educational contexts, AI involves the application of intelligent technologies and data-driven systems to support, enhance, and automate teaching and learning processes.[3] The integration of AI into educational systems represents one of the most significant shifts in pedagogy and learning practices of the 21st century.[4] Initially adopted for administrative and organisational tasks, AI applications in education have evolved into advanced adaptive learning systems capable of personalising instruction, evaluating student performance, and facilitating interactive learning experiences.[5] More recently, the emergence of generative AI, including large language models such as GPT-4 and Claude, has further accelerated this transformation by enabling automated content generation, academic writing support, and real-time feedback, while simultaneously introducing new pedagogical and ethical challenges.[6-8]
As educational institutions increasingly adopt AI technologies, stakeholders must balance the potential benefits with concerns related to equity, data privacy, ethics, and academic integrity.[9] Educators are expected to acquire new competencies to effectively incorporate AI-based tools into instructional practice, while students are adapting to learning environments increasingly shaped by intelligent systems.[10] This evolving landscape underscores the need for a clear understanding of stakeholders’ perceptions, experiences, and responses to AI-enabled educational tools.
Within pharmacy education, AI-driven applications such as chatbots, writing assistants, and drug information platforms are increasingly utilised to support learning and academic activities.[11-13] Despite their potential to enhance knowledge acquisition and clinical reasoning, concerns persist regarding ethical use, over-dependence on AI, threats to academic integrity, and insufficient training in responsible AI use.[14] Evaluating pharmacy students’ knowledge, attitudes, and practices (KAP) toward AI is therefore essential to guide curriculum development and promote the safe and effective integration of these technologies into pharmacy training.
Although studies from other countries have examined pharmacy students’ perceptions and use of AI-based tools,[15-26] empirical evidence from Nigerian pharmacy schools remains scarce.[27] Consequently, this study assessed the knowledge, attitudes, and practices of pharmacy students toward AI-powered learning tools at the University of Maiduguri and identified factors associated with poor KAP.
2. MATERIALS AND METHODS
2.1. Study design and setting
This descriptive cross-sectional study was conducted among undergraduate pharmacy students at the University of Maiduguri, Nigeria, from August 2025 to November 2025.
2.2. Study population
The study population comprised all undergraduate pharmacy students registered during the 2024/2025 academic session. Eligible participants were those who provided informed consent. Students who had deferred their studies or were suspended or expelled during the session were excluded.
2.3. Sample size determination and sampling method
The sample size was determined using the Raosoft® online sample size calculator, assuming a 5% margin of error, a 95% confidence interval (CI), and a total population of 561 undergraduate pharmacy students registered in the 2024/2025 academic session. Based on these parameters, a minimum sample size of 229 participants was required. All eligible students present during the data collection period were invited to participate, and recruitment was carried out using a convenience sampling approach.
2.4. Data collection instrument
Data were collected using a structured, self-administered questionnaire adapted from previous studies.[18,25,27,28] A brief operational definition with examples was added to the questionnaire description to ensure respondent clarity. Following face and content validation from two experts in pharmacy education, the final instrument comprised 29 items assessing knowledge, attitudes, and practices related to AI-powered learning tools. Actual knowledge was assessed using 10 multiple-choice questions, attitudes were measured using 11 items on a 5-point Likert scale ranging from strongly disagree to strongly agree, and practices were assessed using five items with response options of yes, no, or can’t say. The remaining three multiple-choice items assessed the types of AI-powered tools used, the purposes of use, and perceived barriers to their utilisation. A pilot study of the questionnaire was conducted among 20 pharmacology students in the faculty to assess clarity and relevance. Feedback from the pilot was reviewed and incorporated to improve the instrument. The internal consistency of the finalised questionnaire was acceptable, with a Kuder–Richardson 20 (KR-20) coefficient of 0.727 for the knowledge domain, and Cronbach’s alpha values of 0.771 and 0.656 for the attitudes and practice domains, respectively.
2.5. Ethical considerations
Participants were informed about the purpose of the study and assured of the confidentiality and anonymity of all information provided. Participation was entirely voluntary; no incentives were offered, and instructors were present only for routine teaching and did not influence participation. Students were informed that refusal would not affect academic standing, and written informed consent was obtained from all participants before data collection.
2.6. Data collection procedure
Data collection was conducted between August and November 2025. Paper-based questionnaires were administered in lecture halls, and respondents were given adequate time to complete them. Completed questionnaires were retrieved on the same day to minimise non-response and prevent data loss.
2.7. Data entry and transformation
Double data entry was employed to enhance data accuracy and reliability. Knowledge items were scored as correct (1) or incorrect (0). Attitude responses were trichotomised, with agreement (agree and strongly agree) scored as “2”, neutral as “1”, and non-agreement (strongly disagree and disagree) scored as 0. Items expressing negative sentiment or skepticism toward AI (Items 6, 8, 9, and 10) were reverse-coded before computing the composite attitude score. For the practice domain, a “yes” response was assigned 1 point and a “no” response 0 points while responses marked as “can’t say” were treated as missing and excluded from the denominator during composite practice score calculation. Total KAP scores were calculated and classified using modified Bloom’s cut-off criteria. An average score of ≥60% was considered indicative of “good knowledge”, “positive attitude”, and “good practice”, whereas an average score of <60% was classified as “poor knowledge”, “negative attitude”, and “poor practice”.[29] For regression analyses, poor KAP outcomes were coded as “1”, while good KAP outcomes were coded as “0”.
2.8. Statistical analysis
Data were analysed using SPSS version 25. Normality was assessed using the Kolmogorov–Smirnov test, which indicated a non-normal distribution of age and total KAP scores (p < 0.05), justifying the use of nonparametric tests. Descriptive statistics, including frequencies, percentages, medians, and interquartile ranges (IQRs), were used to summarise the data. Associations were examined using Spearman’s correlation and multivariable logistic regression. Statistical significance was set at p < 0.05.
3. RESULTS
3.1. Sociodemographic characteristics
Out of 561 eligible students, 456 participated in the study. The median age was 23 (IQR: 20–25) years, with most respondents aged 21–25 years (53.9%, n=246), followed by <21 years (30.9%, n=141) and >25 years (15.1%, n=69). Males constituted 57.5% of the sample, and 58.6% (n=267) and 41.4% (n=189) of respondents identified as Muslims and Christians, respectively. Third-year students formed the largest group (34.9%, n=159), followed by fifth-year students (19.5%, n=89), fourth- and second-year students (17.3%, n=79 each), and first-year students (19.5%, n=50).
3.2. Knowledge of AI
Participants demonstrated a generally moderate level of knowledge regarding AI. High proportions correctly identified the meaning of AI in pharmacy education (80.7%), its educational functions (75.0%), and examples of AI-powered learning platforms (87.3%). However, knowledge was lower regarding AI limitations (39.5%), simplification of complex topics (39.9%), and academic integrity issues (51.3%). The median knowledge score was 7 (IQR: 5–8) out of a maximum score of 10, with 69.7% classified as having good knowledge [Table 1].
| Items | Statements | Correct n (%) | Incorrect n (%) | Correct answers |
|---|---|---|---|---|
| 1 | The meaning of AI in the context of pharmacy education | 368 (80.7) | 88 (19.3) | The use of computer systems to simulate human intelligence for enhancing learning and decision-making in pharmacy education |
| 2 | Function of AI in education | 342 (75.0) | 114 (25.0) | Personalise learning experiences |
| 3 | Examples of AI-powered learning platforms | 398 (87.3) | 58 (12.7) | ChatGPT, Quizlet, and Khanmigo |
| 4 | The meaning of AI in the context of healthcare | 316 (69.3) | 140 (30.7) | A system that mimics human intelligence to make clinical decisions |
| 5 | The usefulness of AI tools in pharmacy practice | 346 (75.9) | 110 (24.1) | Assisting in drug interaction checking and clinical decision support |
| 6 | AI tools can make errors and should be used with human oversight | 298 (65.4) | 158 (34.6) | AI tools can make errors and should be used with human oversight |
| 7 | The main benefit of using AI in student learning | 306 (67.1) | 150 (32.9) | Provides adaptive feedback and support |
| 8 | AI and academic integrity | 234 (51.3) | 222 (48.7) | AI tools can be misused to generate unoriginal work |
| 9 | AI feature helps students by explaining complex topics in simple terms | 182 (39.9) | 274 (60.1) | Natural language generation |
| 10 | Limitations of AI-powered learning tools | 180 (39.5) | 276 (60.5) | Helps develop critical thinking |
| Median knowledge score (IQR) | 7 (5-8) | |||
| Good knowledge, n (%) | 318 (69.7) | |||
| Poor knowledge, n (%) | 138 (30.3) |
IQR: Interquartile range.
3.3. Attitudes toward AI-powered learning tools
Most respondents expressed positive attitudes toward AI use in learning, particularly regarding improved understanding (74.3%) and enhanced learning experience (79.4%). However, concerns about over-reliance (52.0%) and uncertainty about curriculum inclusion (45.2%) were noted. The median attitude score was 15 (IQR: 13-17) out of a maximum score of 22, with 72.4% demonstrating a positive attitude [Table 2].
| Items | Statements | Responses n (%) | ||
|---|---|---|---|---|
| Agreement | Neutral | Nonagreement | ||
| 1 | AI tools can improve my understanding of pharmacy topics | 339 (74.3) | 79 (17.3) | 38 (8.3) |
| 2 | AI-powered learning tools can significantly enhance my learning experience | 362 (79.4) | 59 (12.9) | 935 (7.7) |
| 3 | AI-powered learning tools can help with personalised learning | 360 (78.9) | 67 (14.7) | 29 (6.4) |
| 4 | AI tools reduce the workload and help save study time | 343 (75.2) | 79 (17.3) | 34 (7.5) |
| 5 | I feel comfortable using AI-powered tools in my studies | 320 (70.2) | 101 (22.1) | 35 (7.7) |
| 6* | I am concerned about over-reliance on AI tools | 237 (52.0) | 163 (35.7) | 56 (12.3) |
| 7 | AI should be included in the curriculum of the pharmacy programme of my university | 206 (45.2) | 157 (34.4) | 93 (20.4) |
| 8* | I believe AI could replace human educators in the future | 138 (30.3) | 147 (32.2) | 171 (37.5) |
| 9* | AI will eventually replace pharmacists in certain roles | 134 (29.4) | 120 (26.3) | 202 (44.3) |
| 10* | AI is unlikely to reduce the need for human expertise in pharmacy practice | 162 (35.5) | 136 (29.8) | 158 (34.6) |
| 11 | I am willing to use AI tools in my future practice | 274 (60.1) | 122 (26.8) | 60 (13.2) |
| Median attitude score (IQR) | 15 (13-17) | |||
| Positive attitude, n (%) | 330 (72.4) | |||
| Negative attitude, n (%) | 126 (27.6) | |||
*Reversed negatively worded items, IQR: Interquartile range
3.4. Practices related to AI-powered learning tools
Despite limited formal training (13.2%), the majority of students reported using AI-powered learning tools (91.9%). Regular use was common, and most respondents verified AI-generated information before use (92.8%). The median practice score was 3 (IQR: 2-3) out of a maximum score of 4, with 50.4% classified as having good practice [Table 3].
| Items | Statements | Responses, n (%) | |
|---|---|---|---|
| Yes | No | ||
| 1. | Ever used AI-powered learning tools in studies | 419 (91.9) | 37 (8.1) |
| 2. | Often use of AI tools for learning purposes | 299 (68.9) | 135 (31.1) |
| 3. | Verification of the information provided by AI tools before using it | 401 (92.8) | 31 (7.2) |
| 4. | Confident using AI tools for studies | 293 (67.7) | 140 (32.3) |
| Median practice score (IQR) | 3 (2-3) | ||
| Good practice, n (%) | 230 (50.4) | ||
| Poor practice, n (%) | 226 (49.6) | ||
IQR: Interquartile range
3.5. Types, purposes, and barriers to AI Use
Chatbots were the most widely used AI tools, followed by writing assistants and online learning platforms. Common uses included content summarisation, research assistance, assignments, and practice question generation. Major barriers to AI use were poor internet connectivity, lack of training, and difficulty understanding AI concepts (Supplementary Material S1).
3.6. Correlation analysis
Knowledge showed a weak but statistically significant positive correlation with attitude (Spearman’s rho = 0.248, p < 0.001). No significant correlation was found between knowledge and practice, and attitude and practice (Supplementary Material S2).
3.7. Predictors of poor KAP
Multivariable logistic regression revealed that study year was the only significant predictor of poor knowledge. Second-year students were more likely to have poor knowledge, while fifth-year students were significantly less likely. No significant predictors were identified for poor attitude or practice [Table 4].
| Variables | Knowledge | Attitude | Practice | |||
| AOR (95% CI) | p value | AOR (95% CI) | p value | AOR (95% CI) | p value | |
| Age group (years) | ||||||
| <21 | Reference | Reference | Reference | |||
| 21-25 | 1.37 (0.72-2.62) | 0.337 | 0.81 (0.42-1.51) | 0.500 | 0.87 (0.49-1.54) | 0.629 |
| >25 | 1.03 (0.43-2.50) | 0.946 | 0.86 (0.38-1.97) | 0.719 | 109 (0.51-2.31) | 0824 |
| Sex | ||||||
| Female | Reference | Reference | Reference | |||
| Male | 1.48 (0.95-2.29) | 0.080 | 1.32 (0.85-2.05) | 0.210 | 0.88 (0.60-1.29) | 0.879 |
| Religion | ||||||
| Islam | Reference | Reference | Reference | |||
| Christianity | 0.79 (0.52-1.22) | 0.291 | 0.77 (0.50-1.17) | 0.221 | 1.30 (0.89-1.90) | 0.173 |
| Study years | ||||||
| First | Reference | Reference | Reference | |||
| Second | 2.21 (1.04-4.70) | 0.039* | 1.69 (0.69-4.13) | 0.254 | 1.55 (0.75-3.22) | 0.236 |
| Third | 0.54 (0.23-1.27) | 0.157 | 2.51 (0.99-6.93) | 0.053 | 0.94 (0.44-2.04) | 0.883 |
| Fourth | 0.84 (0.33-2.13) | 0.708 | 2.49 (0.89-6.93) | 0.082 | 0.97 (0.41-2.31) | 0.947 |
| Fifth | 0.19 (0.07-0.56) | 0.003* | 1.71 (0.60-4.89) | 0.316 | 0.95 (0.40-2.28) | 0.908 |
*Multivariable Logistic Regression is Significant p < 0.05, CI: Confidence interval, AOR: Adjusted odds ratio.
3.8. Summary of the key findings
Based on the four charts in Figure 1, here is a concise summary of the data, progressing in ascending order from the bottom-right to the top-left:
Training vs. practice status: While a vast majority of respondents report that they Verify AI Info (92.8%) and use AI tools (91.9%), only a small fraction (13.2%) has received formal training on the subject, highlighting a significant training gap despite high adoption.
Predictors of poor knowledge: The forest plot indicates that second-year students are a statistically significant predictor of poor AI knowledge compared to 1st-year students.
Key knowledge & attitude indicators: This chart shows that most respondents recognise AI’s role in platforms (83.2%), finding meaning (80.7%), and enhanced experience (79.4%). However, fewer feel equipped to assess limitations (39.5%), simplification (39.9%), or AI’s current inclusion (45.2%) in curricula.
Overall KAP Summary: Respondents show relatively strong self-reported positive attitude (72.4%) and good knowledge (69.7%) toward AI, but demonstrate significantly lower rates of good practice (50.4%).
4. DISCUSSION
This study provides a comprehensive assessment of AI awareness, attitudes, and practices among Nigerian pharmacy students, utilising a KAP framework to offer a more holistic evaluation than previous research focused solely on chat-based tools.[27] By examining how academic progression influences AI literacy, an underexplored area in African literature, and identifying specific usage patterns and ethical considerations, the research aligns with global standards for responsible AI use.[30,31] Ultimately, these findings offer context-specific evidence to guide curriculum development and policy decisions in Nigerian pharmacy education.
This study provides a comprehensive analysis of AI integration among pharmacy students, revealing a landscape defined by high awareness but fragmented critical understanding. A significant majority of participants accurately identified AI’s meaning and educational applications, matching observations among Zambian pharmacy students and broader global trends in higher education.[7,18,28,32] However, a notable knowledge gap exists regarding AI’s inherent limitations, such as the tendency to oversimplify complex pharmaceutical topics and the resulting risks to academic integrity. This “utility-understanding gap” suggests that while students are eager to use these tools, they often lack the critical framework required to navigate the ethical implications of uncritical AI reliance.[14,33]
While the median knowledge score was generally favourable, with over half of the respondents classified as having “good” knowledge, the presence of a sizeable minority with poor understanding highlights the urgent need for structured educational interventions.[21,34,35] Interestingly, this study found higher knowledge levels than those reported in similar Zambian cohorts, a discrepancy likely rooted in differing institutional settings and data collection methods.[18]
Attitudes toward AI remain largely optimistic, with students viewing these tools as essential for personalising and enriching the learning experience.[32] Yet, this optimism is tempered by significant anxiety regarding over-reliance. Students expressed concerns that excessive dependence on AI could erode independent critical thinking skills, positioning AI as a potential “crutch” rather than a cognitive aid.[19,22,25,28,36] This uncertainty is exacerbated by a lack of formal institutional guidance, suggesting a disconnect where student adoption is outpacing official curricular frameworks. While most health professions students maintain positive outlooks, a substantial portion remains neutral or negative, contrasting sharply with the predominantly negative attitudes observed in other African contexts.[18,27,34,35]
Practically, the rapid integration of AI is undeniable. The vast majority of students utilise chatbots and writing assistants for content generation and summarisation, often through self-directed learning rather than formal instruction.[18,28,37] Encouragingly, two-thirds of students demonstrated “good” practice by engaging in information verification, showing that they do not passively accept AI outputs as absolute truth.[34,35] Despite this adaptability, systemic barriers like poor internet connectivity and a lack of technical training threaten equitable access.[7,18,20,32,38]
Statistically, the study identified a weak but significant positive correlation between knowledge and attitude, reinforcing the idea that increased exposure fosters acceptance.[19,21] However, the absence of a link between knowledge and actual practice reveals a “knowing-doing gap” where theoretical understanding does not always dictate usage behaviour.[39] Finally, the study year was identified as a key predictor of literacy; second-year students were the most vulnerable to poor knowledge, whereas final-year students showed greater proficiency. This suggests that AI literacy develops naturally over time but could be significantly enhanced if institutions implemented clear guidelines, ethical training, and redesigned assessments, such as oral or case-based components, starting early in the pharmacy curriculum.
This study’s findings are limited by a convenience sampling approach at a single institution, which introduces potential selection bias and limits broader generalisability. The cross-sectional design prevents causal inferences, while the reliance on self-reported data may include social desirability or reporting bias. Furthermore, the possible exclusion of students with poor lecture attendance and the omission of CGPA data to protect privacy may confound results related to academic level and performance.
5. CONCLUSION
This study demonstrates that pharmacy students possess foundational knowledge of AI-powered learning tools and generally hold positive attitudes toward their use, resulting in a modest adoption. However, critical gaps remain in understanding AI’s limitations and ethical implications. The findings highlight the need for structured educational strategies that go beyond tool introduction to promote critical thinking, ethical awareness, and responsible use. Addressing barriers related to training, conceptual understanding, and infrastructure, such as internet connectivity, is essential for equitable and effective AI integration. Embedding structured AI education within pharmacy curricula will help prepare digitally literate and ethically conscious future professionals capable of harnessing AI’s potential while mitigating its risks.
Acknowledgment
The authors sincerely thank all the pharmacy students who participated in this study.
Ethical approval
The research/study was approved by the Institutional Review Board at the Research and Ethical Committee of the Faculty of Pharmacy, University of Maiduguri, Nigeria, number FP/03/25/18-11-01/052, dated 24 July 2025.
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient has given consent for clinical information to be reported in the journal. The patient understands that the patient’s names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Conflicts of interest
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
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