by Sanja Avramovic, PhD
Associate Professor, George Mason University, College of Public Health, VA
Corresponding author

Hedyeh Mobahi, PhD
Assistant Professor, George Mason University, College of Public Health, VA

Citation: Avramovic, S. & Mobahi, H. (2025). Redesigning a health informatics course for nursing students through AI-driven instruction and clinically contextualized assignments. Canadian Journal of Nursing Informatics, 20(4). https://cjni.net/journal/?p=15674

Abstract

Background: Nursing roles increasingly demand strong informatics competence. However, traditional informatics courses often lack relevance for nursing practice. This study describes the systematic redesign of an undergraduate Health Informatics (HI) course created for health administration students into a version tailored for nursing students, integrating AI-generated instructional content, clinically contextualized assignments, and accessibility-enhancing tools.

Methods: AI tools were used to convert lecture materials into avatar-narrated videos and to generate nursing-specific clinical scenarios for assignments. A mixed-methods evaluation included a voluntary end-of-course survey of nursing students in the redesigned course (n = 28); pretest–posttest comparisons within this AI-enhanced group, and comparison to a control group (n = 25) of students who took the course without AI. enhancements.

Results: The majority of nursing students (16/28, 57.1%) reported AI-generated lectures were more helpful than assigned readings, and 22/28 (78.6%) agreed the tools improved learning. Statistically significant knowledge gains were observed in the AI group (p = 0.0011, Cohen’s d = 0.85), but not in the control group (p = 0.2077, Cohen’s d = 0.29).

Conclusion: AI-enhanced, clinically contextualized course design may improve engagement and learning outcomes in nursing informatics education. Future research should test these approaches with larger and more diverse nursing cohorts.

Keywords: Nursing Informatics, Health Informatics Education, Artificial Intelligence in Teaching, Course Redesign, AI-Generated Lectures, Avatar Narration

Introduction

In modern healthcare environments, nurses are expected to integrate informatics into patient care, navigate complex electronic health records (EHRs), and apply data-driven decision-making (American Association of Colleges of Nursing [AACN], 2021; Hebda & Czar, 2013). Undergraduate informatics courses are often designed for broader healthcare audiences, particularly health administration, resulting in content that focuses on technical systems design and business processes, rather than clinical workflows and patient care.

At a large public university, the undergraduate Health Informatics (HI) course was originally created for health administration and policy and health informatics students, emphasizing process improvement, vendor evaluation, and the business value of health IT. Over time, enrollment patterns shifted, with increasing numbers of nursing students taking the course. Nursing students and faculty identified a gap: while core informatics principles were present, assignments and examples were not aligned with nursing practice. Our goal was to adapt the course for undergraduate nursing students while preserving essential informatics concepts. The health administration course served as the foundation because it already included structured modules on health IT evaluation, regulatory frameworks, and emerging technologies—components that, once contextualized, could directly serve nursing education goals. This redesign employed artificial intelligence (AI) technologies to generate instructional content and enhance accessibility. Instructional materials were converted into AI-generated, avatar-narrated video lectures. Course assignments were revised to reflect clinically contextualized scenarios relevant to nursing practice. The assignments were developed based on clinically contextualized scenarios.

This study evaluates the effectiveness of these instructional innovations through student feedback and performance measures, aiming to determine whether AI-enhanced instruction and clinically contextualized assignments improve nursing students’ engagement and learning outcomes compared to traditional instructional approaches.

Background and Significance

The integration of informatics into nursing education has become a curricular priority, as reflected in national standards such as those set by the American Association of Colleges of Nursing (AACN, 2021). Prior research has shown that instruction is most effective when tailored to the professional context (Hebda & Czar, 2013).  The integration of Artificial Intelligence (AI) into nursing education presents opportunities alongside challenges. Topaz et al. (2025) indicated that generative AI enhances student learning through realistic patient scenarios, personalized interactive experiences, and assistance with academic assignments.  Hwang et al. (2022) evaluated AI-based clinical education tools using quantitative performance metrics such as test scores, completion rates, and time-to-completion. Their findings supported the use of AI for performance measurement and instructional effectiveness assessment. Other research highlights mixed perceptions among nursing students, including enthusiasm for efficiency gains and concerns about ethics and preparedness (Rony et al., 2025).  Labrague and Al Harrasi (2025), used the Technology Acceptance Model (TAM) to demonstrate the importance of perceived usefulness and ease of use in students’ acceptance of AI tools. Their findings indicated positive attitudes significantly predict behavioral intentions. Srinivasan et al. (2024) examined pedagogical implications of AI and chatbots in nursing education, exploring their potential to enhance student engagement, motivation, and learning outcomes. Avramovic & Avramovic (2024) explored the benefits and limitations of using AI tools in classroom assessments. Another study (Avramovic et al., 2024) examined the integration of AI-assisted programming into health informatics education.   These studies reinforced the role of AI as both a supplementary tool and a catalyst in shaping educational workflows and student engagement.

While the literature broadly supports AI for enhancing instruction (Luckin et al., 2016; Zawacki-Richter et al., 2019), fewer studies have examined AI-enhanced health informatics courses specifically tailored for nursing students.

This paper specifically focuses on the redesign of a health informatics course as a way to help nursing students with challenging technical and quantitative tasks. The study incorporated AI-generated instructional content and clinically contextualized assignments. Unlike broader studies, it quantitatively measures the effectiveness of AI-driven methods, demonstrating improvements in knowledge retention and student engagement compared to traditional instructional methods and provides detailed empirical evidence on AI’s direct educational outcomes and specific instructional strategies explicitly designed for nursing students in a health informatics context.

Methods

The redesign was implemented in a Spring 2025 undergraduate Health Informatics course at a large public university. The AI-enhanced group (Cases) included 28 undergraduate nursing students, and the control group included 25 students from a separate section, including health administration and policy, health informatics, and other non-nursing majors.

The existing version, originally developed for health administration, policy, and informatics students, focused heavily on process improvement, the business value of information technology, and the participatory evaluation of electronic health record vendors and AI applications. To make the course accessible to nursing students, instructors converted lecture materials from text and PDFs into slide decks using AI text-to-presentation software. These slides were narrated by AI-generated avatars to create short, self-contained video lectures on core topics such as HIPAA compliance, clinical decision support, and predictive analytics. Assignments were rewritten to reflect nursing-specific problems, including evaluating EHR usability, creating clinical decision support (CDS) tools that reduce alert fatigue, and designing a security protocol following a data breach. One assignment employed Bayesian probability to analyze diagnostic data: for example, assessing the likelihood of meningitis given the presence of a stiff neck.

To evaluate the effectiveness of the redesign, a 12-question survey was distributed online during the final week of the course. Participation was voluntary. Of the 38 enrolled students in the case group, 30 completed the survey, and 28 submitted fully completed responses (response rate: 73.7%). The survey included Likert-scale, multiple-choice, and open-ended questions to assess students’ engagement, format preferences, and perceptions of AI tools. Potential biases due to voluntary participation and self-reporting methods were acknowledged, noting that responses may reflect varying levels of motivation and subjective perceptions.

A pretest-posttest comparison was conducted involving the Case group, nursing students from the current semester using AI-enhanced instructions, and the Control group, students from a different section of the class taught using traditional instruction without AI. The survey questions and students’ responses are provided in Table 1 (quantitative questions) and Table 2 (qualitative questions).

Table 1

Quantitative Survey Questions and Responses

Table 2

Qualitative Survey Questions and Responses

Results

Among the 28 completed responses, 14 (50.0%) students reported having intermediate familiarity with AI tools, while 5 (17.9%) considered themselves advanced users. A total of 16 (57.1%) students stated that they found AI-generated video lectures either “somewhat more helpful” (n=12, 42.9%) or “much more helpful” (n=4, 14.3%) than assigned readings when learning new topics. In contrast, 5 (17.9%) students slightly preferred the paper readings, and 7 (25.0%) students rated both formats as equally helpful.

When comparing AI lectures to traditional video lectures, 13 (46.4%) students preferred the AI format, 4 (14.3%) preferred the recorded traditional lectures, and 7 (25.0%) had no strong preference. Only 1 student reported that the recorded lectures were more helpful and preferred that format.

Regarding the overall course impact, 22 (78.6%) students agreed that the inclusion of AI-tools improved their learning. Additionally, 17 (60.7%) reported using AI-tools either occasionally (13, 42.9%) or frequently (5, 17.9%) when allowed by the instructor.

Qualitative responses to the essay questions supported the quantitative trends. When asked about the most beneficial aspects of the AI-generated videos, 21 of 28 students (75.0%) emphasized the clarity, structured pacing, and ability to rewatch difficult sections. One student noted, “The AI videos were more direct and helped me grasp hard topics faster.” Another shared, “I could slow down and go over confusing parts until it made sense.”

When asked to express their preference between AI-guided and traditional lectures, 9 students stated a clear preference for AI-guided lectures, citing pacing and clarity as strengths, and 11 (39.3%) preferred traditional lectures for their energy and spontaneity. A neutral position was reported by 8 (28.6%) students, who either saw value in both formats or preferred a hybrid model.

A final open-ended question invited suggestions for future use of AI-tools in the course. Several students (28.6%) recommended using AI-generated lectures as supplements to traditional materials. Others (10.7%) suggested improvements, such as adding emotional tone to the avatars or embedding practice quizzes into the videos.

Statistical analyses were conducted to assess the significance of students’ preferences for AI-generated instructional materials. Chi-square tests were applied to determine whether students’ responses to multiple-choice survey questions deviated significantly from an even distribution.

For the topic of predictive analytics (Q2), 57.1% of students found the AI video to be “somewhat” or “much more helpful” than the assigned reading. A chi-square test confirmed this distribution was statistically significant (?² = 18.80, p = 0.0009), though a binomial test with 16 out of 28 students preferring AI did not reach statistical significance (p = 0.286). Similarly, in the comparison between AI video and reading on precision vs. personalized medicine (Q3), 57.1% of students found the AI format more helpful, with the distribution again significantly non-uniform (?² = 15.20, p = 0.004). For clinical decision support tools (Q4), 53.6% preferred the AI video over traditional module content, while only 3.6% favored the traditional lecture; the preference pattern was highly significant (?² = 22.73, p < 0.001).

When evaluating the AI video explanation of a discrete math problem (Q5), 46.4% found it more helpful, compared to 28.6% who preferred the recorded class lecture, and the distribution was again statistically significant (?² = 14.00, p = 0.007). In terms of pacing (Q6), a remarkable 96.4% of students rated the AI video pacing as either “appropriate” or “somewhat appropriate,” a consensus supported by a significant chi-square value (?² = 12.66, p = 0.002). However, when asked about their likelihood of recommending AI videos to peers (Q7), although 78.6% responded positively, this result did not show a statistically significant pattern (?² = 2.88, p = 0.237). These findings suggest strong student engagement with AI-based content across most learning contexts, though not universally.

To assess the impact of integrating AI-driven instructional methods into the redesigned course, a pretest-posttest comparative analysis was conducted. The Cases group consisted exclusively of nursing students who enrolled in the course during the current semester, benefiting from AI-generated instructional content such as avatar-narrated lectures, clinically contextualized assignments, and interactive multimedia materials. Students completed a pretest at the beginning of the semester, before AI-tools were introduced, and a posttest at the end to measure the instructional efficacy of the implemented methods. The control Group included a diverse cohort of students who completed the course in the same semester but received traditional instructional formats without AI enhancements.

Statistical analyses indicated a significant improvement in the Cases group’s performance from pretest to posttest (paired t-test: t = -3.78, p = 0.0011), reflecting substantial knowledge gains attributable to the AI-driven methods. The Control Group demonstrated no statistically significant improvement over the same period (paired t-test: t = -1.28, p = 0.2077). The results of analysis demonstrated a significant improvement and substantial effect size for the nursing students who participated in AI-enhanced instruction compared to the traditional methods. The Cases group, exposed to AI-driven instruction, exhibited significant improvement from pretest to posttest (p = 0.0011), with a large effect size (Cohen’s d = -0.85). The Controls group, receiving traditional instruction without AI, showed no significant change (p = 0.2077), with only a small effect size (Cohen’s d = -0.29). Direct comparison of posttest scores between the two groups showed a significant difference (p < 0.0001), with a very large effect size (Cohen’s d = -1.10), indicating that the AI-enhanced instruction was more effective in facilitating student learning and knowledge retention.

An independent t-test comparing posttest scores between the Cases and Control Group confirmed the effectiveness of the AI-enhanced instruction, with nursing students in the Cases group outperforming the mixed cohort in the Control Group (t = -4.67, p < 0.0001).

Discussion

The study’s results suggest that AI tools and clinically relevant content can significantly improve engagement and learning outcomes for nursing students in a Health Informatics course. Over half of the students (57.1%) found AI video lectures more helpful than traditional materials, and nearly four out of five (78.6%) believed these tools enhanced their overall learning experience.

The ability to replay and control the pace of AI lectures contributed to their perceived usefulness. Additionally, 75.0% of students indicated that the AI lectures offered better clarity than standard readings or recorded lectures. These findings align with the growing body of research on the role of AI in health informatics education, including prior evaluations of AI text generation (Avramovic & Avramovic, 2024) and AI code assistance (Avramovic et al., 2024). Statistical analyses revealed significant improvements from pretest to posttest scores among students using AI-enhanced instruction (p = 0.0011, Cohen’s d = -0.85), while no significant changes occurred in the control group (p = 0.2077, Cohen’s d = -0.29). The direct comparison of posttest outcomes between the two groups indicated a highly significant advantage for the AI-driven instructional approach (p < 0.0001, Cohen’s d = -1.10).

The redesign also demonstrated that embedding real-world nursing scenarios into assignments increased their relevance and applicability. Students showed higher engagement when solving problems such as evaluating an EHR for a small clinic or designing a CDS intervention to reduce alert fatigue.

Despite these strengths, some limitations were evident. Approximately 39.3% of students expressed a preference for traditional lectures, citing concerns about robotic tone and lack of spontaneity in AI narration. Several students remarked that human instructors provide contextual knowledge and can adapt dynamically to student questions. This highlights a key challenge in AI education: maintaining emotional engagement while automating content delivery.

Another limitation lies in the self-reported nature of the survey data, which may be subject to bias. To address concerns about the robotic tone of AI lectures, emotionally expressive avatars can be integrated. Incorporating interactive features such as embedded quizzes may further enhance student engagement. The plan is to replicate this design in all sections of the health informatics class, during the next academic year.

Future iterations should explore hybrid models that combine AI-tools with live, interactive sessions. Additional research is needed to measure long-term knowledge retention and to optimize AI-tools for healthcare education environments.

Implications for Professional Nursing Development

The integration of AI-driven educational strategies into nursing curricula holds significant promise for professional development. The enhanced accessibility and clarity provided by AI tools can substantially improve nurses’ informatics competence, essential for navigating complex healthcare systems. AI-assisted instructional methods foster self-paced learning, allowing nurses to better manage their educational needs alongside professional responsibilities. Incorporating AI-generated clinically contextualized content helps bridge theoretical knowledge with practical application, thereby reinforcing critical thinking and clinical decision-making skills. Familiarity with AI technologies equips nurses to effectively utilize emerging digital healthcare tools, positioning them as leaders in technology-driven care environments. Embracing AI in nursing education can impact nursing practice, education, and leadership.

Acknowledgment

The authors thank the students who participated in this study for their valuable feedback and engagement. While the description of AI usage in developing course content and assignments is detailed in the Methods section, we note here that the manuscript underwent final grammar and clarity review using AI-based text editing software. The authors retained full control over all intellectual content and final wording.

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Author Biographies

Sanja Avramovic, PhD

Corresponding author

Associate Professor

George Mason University, College of Public Health, Fairfax, VA

https://publichealth.gmu.edu/profiles/savramov

Hedyeh Mobahi, PhD

Assistant Professor

George Mason University, College of Public Health, Fairfax, VA

https://publichealth.gmu.edu/profiles/hmobahi2