Online graduate programs provide flexibility for working professionals, but they can also present challenges for creating interactive learning experiences. In asynchronous learning environments, students often engage with course materials independently, which can limit opportunities for real-time interaction, discussion, and applied practice. These limitations can be particularly significant in disciplines where professional communication and counseling skills are essential. The expansion of online and technology-supported learning environments in higher education has highlighted challenges related to faculty readiness, institutional infrastructure, and effective instructional design (Richards & Thompson, 2023). As institutions continue to adopt new digital tools, faculty and instructional designers must consider how technology can be used intentionally to support meaningful learning experiences rather than simply replacing traditional instruction.

The graduate course, Nutrition and Aging, presents one example of this challenge. The course introduces students to the nutritional, physiological, and social considerations associated with aging populations while preparing them for professional roles as registered dietitian/nutritionists who require counseling and education skills (Knight et al., 2024). In traditional classroom environments, counseling skills are often developed through role-play activities or interactive discussions. Replicating these types of applied learning experiences in asynchronous online courses can be difficult, particularly when students are participating from different locations and schedules.

To address this challenge, a collaboration between a faculty member and an instructional designer explored how generative artificial intelligence (AI) tools could be used to support engagement and provide opportunities for practice within an online learning environment. The goal was not simply to introduce new technology, but to integrate AI in ethical ways that aligned with course learning objectives and supported authentic skill development.

Several AI-supported strategies were incorporated into the course. The primary intervention involved the use of ChatGPT (OpenAI) to facilitate simulated counseling sessions in which students interacted with AI acting as a client seeking nutrition guidance. These simulations allowed students to practice counseling techniques and reflect on their communication strategies in a low-stakes environment. Additional tools were used to support engagement with course materials. Copilot (Microsoft) was used to generate an image of a fictional patient for a clinical case study discussion activity, and NotebookLM (Google) was used to generate podcast-style summaries from lengthy scholarly readings. The design and implementation of these generative AI-supported learning activities provided insight into how artificial intelligence can be integrated into asynchronous online courses to support engagement and applied learning.

Literature Review

Student engagement has long been recognized as a critical factor in effective learning, particularly in online environments where opportunities for interaction may be limited. Active learning strategies that encourage participation, discussion, and application of knowledge have consistently been shown to improve student outcomes. A widely cited meta-analysis by Freeman et al. (2014) found that students enrolled in courses incorporating active learning performed significantly better on examinations and were less likely to fail compared to those in traditional lecture-based environments. These findings highlight the importance of designing learning experiences that move beyond passive content consumption and instead create opportunities for students to actively engage with course material.

The shift toward online and hybrid learning environments has further emphasized the need for thoughtful integration of technology to support engagement and participation. The COVID-19 pandemic accelerated the adoption of digital tools in higher education, leading many faculty members to explore new approaches to technology-supported teaching (Aydın et al., 2023). While this transition expanded the availability of digital learning tools, it also revealed challenges related to faculty readiness, training, and effective instructional design (Richards & Thompson, 2023). Institutions have increasingly recognized that successful technology integration requires both pedagogical planning and faculty support structures that promote meaningful use of digital tools rather than adoption alone (Tuga et al., 2021).

More recently, generative artificial intelligence (AI) has emerged as a significant development in educational technology. Tools such as large language models have the potential to support content creation, feedback, and interactive learning experiences. Researchers have noted that generative AI may offer new opportunities to enhance teaching and learning (Michel-Villarreal et al., 2023). However, scholars have also raised concerns regarding ethical use, academic integrity, and responsible implementation of these technologies (Kooli, 2023). National policy guidance has similarly emphasized the importance of approaching AI integration thoughtfully, ensuring that new technologies support learning objectives and promote digital literacy rather than replacing critical thinking and human interaction (U.S. Department of Education, Office of Educational Technology [OET], 2023).

Within higher education, scholars have begun examining how generative AI tools can support experiential and practice-based learning. Experiential learning approaches emphasize the application of knowledge through authentic tasks, reflection, and iterative feedback. Salinas-Navarro et al. (2024) found that generative AI tools could help facilitate experiential learning by creating interactive scenarios that allowed students to explore real-world problems in simulated environments. These types of simulations may be particularly useful in online courses, where opportunities for live role-play or in-person activities may be limited. Similarly, Noviandy et al. (2024) argued that generative AI has the potential to transform higher education by enabling new forms of interaction and problem-based learning that were previously difficult to implement in digital environments.

Instructional designers are increasingly playing a key role in helping faculty navigate these emerging technologies. Research examining instructional designers’ perspectives on generative AI suggested that many designers view these tools as opportunities to support faculty innovation while maintaining alignment with learning outcomes and instructional best practices (Luo et al., 2025). Effective integration of AI often requires collaboration between faculty and instructional designers to ensure that activities are pedagogically meaningful, ethically implemented, and clearly aligned with course objectives. Guidance from the Online Learning Consortium, including recent professional development resources and faculty playbooks, emphasized the importance of structured support as institutions experiment with AI-enabled teaching strategies (Bailey Wilson et al., 2025; Lewis Miller et al., 2025).

Taken together, this body of research suggests that generative AI tools may offer promising opportunities to enhance engagement and experiential learning in online courses when implemented thoughtfully. However, practical examples demonstrating how faculty and instructional designers can collaboratively design AI-supported learning activities remain relatively limited. The project described in this article addresses this gap by exploring how generative AI tools were integrated into an online graduate-level nutrition course to support student engagement, digital literacy, and applied counseling practice.

Project Implementation

The integration of generative artificial intelligence tools into the Nutrition and Aging course was developed and implemented during the Fall 2025 semester through a collaboration between the course instructor (Dietetics Graduate Program Director) and an instructional designer in the university’s Center for Innovation in Teaching and Learning. As part of the broader course development work, this project focused specifically on integrating AI into course activities in ways that could support student engagement and provide opportunities for applied learning. The instructional goal was to introduce generative AI in ways that aligned with existing learning objectives while also helping students develop familiarity with emerging technologies that they may encounter in professional environments. The simulated experiences aligned closely with program educational goals and enabled students to develop the practical skills necessary for professional employment. The activities helped bridge the gap between learning in the online environment and real-world practice while supporting the program’s commitment to producing competent, practice-ready graduates.

Generative AI tools such as ChatGPT are increasingly being explored in higher education as instructional supports capable of facilitating interactive learning experiences and content generation (Chan & Colloton, 2024). With that in mind, several AI-supported activities were incorporated into the course to complement existing assignments and discussions. Three primary AI-supported strategies were implemented within this 15-week course: simulated counseling conversations using ChatGPT, a case study discussion supported by an AI-generated patient image using Copilot, and AI-generated podcast summaries designed to support engagement with course readings using NotebookLM.

Faculty–Instructional Designer Collaboration

The development of these AI-supported activities emerged from a collaboration between the course instructor and an instructional designer, initially framed through a university innovation technology grant. The faculty member applied for the grant with the goal of integrating generative AI into the course through a paid platform that would allow students to engage in both written and verbal counseling simulations. This initial vision focused on expanding opportunities for applied communication practice in an asynchronous environment.

After the grant was awarded, institutional constraints related to technology access, data security, and platform approval limited the use of paid AI tools. As a result, the instructor and instructional designer adapted their approach and implemented the activities using freely available generative AI tools. This shift to freely available AI tools required additional planning to ensure that the tools selected could still support the intended learning objectives while remaining accessible to all students.

The collaboration involved an iterative design process in which the faculty member identified key learning outcomes and disciplinary expectations, while the instructional designer provided guidance on activity structure, scaffolding, and alignment with online learning best practices. Together, they refined prompts, developed reflection components, and integrated AI-supported activities into existing course modules in a way that complemented, rather than replaced, traditional instructional elements. In addition to activity design, the collaboration emphasized student support and feedback. Clear expectations were established through rubrics, structured prompts, and preparatory activities on ethical AI use. The instructor also requested a Mid-Semester Instructional Diagnostic (MID), a service provided by the university that offers a simple and structured evaluation process using student feedback to help faculty improve their courses before the end of the semester. Insights from this process informed ongoing adjustments to the course and further supported the iterative nature of the collaboration.

The roles within the collaboration were complementary. The faculty member brought prior experience teaching the course and identified the need for enhanced opportunities for applied counseling practice, including the initial idea to incorporate AI-supported simulations. The instructional designer contributed expertise in structuring these activities for an online environment, including the development of scaffolding, alignment with learning objectives, and integration of reflection and assessment components. This division of roles allowed the course to retain disciplinary rigor while incorporating new instructional strategies grounded in established design practices. Constraints related to tool access and institutional policies required the team to adapt initial plans, reinforcing the importance of flexibility within the collaborative process.

Although this project originated through a grant-supported initiative, the collaboration model itself was not dependent on external funding. Instructional design support is available to faculty through the university’s Center for Innovation in Teaching and Learning, and the use of freely accessible AI tools demonstrates that similar implementations can be achieved without additional financial resources. This approach to using freely accessible AI tools highlights the potential for scalable and accessible integration of AI-supported learning activities across courses and disciplines.

The collaboration was supported through regular, biweekly meetings, which provided a structured opportunity to review course progress, discuss instructional challenges, and refine activity design. During these meetings, the instructional designer shared relevant examples from other courses, professional development trainings, and emerging practices in AI-supported instruction. These resources informed the development of the activities and helped ensure alignment with broader instructional design principles. The ongoing nature of these meetings allowed for iterative adjustments throughout the implementation process rather than a one-time course redesign.

Preparing Students for Responsible AI Use

In Week 1, before students even interacted with generative AI tools in the course, an introductory activity was used to establish expectations for responsible and ethical AI use. Students were first assigned The Student Guide to AI (Anderson et al., 2025), which provided an overview of generative AI technologies and discussed appropriate uses of artificial intelligence in academic and professional contexts. After reviewing the guide, students completed a discussion post reflecting on the potential benefits and limitations of AI tools. The discussion encouraged students to consider questions related to academic integrity, transparency in AI use, and how artificial intelligence might support professional work without replacing critical thinking.

This introductory AI literacy activity served two purposes. First, the review materials and discussion prompt provided students with foundational knowledge about generative AI technologies before students interacted with these technologies directly in course assignments. Second, the activity and assignment established clear expectations regarding responsible generative AI use within the course. Framing AI as a support tool rather than a shortcut helped position the later assignments as opportunities for learning and experimentation rather than as mechanisms for completing coursework more quickly.

AI-Assisted Case Study Development

In Week 4 AI tools were also used to enhance a case study prompt within the course. Case studies are frequently used to help students apply course concepts to realistic scenarios. However, written case studies can sometimes feel abstract in online environments. To create a more engaging scenario for a case study from the course textbook, Copilot (Microsoft) was used to generate an image representing a fictional patient. This image was included alongside a written case study describing the patient’s health background, lifestyle considerations, and nutritional concerns. Students reviewed the case study and participated in a structured discussion forum in which they analyzed the scenario and proposed potential counseling strategies. To ensure students completed the case study individually, they were required to post their initial responses before they could view classmates’ responses. Initial posts were designed to require application of course concepts to the patient’s specific health background and nutritional concerns rather than simply summarizing information. To promote meaningful peer interaction, students were provided with clear expectations for responses using the “3 C’s and a Q” framework, which encouraged each reply to include a compliment, a substantive comment on the content, a connection to course concepts, and a follow-up question. A detailed rubric was also provided to guide both initial posts and peer responses, reinforcing expectations for depth, application, and engagement. This structure supported more thoughtful discussion by prompting students to build on one another’s ideas, connect theory to practice, and engage in more meaningful interaction (Freeman et al., 2014).

The presence of a patient image provided additional context and helped students visualize the individual behind the clinical scenario. Research supports the inclusion of educational tasks that humanize simulated patients by emphasizing the importance of realism in simulation-based learning (Knight et al., 2024). When simulated patients are portrayed with depth, through either character development or lived experiences, students are better able to empathize and engage in authentic interactions that mirror real clinical encounters. Although the addition of an AI-generated patient image may appear minor, it played an important role in humanizing the case study. By providing a visual representation of the patient, the activity encouraged students to consider the individual behind the clinical scenario, supporting more empathetic and context-aware responses. The use of AI-generated patient imagery aligned with research emphasizing the importance of realism in simulation-based learning and strengthens the connection between course content and real-world application. Overall, incorporating more humanized simulated patient experiences strengthens the transfer of communication skills from the classroom to real-world dietetics settings (Knight et al., 2024).

This approach also presents opportunities for further development. Future implementations could expand the use of AI-generated assets to include a series of case studies with more detailed and varied patient profiles, allowing students to engage with a broader range of clinical scenarios. While time constraints and limitations in AI tool access during this implementation limited the depth of this component, the use of freely available tools demonstrates that this type of expansion is both feasible and accessible for instructors seeking to enhance case-based learning.

AI-Supported Counseling Simulations

The primary activity implemented in the course involved simulated counseling conversations using ChatGPT (OpenAI). The goal of this activity was to provide students with an opportunity to practice communication strategies within a simulated client interaction. This assignment was completed in three phases over the course of the 15-week semester. In Week 3, students were asked to use ChatGPT as a simulated nutrition client to practice asking and evaluating client-style nutrition questions, critically assess the AI’s accuracy using evidence-based sources, compare responses to professional nutrition counseling, and reflect on how question quality influences counseling effectiveness.

In Week 9, students were asked to initiate a conversation with ChatGPT and prompt the system to respond as a fictional client seeking guidance related to nutrition and aging. The instructor provided initial scenario prompts that described the client’s background, health concerns, and dietary challenges. Students then conducted a counseling-style conversation with the AI client. During the interaction, students asked questions, provided recommendations, and responded to follow-up concerns generated by the AI chatbot. Because the responses from the AI varied depending on the student’s questions and recommendations, each interaction developed slightly differently. This process was repeated with a different AI client in Week 13.

The conversational format created an experience that felt more dynamic than traditional written case studies. Students were required to think about how they communicated their recommendations and how those recommendations might be interpreted by a client.

To support consistency and guide student interactions, the instructor provided structured prompts for the simulation. Students were instructed to begin by prompting the AI to assume the role of a client with defined characteristics, such as age, health concerns, and dietary challenges. The interaction required students to conduct a counseling-style conversation that included initial assessment questions, tailored recommendations, and follow-up responses. Each activity concluded with a written reflection in which students evaluated their communication strategies, identified strengths and areas for improvement, and connected their experience to course concepts related to nutrition counseling. One student reflected:

ChatGPT explained that I displayed empathy, validation, and excellent person-centered tailoring throughout (Southern flavors, spoonable textures, morning focus). It perfectly complemented real and clear objectives and concrete environmental actions. To improve, it suggested incorporating an importance and confidence scale to enhance commitment, more complex reflections to entirely capture the feelings behind what she calls ‘unfamiliar foods’ and to conclude with a short teach-back to allow [the simulated patient] to restate the plan in her own words.

The reflective component was an important element of the assignment. This reflection encouraged students to analyze their interactions with the AI client and connect the activity to course concepts related to nutrition counseling and client communication. Although the assignment offered a more dynamic learning experience than a traditional case study, students noted challenges in authentically engaging with the chatbot as a real client and expressed that the absence of nonverbal communication cues limited the realism of the simulated interaction.

Supporting Engagement with Course Readings

Over the duration of the course, four AI-supported podcast summaries were used to support engagement with course readings. This final AI-supported strategy involved using NotebookLM (Google) to generate podcast-style summaries of selected course readings.

Graduate-level courses often include scholarly articles that contain complex terminology and dense explanations of research findings. To support engagement with these materials, selected articles were uploaded into NotebookLM. The system generated conversational summaries highlighting key ideas from the readings in a format that resembled a podcast discussion. Students were provided with both the original article and the AI-generated summary and were encouraged to consult both the original source material and the accompanying podcast to support comprehensive understanding and critical evaluation of the content. This approach ensured that students still had access to the full scholarly source while also providing an alternative way to review the material.

After reviewing the podcasts, students completed short quizzes designed to reinforce key concepts from the articles. Students were permitted up to two attempts on each quiz, with the second attempt limited to questions answered incorrectly on the first attempt, to reinforce mastery of key concepts and support targeted learning. The podcasts served as an additional resource that could help students identify the major themes of the readings before engaging with the full text. Students were given the option to engage with the podcast prior to completing the assigned reading, allowing flexibility in how they accessed and processed course content. Providing multiple formats for engaging with course material can support different learning preferences and make complex information more accessible while maintaining the rigor expected in graduate-level coursework (Taheri et al., 2021). Knowledge of various learning preferences allows instructors to design learning experiences that better align with how the students process information. Research on online education highlights the importance of accommodating diversity in learning preferences (Alonso-Martin et al., 2021).

Project Outcomes

Mid-semester feedback provided preliminary insight into student perceptions of the redesigned course. Both students indicated strong agreement that the course provided sufficient opportunities to practice concepts and that course examples reflected real-world scenarios. Although enrollment in this pilot offering was small (n=2), these student responses suggest that the integration of AI-supported activities contributed to perceived engagement and relevance within the course.

Integrating artificial intelligence activities into the Nutrition and Aging course created new opportunities for student engagement, applied skill development, and interaction with course materials along with assignments on ethical AI usage. Although the project was implemented primarily as an instructional innovation rather than a formal research study, observations from course implementation and the mid-semester student feedback provide insight into how AI tools can support learning in asynchronous environments.

Students’ comments in reflective writing assignments provided qualitative indicators of how students experienced the AI-supported activities. For example, one student noted:

Multiple things went well in the session. I was impressed with the quality response AI had to my questions. I did well in establishing rapport with the client in the beginning. Summarizing and restating what [the simulated patient] said made the flow of the counseling session go smoothly. I did a good job of using motivational interviewing techniques, like using open-ended questions to gather data to determine how to help her food intake.

These indicators suggested not only increased engagement with course material, but also deeper levels of critical thinking and self-awareness regarding their communication practices. Students frequently referenced specific moments from their AI interactions, indicating that the experiences were useful and meaningful.

Increased Student Engagement

One of the most noticeable outcomes was an increase in student engagement during the counseling simulation assignment. Traditional discussion boards often require students to respond to prompts or comment on one another’s posts. While these activities can support reflection and knowledge sharing, they do not always replicate the interactive nature of real-world counseling interactions. The AI-supported counseling simulations introduced a different type of learning experience. Instead of responding to a static prompt, students were engaged in an ongoing conversation that required them to ask questions, interpret responses, and adapt their recommendations. One student stated:

Summarizing and reflecting on past comments in the counseling session was a way for me to gain information on concerns I noticed from our conversation. For example, in one comment, she stated she had breakfast but was not hungry. I wanted her to elaborate on what she had for breakfast, but also ask her about her hunger level. First, I asked her about her breakfast intake. Later in the session, I state, “You mentioned earlier that you don’t feel hungry at times. Could you tell me more about your appetite?”

This interactive format encouraged students to think more carefully about how they communicated their ideas and how their recommendations might be received by a client.

These observations related to student engagement are consistent with emerging research suggesting that AI-supported learning activities can increase engagement when integrated intentionally into course design (Bailey Wilson et al., 2025). By incorporating interactive elements that require students to apply course concepts in a more authentic yet low-stakes learning environment, AI-supported activities can create opportunities for deeper engagement with course material. Student comments further supported these observations. One student noted, “I love getting to use different resources to learn how to counsel well,” while another encouraged the continued use of "new websites and resources, like AI, to be up to date on the latest tools for future practice.

Development of Applied Counseling Skills

The counseling simulations also provided an opportunity for students to practice professional communication strategies. Counseling requires more than simply providing nutritional recommendations; it also involves building rapport, asking appropriate questions, and adapting communication based on client responses (Spahn et al., 2010). The simulated interactions allowed students to experiment with different approaches to client communication. Because the conversation occurred in a simulated environment, students were able to test ideas and refine their strategies without the pressure associated with real client interactions. The reflective component of the assignment further reinforced this learning process. Students were asked to analyze their conversations and identify areas where they could improve their communication strategies.

Students identified gaps between what they intended to communicate and how their responses were perceived by the AI client. For example, some students reflected on instances where they needed to increase collaboration with the client when goal setting, or where they missed opportunities to ask follow-up questions. One student stated:

The SMART goal for [the simulated patient] was to increase food intake with the new meal changes by the follow-up meeting in 2 days. The AI grader stated that I could improve my SMART goal conversation by making the SMART goal clearer. The SMART goals should have been more specific, such as “eat at least 75% of meals until the follow-up meeting in two days”.

This level of self-assessment is important in developing effective counseling skills and can be difficult to achieve through traditional written assignments. These reflections encouraged students to think critically about their counseling approach and to connect course concepts with real-world professional practice.

Engagement with Course Readings

The use of NotebookLM-generated podcast summaries influenced how students interacted with assigned readings. In designing the activity, consideration was given to the length and technical language of graduate-level articles, which can affect how students engage with complex material. Providing a podcast-style summary allowed students to review key concepts in a conversational format before engaging with the full article. Students were still required to access the original readings and complete quizzes based on the material, ensuring that the academic expectations of the course were maintained. However, the podcast summaries provided an additional entry point for engaging with the material. Recent global analyses of artificial intelligence adoption in education emphasize the importance of designing AI-supported learning activities that promote meaningful engagement rather than passive consumption of AI-generated content (Digital Education Council, 2025). In this course, the AI-generated summaries were used to complement the readings rather than replace them. This serves as an example of how teaching strategies in higher education can be adapted to ensure that AI enhances student engagement and learning.

In addition, by designing instruction that appeals to diverse learning preferences, educators help ensure that students with different strengths and study habits can successfully access and process course material. In online learning environments, this flexible and inclusive approach to instruction is essential for supporting diverse student populations and maximizing academic success (Alonso-Martin et al., 2021). A variety of learning approaches support a wider range of learners. While the podcast summaries did not replace the need for reading the scholarly articles, they appeared to reduce barriers to engagement. One student stated, “I enjoyed the podcast quiz….Reading the article and listening to the podcast helped me understand the content. I am an audio/visual learner so this helped a lot.” This approach supported both comprehension and efficiency, which are critical in graduate-level online programs.

Design and Implementation Insights

Several insights emerged from the implementation of these activities. One observation was the importance of providing clear instructions for AI interactions. Students needed guidance on how to structure their conversations with the simulated client and how to frame their counseling recommendations effectively. The reflection component of the counseling simulation assignment also proved to be an important element of the activity. Without the reflection component, the counseling simulation could have been perceived simply as an interaction with a chatbot. The required reflection component ensured that students connected their experiences with course concepts and professional counseling strategies (Salinas-Navarro et al., 2024). Another key lesson involved positioning AI tools as supplements to existing course materials rather than replacements for traditional assignments. The course maintained its existing readings, discussions, and assessments while incorporating AI tools in ways that enhanced engagement and practice opportunities. Overall, the project demonstrated that AI tools can be integrated into asynchronous courses in ways that support applied learning and meaningful engagement with course content.

Recommendations for Future Implementation

Based on the outcomes of this project, several recommendations can be made for educators interested in implementing similar AI-supported activities. First, clearly align AI activities with learning objectives (OET, 2023). The success of this project was largely due to the intentional integration of AI tools with existing course goals, particularly those related to communication skills. Second, incorporate self-reflection into AI-based assignments (Salinas-Navarro et al., 2024). Reflection elevated AI interactions from simple activities into meaningful learning experiences by encouraging students to critically evaluate their performance and connect practice to theory. Third, use AI as a supplement rather than a replacement for traditional instructional materials (Mollick & Mollick, 2023). AI tools should enhance access, engagement, and practice opportunities without replacing foundational learning experiences.

Finally, provide clear guidance for student interactions with AI (Bailey Wilson et al., 2025). Many students may be unfamiliar with how to engage with generative AI tools. Providing prompts, examples, and expectations can improve the quality of student experiences and outcomes. It is also important to prepare students for using AI ethically. Integrating discussions about academic integrity and appropriate use of AI helps students develop digital literacy skills that will be increasingly important in professional environments (Kooli, 2023).

Limitations

This project had limitations that should be considered when interpreting the findings. First, course enrollment in this pilot offering was small (n=2), reflecting its initial implementation. The course is scheduled to be offered again in Summer 2026, when enrollment is expected to be approximately 10 students. Second, the outcomes described are based primarily on qualitative observations and student feedback rather than a formal experimental design. Finally, the implementation was supported in part by institutional resources, including an innovation grant, which may not be available in all instructional contexts. Despite these limitations, the project provided a practical example of how generative AI tools can be integrated into course design to support applied learning and engagement.

Conclusion

The integration of generative artificial intelligence tools into the Nutrition and Aging course demonstrates how emerging technologies can support engagement and applied learning in asynchronous online environments. Through a collaboration between a faculty member and an instructional designer, the course incorporated AI-supported counseling simulations, an AI-assisted case study, and AI-generated podcast summaries designed to support engagement with course readings.

Among these activities, the counseling simulations provided the most significant opportunity for applied practice. By interacting with a simulated client through ChatGPT, students were able to practice counseling strategies and reflect on their communication approaches in a low-stakes environment. These experiences helped bridge the gap between theoretical course concepts and the practical communication skills required in nutrition counseling. The use of NotebookLM-generated podcast summaries demonstrated how AI tools can support engagement with complex course materials. By providing an alternative format for reviewing scholarly articles, the course created additional pathways for students to interact with the material while still maintaining the rigor expected in graduate-level coursework.

One of the most important insights from this project is that the effectiveness of AI tools depends largely on thoughtful instructional design. Artificial intelligence can enhance learning experiences when it is integrated intentionally and aligned with course learning objectives. When paired with reflective activities and traditional instructional strategies, AI tools can support active learning rather than simply automating academic tasks.

As artificial intelligence technologies continue to evolve, educators must consider how these tools can support inclusive and flexible learning environments that align with principles such as universal design for learning (Mallary et al., 2025). Collaboration between faculty and instructional designers will remain essential as institutions explore practical ways to incorporate AI into teaching and learning. This project illustrates one approach to integrating generative AI into an asynchronous online course. When aligned with authentic practice, engagement, and clearly defined learning objectives, AI can serve as a valuable tool for enhancing learning experiences across disciplines. These findings suggest that the effectiveness of generative AI in educational settings is driven less by the tools themselves and more by how they are integrated through intentional instructional design.