Conversational AI agents are computer programs that talk to users like humans using natural language processing and machine learning. In pharmacy education, these AI agents help students and professionals practice talking with patients, manage work tasks, and make better clinical decisions.
From 2020 to 2025, a review found almost one thousand studies, but only six focused on conversational AI in pharmacy education. Five of these were done in English-speaking countries, mostly the United States. This shows that the use of AI in this area is just starting, with room to grow and improve.
Pharmacy training needs many complex communication skills. These include talking to patients and working with others, like managing staff and special care areas such as HIV treatment. AI agents use learning modules with scenarios and give feedback right away. This helps learners practice again and again and get quick answers to their choices, which is not easy with regular training.
The AI agents used in pharmacy education often share some main features. Most use text-based chats. Some also have voice or video parts to help different learning styles.
A common way to use these AI agents is through a single-user system. This lets the AI give feedback just for one learner, making the training fit that person’s needs. This helps learners feel more confident and remember what they learned, as shown in reviews of different studies.
The main features of these AI designs include:
These features help AI agents support goals like improving communication and operational skills in pharmacy settings.
Research on AI agents in pharmacy education goes through different stages of evaluation: checking if the system works, if it helps users, and if it works well in real life.
Out of six studies, three looked at stage one, two at stage two, and one at stage three. Even though few studies exist, results show that AI agents can improve learners’ confidence, knowledge, and communication skills.
Common outcome measures include:
These help managers decide if the AI is working well in education programs.
The evaluation model for these AI agents uses guidance from the World Health Organization’s (WHO) digital health framework. This framework helps make sure digital health tools are safe, effective, and easy to access worldwide.
Researchers added eleven educational features and three result types based on WHO’s ideas to create a strong evaluation method for pharmacy education. This makes sure AI agents are tested not just for technical performance but also for their teaching impact in ways that can be scaled.
Using WHO’s framework helps healthcare groups trust that the AI tools meet global quality standards. This is important when adding AI to sensitive areas like pharmacy training.
Pharmacy managers, owners, and IT staff in the U.S. need to know how AI agents fit into daily work. Pharmacy work is becoming more complex and needs help with communication and administration.
AI agents can be used in several ways:
IT teams must consider data security, system compatibility, and user-friendly designs when adding AI. Choosing AI that follows HIPAA rules and uses cloud systems helps keep data safe and systems strong.
Managers and IT staff should see AI agents as part of bigger workflow tools. Automating boring communication tasks can make pharmacies work better and reduce mistakes.
Even with benefits, AI agents face challenges in health education and pharmacy jobs. One big issue is that few places use these AI systems. This may be because people don’t fully know how to use them well or there is not enough proof that they work well outside studies.
How users interact with AI is different and often not recorded well. Better tracking of learner behavior in AI training could show what works and why.
More research is needed to prove AI agents work in fields beyond pharmacy, like nursing and patient counseling. Looking at more areas will help find where AI can help most.
For pharmacies in the U.S., investing in AI today means joining a field that is still growing. Working with vendors who use WHO-based evaluation methods can help make AI safer and more useful.
For healthcare leaders in U.S. pharmacies, AI agents offer ways to improve staff training and patient communication. Using an evaluation method based on digital health rules helps leaders decide about buying, using, and managing AI training tools.
Pharmacy owners may save money on training and raise staff skills with AI modules that show real-life situations. Healthcare managers should think of AI as a helper for human staff, letting people focus on tasks that need experience and judgment.
IT managers have an important job making sure AI fits well into existing systems and stays safe. Checking for security, compatibility, and updates can lower risks and help users have a better experience.
Together, these groups can help their organizations use new digital health tools to improve pharmacy training and work.
The development of conversational AI agents is a step toward using technology to meet the changing needs in U.S. pharmacy education and practice. A thorough evaluation method, based on WHO digital health rules, offers a way to check and improve these systems for safe and effective use.
CAIAs in pharmacy education are used as innovative and scalable training solutions to address complex educational and practice demands, particularly supporting communication skills, human resource management, and HIV care training.
Common characteristics include scenario-based learning, immediate real-time automated feedback, interactive learning, and multiple interaction modalities such as text, audiovisual, and voice, mostly designed for single-user formats.
Evaluated outcomes include functionality, user experience, cost-benefit, user characteristics, and educational outcomes such as confidence, knowledge, and skills development among learners.
Most CAIAs utilize text-based interaction; some include audiovisual elements, one study combines text and voice, while others rely solely on text, predominantly in single-user formats.
CAIAs are largely in early adoption stages: three studies in feasibility/usability, two in effectiveness, and one in efficacy evaluation stage.
CAIA uptake remains low, with variable and poorly described learner interaction. Additional validation of their effectiveness and expansion to other healthcare disciplines are necessary.
The WHO digital health framework informed the development of an evaluation framework capturing key characteristics and outcome measures for CAIAs, enhancing structured design and assessment.
Eleven educational features and three educational outcome categories were incorporated into the evaluation framework to guide CAIA design and evaluation in pharmacy education.
CAIAs have shown potential in increasing learner confidence, knowledge, and communication skills, despite currently low adoption rates.
Further research is needed to validate CAIA effectiveness, expand their use beyond pharmacy to other healthcare fields, and test the proposed evaluation framework more broadly.
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