Conversational artificial intelligence agents (CAIAs) are becoming more common in healthcare education. The technology is still new but may have a big impact on training programs in the United States. These AI tools use natural language processing to mimic human conversations. This lets learners practice important skills in a safe and interactive way. Most work so far focuses on pharmacy education. However, people are also curious about how CAIAs could work in other healthcare areas like nursing, medical assisting, and managing staff in medical offices.
Hospital leaders, medical practice owners, and IT managers in the U.S. healthcare system want to confirm how well these AI tools work. They also want to use the tools beyond pharmacy and make learning with AI more engaging for users.
This article talks about the current use of CAIAs in healthcare education, the problems they face, future research directions, and how AI can help with healthcare workflows.
A recent review looked at 961 studies done from 2020 to 2025. But only six studies fit the criteria for using conversational AI in pharmacy education. This small number shows the field is still new in the United States. Most research comes from English-speaking countries.
Among these six studies, three focused on helping learners improve communication skills using scenario-based learning. Two looked at human resource management, and one covered HIV care training. These CAIAs gave interactive learning experiences and often gave quick automated feedback. The goal was to help learners practice professional talks in simulated situations. This can build confidence and improve communication skills needed for patient care or team work.
Most CAIA systems used text conversations. A few included audio or voice features. These tools were mostly for individual users, which shows they are designed for personal learning experiences now.
Even though CAIAs have promise, their use in healthcare education is still low. One problem is poor records of how users interact with the systems. Many studies did not track learner engagement well. This makes it hard to measure how effective they really are or where they need improvement.
The technology is still in early use. Three studies focused on whether it is usable and easy to use. Two looked at effectiveness, and only one checked if it works well in real situations. This means that while people like early results, proof that it really works in healthcare jobs is limited.
Healthcare administrators and IT managers in the U.S. should know these limits before adding conversational AI to staff training. They should understand that benefits like better communication skills and learner confidence are showing up but not fully proven yet.
Right now, most research focuses on pharmacy education. But conversational AI could help other healthcare fields too. Medical offices in the United States need ongoing training on many topics like patient communication, following healthcare rules, cultural awareness, and managing staff.
For example, human resources departments could use CAIAs to practice hard talks, like handling staff conflicts or performance reviews. Nursing education might use CAIAs for scenarios about bedside communication, patient teaching, and care coordination.
In HIV care, one study showed how conversational AI helped learners practice sensitive talks about patient privacy and taking medicine correctly. Expanding these features can help healthcare workers get ready for many real-life situations.
Medical practice owners and hospital leaders should think about using conversational AI to support in-person training. This is especially useful when remote or flexible learning is needed, as seen during the COVID-19 pandemic.
Although studies show learners feeling more confident and gaining skills, more data from actual workplaces is needed to confirm these findings.
Healthcare managers and IT leaders who want to use CAIAs should focus on making them easy to use and engaging for learners. Current tools mostly allow one user at a time, which helps with focus. But adding group or peer learning could better match real healthcare teams.
The World Health Organization’s digital health framework inspired a model to evaluate CAIAs. This model adds educational features and categories to help design and measure these systems. It helps healthcare organizations pick tools that fit their training needs.
Also, to use CAIAs beyond pharmacy, scenarios and feedback should change to fit nursing, allied health, and administrative roles.
AI is also helpful for automating work in healthcare offices. Automating front-office phone calls is important for medical offices in the U.S. that want to improve patient access and staff productivity.
One company, Simbo AI, offers AI-based phone answering and automation. Their systems handle routine calls, schedule appointments, refill prescriptions, and answer basic questions. These AI systems can answer many calls at once, cut down waiting times, and free staff to do more complex tasks.
Healthcare leaders could combine CAIAs for training with these AI tools for phone help. For example, simulation tools could train front-office staff on how to talk with patients, while AI handles routine phone tasks.
This use of AI can make communication more consistent, reduce mistakes, and improve patient satisfaction.
To make the most of conversational AI in U.S. healthcare education, researchers and health workers should focus on:
Healthcare leaders should judge CAIA tools not just by technical features but also by how well they work for learning and fit into daily work. Though proof is still small, evidence suggests conversational AI can improve learning and make training more interactive and easier to access.
When adding AI training tools or automation like Simbo AI’s phone system, administrators should:
The future of conversational AI in healthcare education and workflow automation in the United States is still developing. Still, research shows early gains in learner confidence and communication skills in pharmacy education, which may guide further growth. With careful testing and improvement, medical offices can prepare their workers better and make daily operations smoother, helping the overall quality of care and service.
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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