AI can help tailor training programmes for the individual to maximise efficiency, says Nick Dobrzelecki, co-founder of The Learnery
I remember the first time I heard someone mention AI in healthcare education. It was a couple of years ago, and at the time, the focus was on AI’s potential to create content. But for me, the conversation that was missing—the one I felt we needed to have—was about the ethical and moral implications of AI in our field. We have a responsibility to ensure that the databases we use are accurate and that the data we rely on is protected. This is not just a technological concern; it’s a fundamental issue of trust and responsibility.
One of my biggest concerns with AI in education is the reliance on the internet as a source of truth. Many companies are using AI to generate content, pulling information from the web without rigorous validation. That approach is not just flawed; it’s dangerous. In our work, we have made a deliberate decision not to use AI for content creation until we have a structured database of verified educational material. For us, that source of truth must be something as reliable as NHS content. Without a vetted, standardised foundation, AI-generated content risks being riddled with inaccuracies, ultimately harming the very people it aims to educate.
While AI-driven content creation is often touted as the future, I believe that’s the easy part. Developing algorithms to generate content is relatively straightforward. What is far more complex—and far more valuable—is using AI to enhance productivity in ways that actually support learners. This is where AI’s potential excites me: not in writing textbooks, but in tailoring education to individuals in a way that maximises efficiency and effectiveness.
Creating individualised learning paths
Right now, we train all learners the same way. Regardless of prior experience or education level, everyone starts at the same baseline. That’s an outdated and inefficient model. AI gives us the opportunity to change that. Imagine a system that assesses a learner’s background, education, and experience before they even begin training. Instead of a one-size-fits-all approach, we could create individualised learning paths—shorter, more focused orientations for experienced professionals, and longer, more supportive training for those just starting out.
This shift would have significant benefits, not just for learners but for healthcare organizations as well. If we can fasttrack experienced professionals through their training while providing additional support to those who need it, we reduce frustration, increase engagement, and, most importantly, get staff into patient care roles more quickly and effectively.
Take nursing, for example. We know that burnout is a major issue, particularly in the first three to four years of a nurse’s career. A significant part of that burnout comes from feeling overwhelmed and underprepared. If AI could help tailor training to individual needs—offering more guidance where necessary and removing unnecessary bureaucracy for experienced nurses—we could ease that transition and improve retention rates.
Likewise, senior staff often feel frustrated by redundant training. They are required to check off the same boxes year after year, even when they’ve mastered the material. If AI could assess their competencies and verify their knowledge in real time, we could eliminate unnecessary steps and allow them to focus on what truly matters: providing excellent patient care.
Integrating healthcare systems to support clinicians
But AI’s potential in healthcare education goes beyond training. Integration is the next frontier. Right now, our systems— electronic medical records (EMRs), learning platforms, diagnostic tools—exist in silos. This lack of integration creates inefficiencies and gaps in education and patient care. At an upcoming conference in Abu Dhabi, I’ll be speaking about how we can bridge these gaps. How do we integrate EMRs with learning management systems? How do we connect radiology and diagnostic data with training modules? The ultimate goal is to create a seamless, intelligent system that not only educates but also supports clinicians in real time.
Imagine an ecosystem where AI continuously learns from a clinician’s experiences, updating training programmes dynamically based on real-world performance. Instead of rigid, scheduled training modules, education would become fluid and adaptive. AI could analyse data to determine which competencies need reinforcement, tailoring training to individual and organisational needs. This kind of personalised, just-in-time education would revolutionise how we think about professional development in healthcare.
This level of sophistication requires us to build ethical AI models from the ground up. Instead of retrofitting AI onto existing data, we are taking a structured approach—creating standardised databases first and then layering AI on top. By learning from past mistakes, we can build a system that is not only effective but also ethical and transparent.
For clinicians at the top of their field-surgeons, cardiologists, specialists—AI could play an even more nuanced role. These professionals already engage in continuous learning through research, teaching, and professional development. But AI could help ensure that their knowledge remains cuttingedge. Instead of testing them on the basics, AI could assess their familiarity with the latest guidelines and best practices, ensuring they are always operating with the most current knowledge.
Take the example of an ICU nurse. A new graduate may have learned the latest evidence-based practices in school but lacks hands-on experience. Their training should focus on reinforcing knowledge and building confidence. In contrast, a seasoned nurse may have decades of experience but could benefit from refreshers on high-risk, low-utilisation scenarios—those rare but critical situations where up-todate knowledge can mean the difference between life and death. AI can help deliver targeted education, ensuring that both new and experienced clinicians receive the training they need without unnecessary redundancy.
Globalising healthcare education
This approach also has global implications. Healthcare is increasingly international, with clinicians moving across borders to fill workforce gaps. Yet, standards of care vary from country to country. AI-driven education could help standardise training worldwide, ensuring that a doctor trained in the UK could seamlessly transition to a role in Riyadh or New York. By integrating AI into healthcare education on a global scale, we could improve consistency and quality of care across borders.
Of course, change is never easy. One of the biggest challenges we will face is shifting mindsets. Healthcare professionals are accustomed to traditional training models and adapting to an AI-driven system will require a cultural shift. There will be questions: Why is my training different from my colleague’s? Why do I have to complete six weeks of training while someone else only does two? We will need to educate not just our learners but also our organisations, demonstrating that this approach is about fairness, efficiency, and, ultimately, better patient care.
We are at the doorstep of the future. The technology is here. The data is being structured. The opportunities are endless. Now, the challenge is not just implementing AI, but rethinking how we approach education, training, and professional development in healthcare. The question is not if AI will transform healthcare education—it’s when, and more importantly, how we ensure it is done ethically, effectively, and equitably.
This is not a distant vision for 2050. This is happening now. In the next few years, we will see AI-driven education platforms revolutionise how we train healthcare professionals, making learning more personalised, efficient, and impactful. We are not just preparing for the future we are building it
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