{"id":378,"date":"2026-06-02T15:11:04","date_gmt":"2026-06-02T15:11:04","guid":{"rendered":"https:\/\/facultyhub.elsevier.com\/en\/?p=378"},"modified":"2026-06-19T14:02:48","modified_gmt":"2026-06-19T14:02:48","slug":"sundeep-watkins-on-ai-in-physiotherapy-education","status":"publish","type":"post","link":"https:\/\/facultyhub.elsevier.com\/en\/sundeep-watkins-on-ai-in-physiotherapy-education","title":{"rendered":"Sundeep Watkins on AI in Physiotherapy Education"},"content":{"rendered":"\n

Elsevier Medical Education presents an interview with Sundeep Watkins, Education Adviser at The Chartered Society of Physiotherapy, Women’s Health Physiotherapy Clinician & Educator, focused on how AI can support physiotherapy education.<\/strong><\/p>\n\n\n\n

From your perspective as an Education Adviser, how can AI enhance the learning journey and clinical decision-making skills of future physiotherapists? <\/h2>\n\n\n\n

We’re just at the foothills of what AI’s capabilities are, really. In terms of enhancing the learning journey for an individual undertaking a physiotherapy program, AI presents that opportunity for people to engage with the content and materials and individualize their interaction with that content and materials. It can moderate how people interact with learning content. But we want to keep in mind that AI should be utilized in support of delivery rather than replacing teaching. In terms of learning support, a physiotherapy learner might be able to go away, explore the content, and use AI to change the modality in which they interact with it. For instance, changing content into a podcast or an infographic, or converting text into something that is much more readable. The other thing that learners can do is individualize their approach. For example, taking parts of what they have learnt or understand and inputting that into an AI tool to give them feedback on their approach, their thinking, their language. So, in that way, it\u2019s supporting development of their critique and other skills that will be needed in their professional life.<\/p>\n\n\n\n

The other way we would like to see AI implemented would be integrating it into the design of programs, in support of a learner’s journey. Learners need to understand the background behind the AI tools that they’re using, the way data informs AI responses, and be able to critique what they are getting fed back. Essentially, they need to develop their literacy in AI. They need to understand the ethics around the use of AI and the real-world applications of that AI to their clinical practice. I think this skill development needs to be incorporated into the education that learners are engaging with. In addition to that, the learner’s journey through a program includes assessment and feedback so, AI could be integrated into those aspects. I have seen a recent example where a program has specifically asked learners to utilize AI within an assessment process and then critique the AI output to develop that AI literacy and ethics capability. I think this is something else that could be explored in terms of improving a learner’s journey through a program.<\/p>\n\n\n\n

The other thing we should consider is reflective practice and using AI as a companion in a process \u2013 a critical friend maybe \u2013 for bouncing ideas off and getting feedback. Lastly, we need to talk about simulation and how simulation is integrated into programs to enable learners to enact real-world skill-building, utilizing AI tools.<\/p>\n\n\n\n

All of these aspects we have tried to include within the Chartered Society of Physiotherapy’s AI principles<\/a>, which is really aiming to help direct physiotherapists in how to integrate AI into their practice, and we have included a specific education section within that. So, we can be helping to guide people within the education sector that have been utilizing AI for teaching and learning.<\/p>\n\n\n\n

For practice educators, how can AI support consistency in teaching standards while still allowing for human-centred mentorship and professional judgement? <\/h2>\n\n\n\n

This is such an interesting question because practice educators are key in the whole process. They are so important and consistency between practice educators in feedback and assessment of learners has always presented a tricky challenge. But for practice educators, I envisage that AI should be assisting at an infrastructure level, trying to remove some of the administrative burden in order to unlock more of a cognitive level of expertise among practice educators. I can see AI supporting educators in terms of the documentation aspects of practice-based learning. For instance, walking people through the assessment criteria, standardizing feedback language, or evidence gathering to demonstrate a learner’s development. Additionally, being able to support the practice educator in their decision making but also providing conversation for them and the learners that they are supporting. The AI could support those educators by offering prompts through those processes and maybe mapping some objectives to the things they’re observing.<\/p>\n\n\n\n

So, hopefully, removing some administrative burden and creating the space for those educators to be able to interact with their learners on a deeper level. That’s how I see AI as being really supportive. But practice educators will need to develop skills to be able to do that. To harness this, they also need to up their AI literacy in this environment. They need to be aware of their own potential reliance on AI, on automation bias, for instance, which might result in trusting the AI’s conclusions instead of their own for that particular learner. It’s really essential that they do remain a key decision maker in terms of their own judgment and bringing their own expertise to that process of supporting learners through the developmental journey in practice.<\/p>\n\n\n\n

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\"When a learner is interacting with an AI tool or something that has got an AI element to it, they can get instantaneous and individualized feedback on their input. This helps a learner to identify their own specific needs in terms of gaps that they need to address or their strengths.\"<\/h2>\n<\/section>\n\n\n\n

Given your combined clinical and academic experience, how could AI tools help bridge the gap between theoretical knowledge and real-world clinical application in physiotherapy training? <\/h2>\n\n\n\n

AI is evolving so quickly. It is becoming much more realistic in terms of how you can interact with different AI tools and the data that’s being utilized to inform the AI tools is becoming more sophisticated. People are able to interact with AI in multimodal ways, which replicate clinical scenarios. So, building skills such as communication skills, clinical reasoning skills, assessment skills in terms of questioning, and that enabling treatment planning is a key, fundamental aspect that we can already replicate using AI tools to support learners in that journey from theory to practice.<\/p>\n\n\n\n

Patient simulation is, again, getting much more sophisticated and realistic. When a learner is interacting with an AI tool or something that has got an AI element to it, they can get instantaneous and individualized feedback on their input. This helps a learner to identify their own specific needs in terms of gaps that they need to address and also highlighting their strengths so, that’s another key thing that’s helpful there. The use of simulation will be key in bridging that gap between theory and clinical, and that simulation provides consistency. In terms of experience for learners, there is an issue at the moment whereby not all learners have access to all the practice experiences that may be helpful for them to have pre-registration. So, using simulation could help bridge those gaps.<\/p>\n\n\n\n

An AI-powered reflective practice is also really helpful in that space to enable learners to identify their strengths and challenges. But it can’t really replace the physical aspects of physiotherapy training. At the moment, we\u2019ve got access to simulation kit that can give feedback on things like reading heart rates and blood pressures, etc., but that doesn’t change the need to actually have hands-on practice with real people to be fully ready to take part in a clinical practice going forwards. There are potential limitations to be aware of if we’re using AI instead of experienced staff which presents a risk going forwards. There must always be experienced staff involved in the whole process of teaching, training, and learning. This is a key inclusion in the CSP Physiotherapy Education Framework<\/a> in terms of our expectations around skill building in the use of digital technologies and AI for staff as well as learners. <\/p>\n\n\n\n

There’s also equity of access to consider in terms of digital poverty, but also as the resourcing at different universities can vary. Equity and access are a key thing to consider here when we’re combining that approach to integrate theory in relation to practice.<\/p>\n\n\n\n

In the context of quality assurance and accreditation, do you see AI as an opportunity to improve how educational outcomes are measured and monitored? If so, how? <\/h2>\n\n\n\n

There is huge potential to harness AI in how we quality assure physiotherapy education. That accreditation process at the moment has been designed around a system that involves periodic review. It’s retrospective, quite often and there’s a lot of documentation in terms of reviewing documents and evidence in relation to our decision making. Then, there’s usually a panel review process that is a final layer of assessment to reach that accreditation status. We have tried to introduce some aspects that provide a more continuous quality assurance but that’s limited right now. That’s where I see AI as being able to have an impact as it could create the conditions for a different approach. For example, thinking about that documentation burden and how that’s currently analyzed and mapped to quality assurance frameworks by a person, AI could reduce administrative load with this task. Also, key themes could be picked out by AI to target our conversations with universities more specifically. AI could enable a more continuous monitoring approach \u2013 a more nuanced approach.<\/p>\n\n\n\n

Different Higher Education Institutions (HEIs) have got different contexts in terms of the numbers of learners on programs and the different approaches they’re taking to training physios. So, one university can’t necessarily be compared directly with another easily and we’re using a lot of nuance in trying to do that right now where AI could potentially support that process better. Flagging specific issues, triggering conversations, enabling us as a professional body to be more supportive and proactive rather than reactive in this space. That’s how I see AI as being supportive of quality assurance in education. But we do need to be careful about the data governance in those interactions – who is accessing whose data and for what purpose in order to enable that process. We need to be careful of the potential for algorithm biases. In developing a system, it’s really important that the system doesn’t decision-make on behalf of people with the expertise. We definitely need to integrate that human override of decisions being made so that we’re not just relying on the AI to decide things for us and also then direct conversations down a certain direction that would not actually be beneficial in terms of quality and improving or enhancing education.<\/p>\n\n\n\n

Considering your commitment to health equity, how can AI in physiotherapy education contribute to widening access to training and better preparing professionals to serve diverse and underserved populations?  <\/h2>\n\n\n\n

I think AI can really enable learners’ access to education. In terms of widening access, AI will enable a more flexible and asynchronous approach to accessing learning pathways, providing individualized feedback for those learners and interacting with them on an individualized level, even just in terms of the timing that they would like to access content they’re engaging with. AI can adapt to a student’s pace, to their learning preferences, and it could also transform how we assess those learners in the process of their development. We’re seeing that AI could potentially act almost as a personal tutor or as a critical friend in the process of a learning program. And that, we would hope, would improve access to the physiotherapy profession. But there is also a potential for AI to magnify inequity if that learner doesn\u2019t have access to those digital platforms. There is a certain assumption of AI literacy in order to engage with the tools. Those are things that we need to really build into the process to make sure that a learner will be able to access learning as needed.<\/p>\n\n\n\n

In terms of preparing professionals to serve diverse populations, the AI tools have got immense potential to identify key characteristics and needs for certain populations, and direct clinicians to interventions, thinking, principles, etc. that might be helpful in real time. But we absolutely need to build in the AI literacy for those future clinicians because the potential is for AI to magnify the inequities as it’s only as good as the data that it’s relying on. At this moment, we definitely know that the data is flawed and that there are lots of gaps that we need to address proactively. We absolutely need to build in that human override to the system and enable humans to question decisions that AI is making or suggesting. We also need to ensure those humans maintain their skills in avoiding automation bias, which is a really key issue, I think.<\/p>\n\n\n\n

So, to serve populations, we need to really think about access to that care. We need to think about what initial interventions and advice are needed for those specific populations. We need to monitor the impact of using AI in this space on those different populations to then be able to refine the models as we go on. It’s really important to have clinical professionals at the heart of research and inputting into the algorithms at this stage to inform that and ensure that equity is the priority.<\/p>\n\n\n\n

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\"We need to use simulation to build skills for our future practitioners and we don’t want simulation to fully replace practice-based learning in that context. We want it to reproduce real-world scenarios, ensure there is a space to safely practice in advance of treating real patients, and also, gain exposure to complicated scenarios to build that confidence and resilience in our learners.\"<\/h2>\n<\/section>\n\n\n\n

How do you see AI-driven simulations evolving to better capture the complexity, variability and uncertainty of real clinical environments? <\/h2>\n\n\n\n

AI is becoming so much more sophisticated, and it’s doing so at a rapid rate. The simulations are based on current data so, by using real case studies to inform the AI algorithms, we\u2019re trying to build in that complexity, variability, and the uncertainty. But, it’s the data that\u2019s being input currently which is also the key limitation of the simulation we have access to. We really need to ensure that simulation is being quality assured in terms of meeting the purpose that we would like it to address. We need to roll back that question and ask, \"What is the purpose of using simulation?\" And if it\u2019s just to use it for the sake of it, then that isn’t really good enough. We need to use simulation to build skills for our future practitioners and we don’t want simulation to fully replace practice-based learning in that context. We want it to reproduce real-world scenarios, ensure there is a space to safely practice in advance of treating real patients, and also, gain exposure to complicated scenarios to build that confidence and resilience in our learners. It’s there to prepare, enhance, and augment that practice-based learning experience. So, we absolutely want learners to feel confident, but we also need them to understand that that confidence may not fully translate into the practice setting until they\u2019ve had those real-life experiences with people.<\/p>\n\n\n\n

What makes simulation-based learning truly effective in physiotherapy education, and how can AI enhance its impact without replacing essential hands-on clinical experience? <\/h2>\n\n\n\n

Simulation really needs to be designed and executed with quality in mind, with the purpose to produce really effective learning experiences. We’ve got some criteria that we utilize in terms of quality assurance, which looks at the design element, the execution of a simulated experience, and then, the key aspect, which is the debrief and how that debrief is facilitated by experienced facilitators who have the skills to draw out deeper learning. That is what we really need to consider when we are talking about effective simulation.<\/p>\n\n\n\n

AI presents an opportunity to create safe experiences. So, learners being put into situations to develop skills that, if they were real life, would potentially be really stressful, and not very controlled. Simulation presents the opportunity to give that reproducible, safer environment to test out knowledge, skills, theory, and put it into practice. It can create really tailored and realistic interactions, which can be multi-layered in terms of the presentation – providing that intersection between the different aspects of an individual’s care needs. You could also vary the experiences in simulation to match the ability of the learner at different stages of their development, which is something that might be really beneficial. It also presents an opportunity for students to experience rarer situations that you wouldn’t often see in practice, but would be really beneficial to get that experience in training so that they could respond effectively in practice. But the expertise of the facilitators in those simulation environments is key and that debrief is essential to ensuring that learners get the best out of simulation experiences.<\/p>\n\n\n\n

We want to use those simulation experiences to enable learners to, perhaps, hit the ground running when they get into practice settings and take out some of the uncertainty they may have about those situations and build their confidence. That is also why we absolutely need clinicians with expertise informing those simulation experiences, providing feedback, and feeding into the development of those algorithms to make sure that they are as representative of real-life scenarios as possible.<\/p>\n","protected":false},"excerpt":{"rendered":"

Elsevier Medical Education presents an interview with Sundeep Watkins, Education Adviser at The Chartered Society of Physiotherapy, Women’s Health Physiotherapy Clinician & Educator, focused on how AI can support physiotherapy education via individualized learning, simulation, and assessment, while emphasizing human-led quality assurance and the important role of experienced educators. <\/p>\n","protected":false},"author":289,"featured_media":612,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"event_date":"","event_end_date":"","event_location":"","event_cta_text":"","event_form_url":"","event_bigmarker_id":"","testimonial_name":"","testimonial_role":"","testimonial_institution":"","testimonial_country":"","_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[2,3],"tags":[],"class_list":["post-378","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-medical"],"yoast_head":"\nSundeep Watkins on AI in Physiotherapy Education - 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