LEARNING OUTCOMES
After completing this module, the paramedic will be able to:
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Reflection has been a fundamental pillar within paramedic practice, embedded in clinical education and professional development.
During education, paramedics engage in structured reflection through written academic assessments and placement documentation, reinforcing critical thinking and self-awareness. Following registration, the Health and Care Professions Council (HCPC) requires practitioners to maintain reflective practice through continuous professional development (CPD), while informal reflection continues through peer discussions, journalling and personal contemplation.
While traditional reflective models – such as Gibbs’ Reflective Cycle – have stood the test of time, the emergence of generative artificial intelligence (Gen AI) introduces new opportunities and challenges for reflection in paramedicine. This article explores the intersection of Gen AI and reflective practice, considering its theoretical foundations, practical applications and potential implications for education and professional development.
This article is the fifth in a six-part series exploring the role of Gen AI in paramedic practice. Articles have covered ethical considerations, goal-setting, live language translation, and training companion.
Theoretical foundations
Reflection in paramedic education
Reflection is a critical process in professional practice that involves the deliberate review of experiences, with the goal of learning from those experiences and improving future actions (Préfontaine et al, 2022). It encourages individuals to critically examine their decisions, actions, and the outcomes of their practice, leading to enhanced self-awareness.
Reflection is a cornerstone of paramedic education, designed to develop critical thinking, self-awareness and clinical reasoning. Both formal and informal reflection play crucial roles in shaping clinical competence and professional identity. The term ‘reflective practice’ was considered a relatively new phrase to the paramedic profession in 2008, emerging from the academic education the profession was aligning with (Sibson, 2008).
Formal reflection
Formal reflection is introduced early in the paramedic educational journey, typically structured around established frameworks focusing on describing, evaluating, analysing and critiquing experiences. Among the most widely used models is Gibbs’ Reflective Cycle (Gibbs, 1988), valued for its straightforward, step-by-step structure, which makes it particularly beneficial for novice practitioners. Another commonly used model is Rolfe et al's (2001) reflective cycle, originally developed for nursing but widely applicable to paramedicine. This model follows three simple reflective prompts: ‘What? So what? Now what?’, encouraging practitioners to analyse their experiences and consider future improvements.
While these general models remain dominant, paramedic-specific reflective frameworks have also been proposed. One such model is CRASHED, featured on the CPD platform CPDme (2023). This acronym-based structure prompts practitioners to reflect on key aspects of a prehospital incident: communication, response, actions, subsequent actions, hospital, evaluation and ethics, and discussion.
Unlike cyclical models such as Gibbs’, CRASHED follows a more linear approach, focusing on the structured breakdown of an incident rather than iterative reflection. Although Hilliard et al (2017) acknowledge its existence, Gibbs’ model is more commonly used.
The I.F.E.A.R model was developed from the Gibbs’ model, transforming it into one that is specific to attending an incident (Smart, 2011). The acronym stands for: Incident, Feelings, Evaluation, Analysis and Reaction/Response. However, similarly to CRASHED, the literature for this model is scarce. Therefore, academic institutions will refer back to models such as Gibbs’ with its excess of research or alternatively popular models like the Johns (2009) model of structured reflection.
Informal reflection
While formative reflective models like those mentioned in the previous section provide structured approaches, reflection in paramedic practice often extends beyond these frameworks. Not all reflective practice takes place in written form or through structured CPD activities. It may take the form of peer-to-peer ‘crew room’ discussions, journalling and personal notes, and replaying events in one's mind to consider alternative approaches. Group reflection is often encouraged where team working has occurred, although it is recognised that this approach sometimes lacks engagement with empirical evidence (Howlett, 2019).
Howlett (2019) identified that reflective practice was commonly instigated in response to a negative trigger or experience, not limited to only the experiences of emotional incidents, but also the internal feelings towards one's performances, or following an incident requiring safety reporting. Incident reporting is well established within the National Health Service (NHS), with an emphasis on recognising and mitigating safety failures rather than celebrating positives, and therefore a bias towards reflecting upon negativity exists institutionally (Kelly et al, 2016).
While it is imperative that negative triggers are reflected upon and learning occurs, positive triggers also must receive the same attention within reflective practice. A study on teachers’ reflections of positive versus negative experiences found that positive reflections led to an increase in innovative solutions, higher motivation and the experience of additional positive emotions (Janssen et al, 2008), though it must be recognised that the triggers for reflections within teaching are likely to be less acute in nature than those experienced by paramedics. Jones et al (2020) supported these findings, noting that positive feedback for nurses resulted in reflections, which led to the maintenance of good practices.
Reflection 1
How do you currently engage in reflective practice? Reflect on your preferred method of reflection, why you choose this approach, and how effective it is at improving your practice.
Generative AI in professional development
Gen AI refers to a category of machine learning models capable of producing original content, such as text, images, or code, in response to user prompts. These models are a type of artificial intelligence (AI) that has been publicly available since November 2022 with the launch of OpenAI's ChatGPT. Since then, a plethora of public models have become available for internet users to leverage for personal and professional needs. Notable examples include Google Gemini, Claude by Anthropic and Co-Pilot by Microsoft.
These models offer possibilities for enhancing productivity and idea generation, allowing for professional development in areas which may have previously required additional training, costs and extensive time to gain. Much of the current literature focuses on the possibilities for educators to harness Gen AI for professional development, aligning with education being one of the largest sectors adopting this technology. In contrast, there is comparatively less research on how healthcare professionals themselves are using Gen AI for self-directed learning and reflection, with existing studies primarily situated within the context of healthcare education (Almansour and Alfhaid, 2024). This difference is important, as much of the literature emphasises the educator's role in using Gen AI as a teaching tool, rather than using the tool independently for professional growth. Despite the limited literature, Gen AI has been identified as creating a new era within healthcare CPD, with suggestions of developing presentations, summarising literature, and creating simulation scenarios, among others (Ensign et al, 2024).
This growing interest in Gen AI as an educational tool invites further exploration of how it may support reflective practice and self-guided professional development within frontline healthcare. For paramedics, in particular, the flexibility and responsiveness of Gen AI tools may offer structured support for both formal and informal reflection – especially in environments where time, supervision or emotional bandwidth are limited. This article recommends practical applications of Gen AI in the reflective process, of relevance to prehospital care.
The term ‘prompt’ refers to the text input from a user provided to a Gen AI model, which it subsequently interprets in order to generate a response. While these models are increasingly adept at inferring intent and meaning, the quality and relevance of the output still rely heavily on the clarity and specificity of the user's prompt. A user should ensure that the prompt gives direction, defines rules, provides examples and has taken into account a feedback loop and whether complex tasks need chunking into multiple prompts (Phoenix and Taylor, 2024). It is also important to note that not all Gen AI models interpret prompts equally, and the degree of nuance of contextual understanding may vary significantly between platforms (Aydin et al, 2025).
Practical applications of Gen AI in reflection
AI-generated reflection prompts
Fleck and Fitzpatrick (2010) state that there are five levels of reflection, listed from the lowest to highest forms:
The levels of reflection may align with where a healthcare professional is in their professional development. A student paramedic In their primary years of their degree may align with the lower levels of reflection owing to their limited experience and knowledge of reflection. As one gains expertise, and also increased responsibility for their actions, the reflection is likely to progress to the higher forms. Understanding these levels of reflection provides a useful framework for tailoring reflective practice to the development stage of the paramedic. A student paramedic may benefit from prompts that encourage structured description and basic analysis, while a more experienced clinician may require deeper questions that challenge underlying assumptions. Gen AI offers a unique opportunity to adapt reflective prompts to the user's level of experience and the nature of the incident. By responding to the user input, AI tools can scaffold reflection – guiding novices through structured cycles as previously mentioned, while also encouraging complex thinking for those engaging in dialogic or transformative reflection.
Reflection 2
Consider both formal and informal reflection. What benefits have you experienced from using structured models and how do these compare with the benefits of informal reflection?
Reflection 3
Can you identify a recent clinical event where Gen AI could have enhanced your reflection or learning? How do you think this would have helped?
Gen AI response examples
To demonstrate how Gen AI can support reflective practice in paramedicine, two example responses are presented below. These illustrate how AI tools can tailor reflection based on the clinician's level of experience and the depth of reflection required. Sample prompts generated for different levels generated with ChatGPT-4o mini.
Example 1: year 2 student paramedic Gen AI responseE
‘Think about a recent patient encounter during placement.
This response from Gen AI mirrors the structure of Gibbs’ Reflective Cycle (Gibbs, 1988), guiding the student through stages of description, feeling, evaluation, analysis, conclusion and action planning. For novice reflectors, as is the preregistration student paramedic, scaffolding helps to formalise early reflective habits, ensuring emotional responses are acknowledged, while encouraging analytical thinking (Sibson, 2008; Bowman and Addyman, 2014). This particular model of reflection is simple and used within early paramedic education. It has been found to improve empathy and communication skills, leading to better patient care (Ahmadpour et al, 2025). Gen AI tools can generate such responses to simple or minimal inputs such as ‘help me reflect on a trauma call’, allowing accessibility and timeefficient reflection. The more comprehensive the user's prompt, the more in-depth and accurate the Gen AI response.
Experienced paramedic (8 years qualified) Gen AI response
‘Reflect on a situation where you felt that your usual clinical approach or belief was challenged. l What assumptions did you hold at the time, and how did they influence your decisions?
This response from Gen AI targets transformative reflection, encouraging the clinician to interrogate their assumptions, integrate broader context, and link reflection to leadership or future behaviour. Gen AI tools may generate outputs aligned with emotional cues present in the prompt and that reflect the complexity inferred in the user's prompt. This allows for tailored support that is sensitive to uncertainty or responsibility commitments required by the user's role.
Peer-to-peer reflection and CPD
While individual reflection is a well established component of paramedic development, peer-to-peer reflection and collaborative learning are increasingly recognised as vital to maintaining clinical competence and psychological safety, credited for creating high-performance teams (Newman et al, 2017). Gen AI can enhance this process by facilitating structured, accessible tools that support group-based learning, post-incident discussion and formalised CPD recording.
Peer-to-peer assessment is a fairly well documented source for clinicians to gain positive interprofessional feedback (van Schaik et al, 2016). Markowski et al (2021) identify that there is a sizable amount of research regarding peer learning, although with minimal mention of how reflective practices can align within this area of education. There are small areas of research introducing peer reflection into education programmes with perceived success. An example within physical therapy is a small trial resulting in students wishing for peer reflection to become embedded in the programme (Thompson, 2022). When considering group reflections, there should be an emphasis on targeted discussion with guidelines, and purposeful consideration of the perspectives involved to ensure a well rounded discussion (Phua et al, 2024).
One potential application is the use of Gen AI-generated discussion prompts to guide reflective dialogue between colleagues. These could be used informally after a shift, or more formally in group CPD sessions with a facilitator, potentially reducing cognitive load. For example, following a complex case, a Gen AI tool could generate open-ended prompts such as: ‘How might this scenario be approached differently by clinicians with varying levels of experience?’ Such questions encourage deeper dialogue that goes beyond recounting events, prompting clinicians to explore clinical reasoning, teamwork dynamics and variations in practice.
Another emerging use of Gen AI in peer-based learning is the generation of simulated patient cases to facilitate structured group discussions. These AI-generated scenarios can replicate a wide range of clinical presentations, ethical dilemmas, or highpressure decision-making environments. This offers an alternative to in-person simulation, which is time- and cost-high; it provides an on-demand environment to practise clinical reasoning and develop confidence, while testing knowledge.
Finally, Gen AI may also be used to summarise group discussions into formalisable CPD entries aligning with the HCPC's expectations. A user may prompt the Gen AI with their personal reflection and ask it to align with an identified model. Crucially, this should not be used to generate the content included in the reflection, but instead as a formatting tool for clarity, streamlining documentation, making reflective practice more manageable. Gen AI is a tool to support reflection, and not replace the interpersonal values of undertaking reflection. The HCPC has not published guidance on using Gen AI in CPD at this time. Registrants must ensure that any submitted CPD aligns with the Standards expected. Regulatory bodies internationally are starting to explore AI and the impact it will have on professions, and therefore publications may be available soon.
Limitations
While Gen AI offers considerable promise in supporting reflection and professional development, several limitations must be acknowledged to ensure its responsible and effective use.
Inability to replicate empathy/emotional intelligence
Gen AI lacks the lived experience, emotional nuance and empathic understanding that underpin reflection – particularly in the emotionally charged environment of paramedic practice. While it can generate relevant prompts or simulate dialogue, it cannot replace the value of human connection, support, or shared understanding that often accompanies reflective conversations. Rather than creating reflections with Gen AI, it is recommended that it be used as a tutor to enhance learning and formative feedback (Combrinck and Loubser, 2025).
Reflection 4
How might Gen AI support your development as a practice educator or supporting junior staff through reflective practices?
Reflection 5
In what ways can you ensure that the use of AI supports, rather than replaces, your own critical thinking and emotional insight in reflection?
Risk of over-reliance on AI-generated insights
There is a risk that users may come to overly depend on Gen AI content, risking treating it as a definitive answer, rather than a recommendation for further exploration. This can lead to incorrect conclusions being made from AI-generated content and a reduction in critical thinking, deterring users from engaging with research and developing their own opinions (Zhai et al, 2024). Gen AI tools can generate false information in manners which appear correct, and therefore content should be cross-referenced with available literature (Abd-Alrazaq et al, 2023).
For example, a Google search for ‘CRASHED model reflection’ generates an incorrect acronym with Google's Gen AI model Gemini, highlighting the risks of relying on AI-generated content for professional learning. This inaccuracy likely stems from the limited availability of peer-reviewed literature on the model and restricted access of CPDme's reflection templates. The limitations of AI-generated knowledge in niche professional domains raise concerns about misinformation.
Hallucination is the current terminology for when a Gen AI confidently delivers incorrect information (Maleki et al, 2024), with risks of widening disparities and spreading misinformation. It is recommended that using trusted, closed-loop AI systems could be a solution, with plans from the government to implement systems across the NHS (Department for Science, Innovation and Technology et al, 2025).
Balancing AI assistance with critical thinking and personal reflection
Reflection requires more than structured questioning – it involves critical thinking, moral reasoning and a willingness to challenge one's own actions and beliefs. In self-directed learning contexts, such as reflective practice, paramedics benefit from an inquiry-based approach where deeper questioning creates insight and growth (Ali et al, 2023). Gen AI can support this process as a facilitator, offering structure and stimulus to guide reflection. However, there is a risk that clinicians may come to view AI-generated outputs as complete, rather than as starting points for critical self-exploration. Conscious effort should be applied to balance the assistance provided by Gen AI and the personal judgment and lived experience of the person undertaking reflection.
Ethical and professional considerations
As previously explored within this CPD series, ethical considerations (Furey, 2025) remain at the forefront of the discussion when Gen AI use cases are proposed or recommended. There remains a concern around bias, data security and professional accountability. Gen AI models may reflect biased trained data or generate inaccurate outputs, and users must critically evaluate the content produced. Crucially, when using Gen AI for reflection, confidentiality and data protection must be ensured (HCPC, 2023). Therefore, patient details must never be entered into public Gen AI platforms. It is essential that clinicians apply professional judgement and ensure that human oversight remains the focus of any reflective process.
Future directions
As Gen AI continues to evolve, its role in supporting reflective practice for paramedics remains an emerging field. Future work could focus on developing profession-specific tools that incorporate clinical language, ethical nuance and regulatory expectations such as the HCPC (2017) CPD Standards. Currently, literature regarding reflection correlates with intentions to improve academic integrity since the advent of Gen AI. Therefore, a gap exists for uses outside of preregistration courses. With the HCPC (2024) review of the standards of education and training including AI, there may be changes afoot across the professions.
Future research should consider the quality of Gen AI enhanced reflection, exploring the impact on clinicians and patients when compared with traditional reflective methods. Exploring how Gen AI can support team-based reflection, debriefing, and simulation may further enhance peer learning and psychological safety. Collaboration should occur between educators, clinicians and those with expertise in AI to ensure that Gen AI tools are designed or recommended ethically, securely and with practical usability in mind.
Conclusion
Reflective practice remains fundamental to the development and ongoing competence of paramedics. As Gen AI becomes increasingly embedded within healthcare, it presents both opportunities and challenges for enhancing reflection. When used thoughtfully, Gen AI can support clinicians by scaffolding reflective processes, and streamlining self-directed learning and CPD documentation.
It is essential that these tools complement – rather than replace – critical thinking, emotional intelligence and professional judgement, all of which underpin meaningful reflection. A balance is required between using technology to assist reflection and relying on human insight. The ethical considerations remain paramount when using Gen AI in this manner, and users need to evolve with the guidance and requirements of professional bodies.