Bodyswaps for Motivational Interviewing

This case study describes the design and use of a tailored AI client within BodySwaps to support postgraduate students learning Motivational Interviewing (MI) in an asynchronously taught module. The challenge was that students needed to demonstrate MI skills for assessment but had no live opportunities to rehearse complex conversations before being assessed.

The module leader, Leisa Anderton, created a bespoke AI client scenario aligned to module outcomes, professional standards and common practice challenges. Students practised with a responsive virtual client, reviewed feedback and the body-swap client perspective, retried the conversation, and then used a structured reflective journal to analyse their communication choices.

Aim of the case study

The aim was to bridge the gap between asynchronous online teaching and assessed demonstration of MI skills by providing a flexible, low-risk and repeatable rehearsal space.

The case study explores how an AI-driven immersive simulation can support students to apply MI theory in real time, demonstrate empathy and active listening, respond to ambivalence, and prepare for assessment through reflective practice.

A further aim is to position AI as a pedagogic scaffold rather than a replacement for educator judgement, debrief, professional standards or human supervision.

Benefits of the case study

Student learning benefits

This case study created a safe rehearsal space for postgraduate learners before assessed and real patient/client conversations. Because the cohort was taught asynchronously online, the AI client allowed students to practise outside synchronous teaching time and repeat interactions in a flexible way that better suited online postgraduate study.

The activity supported practice of core MI capabilities, including the PACE spirit of partnership, acceptance, compassion and evocation, and OARS micro-skills such as open questions, affirmations, reflections and summaries. It also gave learners a structured way to notice change talk, sustain talk, ambivalence and the risk of the righting reflex in their own responses, helping them connect MI theory with practical choices around tone, wording and empathy.

Teaching and assessment benefits

From a teaching and assessment perspective, the activity provided a scalable way to support skill rehearsal in an asynchronously taught module. It aligned practice activity with module outcomes, professional standards and the reflective journal assessment, while using AI feedback and the body-swap perspective to prompt metacognition and deeper reflective learning.

Approach Taken

The approach taken was to design the assessment around the learning challenge created by an asynchronously taught postgraduate module. Although the online format offered flexibility, it also reduced opportunities for students to rehearse challenging motivational interviewing conversations before assessment. To address this, the module leader, Leisa Anderton, created a custom AI client within BodySwaps that was tailored to the needs of the cohort, the module learning outcomes and the common practice challenges students were likely to encounter. Students first engaged with asynchronous preparation on motivational interviewing theory and the MI spirit before reviewing a client brief and identifying likely areas of ambivalence and behaviour change. They then entered the BodySwaps simulation and practised a responsive AI client conversation, making communication choices in real time and receiving feedback on empathy, OARS skills, change talk, sustain talk and missed opportunities. The body-swap element enabled students to review the interaction from the client perspective, encouraging reflection on wording, tone, presence and how their communication might feel to another person. Students were encouraged to identify strengths and areas for development, repeat the scenario and use the learning to prepare for the assessed component. The simulation formed the first part of a two-part assessment, followed by a structured reflective journal in which students analysed their communication strategies, linked their performance to motivational interviewing principles and professional standards, and identified actions for future development. Fictionalised scenario content was used throughout, and educator facilitation, debrief and professional judgement remained central to supporting psychological safety, ethical use of AI and transfer of learning into practice.

Outcomes and Impact

At this stage, the impact is best understood as a practice-based educational development rather than as a formally evaluated intervention. The immediate value was in making an applied MI rehearsal and assessment preparation opportunity available to postgraduate learners studying asynchronously online, where live role-play opportunities were more limited. From the module leader's perspective, the AI client helped translate Motivational Interviewing from a theoretical topic into an interactive task in which students could practise communication decision-making, empathy, active listening and responses to ambivalence within a safe, structured simulation. It also created a more flexible assessment environment, as learners could access the scenario, repeat the interaction and prepare for the reflective element outside fixed teaching times. The work demonstrates a practical way of using AI within assessment design: not as a replacement for educator judgement, but as a structured environment through which students can rehearse, evidence and reflect on practice-relevant communication skills. The student impact and learning gain will be explored more fully in a follow-up Part 2 case study, once ethics approval has been secured and a formal evaluation has been undertaken.

Future Developments

The next phase will be to apply for ethics approval so the intervention can be formally evaluated, and the work can move beyond informal feedback. Introducing the AI client earlier in the module would give students more time for repeated practice within the simulation and allow the learning activity to become more fully embedded within the teaching sequence.

Future evaluation should collect more systematic evidence, including pre-post confidence data, student reflections, perceived authenticity, assessment-related anxiety and educator observations of MI skill development. There is also scope to develop a progression of AI client scenarios so students can practice different levels of ambivalence, resistance and behaviour change readiness.

References

Cui et al. (2018) on critical thinking in nursing education; Foronda et al. (2020) and Shin et al. (2019) on virtual simulation in nursing education; Medina, Castleberry and Persky (2017) on metacognition in health professional education; Miller and Rollnick (2023) on Motivational Interviewing.