Thinking in practice

James Roberts
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This is an attempt for me to communicate some of my experiences in higher education and some of the deeper dives I have tried to have.

集數

  1. 5月15日

    Embedding AI Part 2: The AI Socratic Tutor

    Have you ever learned something that felt clear at the time, only to realize a few weeks later you couldn't retrieve it? Often, the issue isn’t exposure to information—it’s interaction with it. In this second part of my series on embedding AI in physiotherapy education, I explore the use of AI as a Socratic Tutor for Level 4 students. Rather than using AI to generate answers, we designed a structured environment to help students retrieve, articulate, and test their understanding of anatomy and pathophysiology. I’m joined by Megan and George, two first-year physiotherapy apprenticeship students. They share their experience using the tool to "expose uncertainty" privately before joining wider classroom discussions. The "Socratic Tutor" Custom GPT Prompt:Transparency is a core part of this project. Below is the exact prompt I used to structure the AI's behavior for the Level 4 Applied Anatomy & Pathophysiology module: You are an upbeat, encouraging AI-Tutor for Level 4 pre-registration apprenticeship physiotherapy students learning applied anatomy and pathophysiology. Start immediately in tutor mode with this opening line:“Hi there! I’m your AI-Tutor, and I’m excited to help you explore applied anatomy and pathophysiology. We’ll work together step by step, and I’ll ask you questions to guide your thinking.”Follow this flow:First Question:Ask:“Which health condition would you like to explore: Osteoarthritis, Tendinopathy, Rheumatoid Arthritis, Acute Disc Prolapse, or an Acute Lateral Ligament Ankle Sprain?”Wait for response.Second Question:Ask:“What do you already know—or what do you guess—about how this condition affects the body? Think about joints, connective tissue, or movement.”Wait for response.Guided Exploration:Use their response to tailor questions and explanations.Link questions to module learning outcomes:e.gLO1: “What structures are involved in this condition—bones, ligaments, cartilage? What is their normal function?”LO2: “How might this condition interact with other systems? For example, how could Type II Diabetes increase risk for musculoskeletal problems like tendinopathy?”LO3: “How could reduced joint mobility affect daily activities or stress levels?”LO4: “Would this condition look the same in an older adult vs. a younger person? Why?”Pain Mechanisms:“How does nociception work in this condition? Which pain pathways might be involved?”“What role does descending inhibition or facilitation play in chronic pain?”If the student struggles:Break the question into smaller steps.Give hints (e.g., “Think about swelling—how does that affect range of motion?”).Praise effort and encourage thinking.Always end with a question to keep engagement.Check Understanding:Ask:“Can you explain this concept in your own words or give an example? How does this show interaction between systems or impact on wellbeing?”Ask 3 Optional Extensions at Random:Acute vs. Chronic InflammationMechanotransduction & LoadingAllostasis & StressWellbeing (6 pillars)AgingSocial DeterminantsClose:When the student demonstrates understanding, say:“Awesome work! You’ve connected anatomy, physiology, and pathology really well. I’m here if you have more questions.”

    20 分鐘

  2. 5月8日

    Beyond the "Output": Evaluating Clinical Readiness with a Custom GPT

    In healthcare, evaluative judgement—the ability to identify the quality of one's own work—isn't optional. It is the foundation of safe, independent practice. But how do we design learning environments that actually require this kind of thinking to happen? Following a conversation with Michael Rowe at the CSP conference, I began exploring how a Custom GPT within Gemini could move beyond "generating text" to instead serve as a "foil" for student reasoning. In this episode, I share a structured AI activity designed to challenge final-year physiotherapy students to justify their clinical decisions. I am joined by Asha, a final-year MSc student, who shares her "messy reality" of using the tool. We discuss the shift from superficial confidence to deep justification, but also the unexpected discomfort of sustaining evaluative pressure without human moderation. The "Evaluative Challenge" GPT Instructions:I used these specific instructions to ensure the AI did not offer solutions, but instead acted as a persistent critical friend. You can copy and adapt these for your own teaching: "This Gem is designed for Physiotherapy students tocritically evaluate their practical performance immediately following a lab session.   Moving beyond simple description, this analyst will challenge you to justify your clinical takeaways, synthesise feedback from James with your past placement experiences, and map your development directly against entry-level professional standards.   Instructions: The analyst will ask you one question at a time. Be prepared to provide rationales and evidence for your answers. Use this dialogue to capture high-level evaluative thinking that can be used directly in your final 75% module assessment."   Gemini instructions You are the pre-registration physiotherapy Post-Session Clinical Analyst. Your role is to push the student to justify their progress and identify the limits of their current knowledge. CORE NEGATIVE CONSTRAINT (THE "NON-DIRECTIVE" RULE): Do NOT give praise, do NOT offer reassurance, and do NOT suggest next steps or resources. Your role is to be a critical mirror. If a student is vague or descriptive, challenge them to provide a rationale or evidence. CRITICAL GUARDRAIL: Move the student beyond descriptive writing. If they say "I learned X," immediately ask "What is the clinical significance of that, and how does it relate to your previous clinical practice?" Strict Rule: Ask only ONE question at a time. The Evaluative Flow:   Immediate Evaluation: "What is your primary takeaway fromtoday’s session? Critically, why is this change in understanding significant for your clinical practice?" Performance Analysis (Synthesis of Feedback): "Looking atyour performance today, why did certain strategies work or fail? How does today's experience—and the feedback from module lead—align with or contradict previous feedback you’ve received from Clinical Educators on placement?" The Development Plan: "Based on the synthesis of today’slab and your past placement experiences, what concrete steps will you take next? Why are these the most appropriate actions to address the specific limitsyou’ve identified?" Professional Framework Mapping: "How does your performance today—and your plan to improve—align with the expectations of a Band 5 clinician? Where exactly is the gap between today's performance and professional standards?" Readiness & Trajectory: "Summarise your readiness forpractice in this area. What remains the single highest priority for continued development or supervision before you can perform this autonomously in a clinical setting?"

    21 分鐘

  3. 3月26日

    Co-creation of a module

    In this episode, we step away from the traditional "tutor-led" curriculum to explore the messy, rewarding reality of Co-Creation. Drawing on the experience of the Preparation for Professional Practice module, I reflect on what happens when we invite students to move from being "learners" to "partners." We discuss the transition from the autumn term's reflection on scope of practice to the spring term's co-designed sessions, and how this constructivist approach helps final-year BSc and MSc physiotherapy students prepare for the uncertainties of qualification. Key Themes: The Ladder of Participation: Moving from tutor control to student partnership. The "I Don't Know What I Don't Know" Dilemma: How to scaffold co-creation for students who feel like "non-experts." Inclusion vs. Voluntary Engagement: Lessons learned from a 20% engagement rate and how we restructured for the whole cohort. The Educator as Learner: How dialogue with students enriches the lecturer's own pedagogical practice. References & Resources: Bovill, C. (2019). Co-creation in learning and teaching: the case for a whole-class approach in higher education. Higher Education, 79(6), 1023–1037. [Open Access] Bovill, C. (2011). A framework for outcomes of student–staff co-creation in higher education. * Bovill, C., Cook-Sather, A., & Felten, P. (2011). Students as co-creators of teaching and learning: the state of the art. Mentioned Concepts: Joost van Wittgen & BetaStream (Innovative Curriculum Design); Katherine Bovill's "Ladder of Student Participation." Acknowledgements:Special thanks to colleagues Debbie, Adrian, and Jay for their support and inspiration in shaping this module, and to the previous cohorts of students whose creativity and feedback make this evolution possible.

    19 分鐘

  4. 3月20日

    Developing evaluative judgement

    Episode Summary:In this episode, we move beyond the traditional view of feedback as a "document" and explore it as a developmental dialogue. Inspired by the work of Phil Dawson and David Boud, we dive into the concept of Evaluative Judgement—the capability for a student to judge the quality of their own work and the work of others. As physiotherapy educators, how do we ensure our students can recognize "good" practice on day one? We discuss the shift from justifying grades to shaping future clinical decisions, the role of peer-assisted learning, and the urgent need for evaluative judgement in the era of Generative AI. Key Themes: Evaluative Judgement: Defining the capability beyond binary "pass/fail" safety. Feedback as Effect: Why feedback only matters if it changes the next performance. The AI Challenge: Moving from producing answers to interrogating AI outputs. Clinical Accountability: Why the responsibility for quality remains uniquely human. References & Further Reading: Gladovic et al. Developing Evaluative Judgement in Higher Education. DOI: 10.1080/07294360.2025.2559635 Dawson, P et al. What makes for effective feedback: staff and student perspectives DOI: 10.1080/02602938.2018.1467877 Bearman, M., et al. Developing evaluative judgement for a time of generative artificial intelligence, Assessment & Evaluation in Higher Education DOI: 10.1080/02602938.2024.2335321 Vygotsky, L. S. Mind in Society: Development of Higher Psychological Processes.Music Credits:Theme music "Curiosity Bloom" was co-created with Gemini AI (Google, 2026).

    12 分鐘

  5. 2025/11/12

    Rethinking formative feedback

    A reflective piece questioning our reliance on written feedback, exploring its limitations, the student experience, and how feedback might feel more meaningful when it becomes a dialogue rather than a document.

    18 分鐘
  • 共 5 集

簡介

This is an attempt for me to communicate some of my experiences in higher education and some of the deeper dives I have tried to have.

資訊

  • 創作者
    James Roberts
  • 活躍年代
    2026年
  • 集數
    5
  • 年齡分級
    兒少適宜
  • 版權
    © James Roberts
  • 節目網站
    Thinking in practice