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SLC Joins UK Skills Partnership as Associate Member
We are delighted to announce that SLC has joined the UK Skills Partnership as an Associate Member.
July 14, 2025
Artificial Intelligence (AI) is changing everything from healthcare to classrooms; the teaching of Medical English is no exception.
From AI chatbots that role-play patients to analytics that surface common language errors, today’s tools can draft, translate, and even simulate clinical conversations. So where does the teacher fit? Front and centre.
In healthcare, language is safety-critical. Patients need clarity, empathy, and trust; teams need precision and shared understanding. AI can accelerate practice and feedback, while teachers guide judgement, empathy, ethics, and real-world appropriacy.
This guide explains how teachers can integrate AI into Medical English teaching, with practical, ready-to-use classroom examples.
Use AI to create safe, anonymised practice materials and rehearsal scenarios—then layer in the clinical nuance.
Classroom ideas
OET Speaking role-plays: Ask an AI to generate a triage scenario (presenting complaint, brief history, allergies, red flags). Students perform the role-play; you coach rapport, signposting, chunking, and clarification strategies.
SBAR handovers: Provide a short case. Students draft an SBAR with AI help, then refine for concision, tone, and prioritisation.
Referral & discharge letters: Students draft with AI, then edit for accuracy, register (doctor→doctor vs doctor→patient), and lawful, de-identified content.
Pronunciation clinics: Use speech-to-text or pronunciation tools to practise terms like ophthalmology, ischaemia, analgesia. Students compare outputs, mark stress, and rehearse.
Teacher moves: Always review AI outputs for plausibility, units, and UK/US terminology differences. Use them as drafts, not gospel.
In healthcare, the “how” matters as much as the “what”.
Focus areas
Empathy & ethics: Delivering results, exploring concerns, shared decision-making, consent.
Cultural & plain-English mediation: Translating “You have community-acquired pneumonia” → “You’ve got a lung infection you caught outside hospital.”
Non-verbal communication: Pauses, pacing, softeners (“Let’s take this step by step.”), and checking understanding.
Safeguarding & professionalism: Sensitive questioning, boundaries, and escalation language.
Activities
Compare two AI-generated scripts: one “clinical-jargon heavy,” one “patient-friendly.” Students annotate for empathy, readability, and risk.
Medical English learners must interrogate AI output.
Activities
Fact-check lab: Give an AI summary of a condition. Students verify terminology and red-flag advice against trusted guidelines (e.g., national health services or recognised clinical references you choose).
Idioms & collocations: Compare AI translations of phrases (“chest tightness,” “flare up,” “red flags”) with authentic usage; discuss nuance.
Plagiarism & disclosure: Teach when and how to acknowledge AI assistance in coursework and how to paraphrase safely.
Red flags to teach: invented guidelines, wrong units/doses, outdated terms, culturally inappropriate advice.
Use tools that highlight each learner’s patterns so you can coach what matters.
Applications
Error heat-maps: Export AI-identified issues (articles, prepositions, tense control in case notes). Turn top errors into mini-workshops.
Spaced repetition decks: Generate flashcards for high-value terms (symptoms, investigations, pharmacology) and patient-friendly paraphrases.
Speaking analytics: Learners record 60-second monologues (e.g., explaining an ECG). Use AI feedback on pace, fillers, and mispronunciations as a springboard for targeted drilling.
Let AI pre-mark for surface features; you do the human work.
Workflow
Students submit an OET-style letter/notes → AI highlights mechanics (grammar, coherence markers, format).
You add clinical-communication feedback: relevance to case notes, task fulfilment, tone, prioritisation, and concision.
Micro-targets: each learner gets 1–2 specific goals for the next task (e.g., “Reduce irrelevant detail; use problem–action–outcome sentences.”).
AI is a rapid prototyper for varied clinical contexts.
Examples
Case banks: Generate short ED, ward, GP, and community cases at CEFR B1–C1 with vitals, meds, allergy, and social history.
Documentation practice: SOAP notes, imaging summaries, ward round notes, clinic letters, consent forms (de-identified and educational).
Listening & note-taking: Convert a case into a Q&A audio (text-to-speech) for dictation; follow with a brief case discussion.
Safety note: Label everything “for language practice, not medical guidance.” Avoid drug doses and management plans unless sourced and checked; focus on language.
Role-play generator:
“Create an OET Speaking role-play at B2. Patient: 68-year-old with new shortness of breath. Include background, 5–7 patient cues, and likely concerns. Keep clinical details generic and safe for language practice.”
Plain-English converter:
“Rewrite the following for a patient at B1 level. Keep it empathetic, avoid jargon, and include one teach-back question at the end: [text].”
SBAR scaffold:
“From this note, produce an SBAR at B2 with bullet points under each heading and one clarifying question: [case summary].”
Pronunciation list with stress:
“Provide IPA and primary stress marks for these terms and a patient-friendly paraphrase: [list].”
AI won’t replace Medical English teachers—it amplifies them. Let AI draft, drill, and surface patterns; you bring safety, empathy, judgement, and authentic professional voice. Used well, AI frees time for what matters most in healthcare communication: building understanding, confidence, and trust—patient by patient, team by team.
We are delighted to announce that SLC has joined the UK Skills Partnership as an Associate Member.
We’re delighted to have won a grant from Ufi VocTech Trust to develop an AI-driven technology solution that provides cheap phone-based language and communication skills
We’re delighted to announce a partnership with leading Medical English app, Doxa.
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