This Week in MedEd

16 open funding calls, 57 upcoming events, 60 live jobs (21 new, 11 closing this week), including:

• 💰 Digital Skills and Jobs Platform: National Coalitions for Digital Skills and Jobs (up to €2M from the European Commission’s Digital Europe Programme, deadline 1 Oct 2026)

• 🎓 2027 AAMC Medical Education Innovation Conference (Indianapolis, IN, 17-20 Apr 2027)

• 💼 Assistant/Associate Professor in AI for Medical Education (Duke-NUS Medical School, Singapore)

The full list, with deadlines, links and eligibility, is one tap away. Try everything free for 30 days.

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Cai et al. undertook a systematic review and meta-analysis of studies that looked at AI-assisted training for operative technical skills1. The review surfaced nine studies comprising 588 participants. The headline pooled effect was not significant and wide, but the subgroup analysis is very interest interesting:

• Against active human instruction the pooled effect was 0.13 (95% CI −0.24 to 0.51, P = 0.48).

• Against passive conditions or training without AI it was 0.94 (95% CI 0.30 to 1.59, P = 0.004).

So it seems that the apparent advantage of AI-assisted training is greatest when the alternative is not being taught very much. Set against a competent human teacher, it disappears.

There is a caveat, though. The certainty of evidence was rated low under GRADE, downgraded for risk of bias and imprecision, and to their credit the authors, they call the passive-comparator result "exploratory" rather than presenting it as a settled finding.

Effiong and Effiong surveyed 128 medical and health science students at the University of Birmingham2. They found that 96.3% of students were using genAI, but only 40.0% reported receiving any formal training. This finding is not particularly illuminating, and confirms what we already know from much larger national survey, such as the HEPI Student Generative Artificial Intelligence Survey.

There was another finding, which has also been reported previously, that caught my eye: students who had received training reported significantly lower trust in AI outputs (Mann-Whitney U = 321.50, p = .004, surviving Bonferroni correction).

This is super interesting to me. It suggests that training does not produce enthusiasts, it produces sceptics. I think we are beginning to build up a picture of factors that are protective against AI slop. In addition to additive training, there might also be personality traits that contribute to this. I previously wrote about this concept during the spring:

Rosella et al. ask "what should we actually teach" in relation to GenAI3. Writing in npj Digital Medicine, an International AI in Medicine Education Working Group drawn from 23 universities and healthcare centres sets out seven domains and 24 learning objectives, aimed at undergraduate medical education but adaptable to other contexts. The domains are:

1. fundamentals

2. quality of evidence

3. responsible healthcare AI

4. deployment, post-deployment monitoring, evaluation and oversight

5. leadership and change management

6. anticipating future trends

7. case studies.

The first three build appraisal skills, the next three extend into implementation, and the last integrates everything through case-based reasoning.

The report is refreshingly honest. They report real disagreement about how much technical depth clinicians need, settling on conceptual fluency because "the aim is not to train all clinicians to become AI developers". They also found responsible AI was "the domain where the widest variation in curricula existed", with some programmes running standalone ethics modules and others barely addressing it.

They close by asking the ACGME and national Royal Colleges to treat AI competency as required and assessable. As with many of these ‘frameworks’ papers, its worth noting that this is expert consensus rather than a formal Delphi, and their recommendations have not been validated in any way.

I’ve written before about my frustration at the absolute glut of data-lite frameworks in AI x MedEd literature and conferences. Ghavami Hoseinpour et al. have produced another one by synthesising 25 articles into a five-domain ethical framework4:

1. ethical foundations

2. educational integration and curriculum governance

3. clinical training and human oversight

4. assessment and academic integrity

5. data governance

While the framework is sensible, I'm not sure it adds much. The useful part of this paper, and the reason I've included it here, is their critique of frameworks in general. They find that "implementation strategies remain largely abstract, with minimal empirical validation", that institutions risk "ethics washing" by endorsing principles without building accountability, and that the field keeps framing algorithmic bias as a technical flaw rather than "a reflection of broader institutional, historical, and sociocultural inequities embedded within medical education systems themselves".

They also note that student perspectives are nearly absent from this literature, which they attribute to learners being positioned as passive recipients of systems designed for them.

Ironically the authors also say that their own framework "remains a preliminary conceptual model that has not yet undergone formal validation", which is bold for a paper criticising unvalidated frameworks!

Based on these papers I think the problem is reasonably clear. Cai et al. show that the best randomised evidence we have cannot distinguish AI instruction from a good teacher. Effiong and Effiong (and lots of other studies before them) show that near-universal adoption happened anyway, with the majority untrained. Rosella et al. and Ghavami Hoseinpour et al. argue that AI use should be governed as an ongoing commitment rather than a one-off.

If AI's demonstrated advantage is over poor teaching rather than good teaching, then every deployment decision is really a question about what it replaces. Swapping a supervised session for a chatbot is not supported by this evidence. Filling the hours between timetabled teaching, when most students currently get nothing at all, probably is.

This Week in MedEd

9 open funding calls, 40 upcoming events, 47 live jobs (22 new, 15 closing this week), including:

• 💰 Medical Student Education Program (up to $1.625M from the US Health Resources and Services Administration, deadline 1 Sep 2026)

• 🎓 AI in the Global Classroom: Between Innovation and Inequity (ASME MEGAbite, online, 9 Sep 2026)

• 💼 Faculty Open Rank (Tenure), Data Science and AI in Health Professions Education (University of Illinois Chicago)

The full list, with deadlines, links and eligibility": Try everything free for 30 days.

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The simulated patient encounter is one of the most expensive things a medical school does. Actors have to be recruited, trained, and paid. They need car parking and cups of tea. OSCE circuits need rooms, examiners, and a whole day of everyone’s diary. Simulation centres need managers, technicians and other support staff. In the past, we had assumed that these activities would be the last to be automated, because simulation is relational, embodied, and stubbornly human.

However, that assumption is now out of date. Not only has AI become more capable and freely available, but the challenges associated with scaling traditional simulation are becoming insurmountable. I initially wrote about this capacity challenge, and the potential for AI to be part of the solution in Simulation in Healthcare last year1.

I have selected four recent papers that map how AI is being used in simulation, and between them they cover the whole cycle: writing the case, playing the patient, marking the station, and running the debrief afterwards. You can read more about my own AI patient simulator, SimPatient, here:

Key points

Thind et al. undertook a scoping review, which screened 1,130 records down to 392. The review describes how AI was implemented in undergraduate clinical skills teaching between January 2022 and January 2026. They identified eight categories:

• LLM-based virtual patient and clinical simulation systems account for 19 of the 39 papers

• AI-augmented OSCE and simulation assessment (6)

• Embodied and robotic simulations (4)

• Procedural skills training (3)

• Documentation and multimodal analytics (2 each)

• Educator-facing case authoring (2)

• Clinical reasoning tutoring (1).

The pace of development is increasing; 25 of the 39 were published in 2025 alone. Feasibility and acceptability were consistently reported while longitudinal retention and transfer to real patients were almost never measured, which we have been echoed in other recent editions of this newsletter.

Müller et al. built four GPT-4o patient personas mapped to core objectives in the German national curriculum3:

1. Shared decision-making

2. Motivational interviewing

3. Sexual history taking

4. Breaking bad news

A total of 162 students each had a single conversation, which lasted a median 22 minutes and a mean of 13 prompt-response pairs, then received structured AI feedback across eight criteria. Self-rated communication competence rose from 5.89 to 6.83 on a 0 to 10 scale (mean difference 0.94, Cohen d=0.58), with significant gains in three of the four cases and the largest gains among students who started lowest.

Interestingly, motivational interviewing produced no significant change. The authors suggest this is because the AI is not very good at portraying a patient who expresses ambivalence and resistance, which "a fundamentally supportive AI chatbot may struggle to represent adequately". I think this might be the best material example of sycophancy or acquiescence bias in medical education.

Students rated the AI feedback useful (7.92 out of 10), but feedback adequacy did not correlate with competence gain at all (r=0.08, p=.32), and the model awarded 85% to 92 of the 162 students. More sycophancy?! It should be noted that there was no control group and the student outcomes are self-reported, so read this as evidence about confidence rather than competence.

León-Ariza et al. completed a scoping review to consider how AI can be used to mark OSCEs4. They screened 421 records to 22 included studies and used the FACETS and SAMR frameworks. Automated video scoring produced higher and more uniform marks than human examiners (intramuscular injection 28.23 versus 25.25, knot tying 16.07 versus 10.44), with agreement strongest for visually observable tasks and weakest for communication-dependent elements.

ChatGPT-4.0 scored clinical records at a level statistically indistinguishable from residents while 3.5 did not. When generative models played the simulated patient, 35% of faculty rated the performance excellent and another 35% good, yet 85% identified limitations in non-verbal communication and the obvious inability to be physically examined.

Their conclusion is that AI currently augments rather than transforms OSCE assessment. Their paper contains the now ubiquitous recommendations / frameworks for use, including local validation before adoption in a new curriculum or language, bias audit across learner subgroups at fixed intervals, a documented appeal pathway when AI-supported scores affect progression, and a flat recommendation against fully autonomous AI scoring in high-stakes decisions.

Khan et al. close the loop by examining how AI can be used to support simulation debriefs5.. Reviewing a decade of work on AI in extended reality debriefing, they identify four functions:

1. Converting XR telemetry into performance analytics

2. Using NLP to transcribe and code the discourse

3. Deploying conversational agents to scaffold reflective dialogue

4. Fusing action, speech, gaze and physiological signals into adaptive feedback.

The evidence base is feasibility studies, pilots and prototypes, with almost no controlled comparisons and no psychometric validation of the AI-derived metrics. They land on a hybrid model in which AI prepares transparent artefacts (transcripts, event highlights, suggested advocacy-inquiry prompts, each with timestamps and quotations) while the human facilitator retains psychological safety and high-stakes interpretation. They also flag that NLP systems mis-transcribe non-native accents, which in most medical schools is not a marginal concern.

So, based on these papers, it seems that AI may be useful in some parts of the simulated encounter. Particularly ones that are observable, repeatable, and countable: generating the case, holding a conversation to a brief, checking whether a step happened, flagging the ninety seconds worth returning to.

It seems AI is poor at the parts of simulation that are relational and situated: ambivalence, non-verbal cues, professionalism, and the collective sense-making that turns an event into learning.

So, presumably if we hand the observable parts to the machine and keep the relational parts for ourselves, we should be honest that we are keeping the half we have the least evidence about. So my suggestion: use these tools to multiply the number of low-stakes repetitions a student can get before they meet a real actor, and keep the actor, the examiner, and the debrief for the encounter that actually counts.

This Week in MedEd

You may have noticed that there was no newsletter last week. I completely forgot to post anything because I was enjoying AMEE 2026 in Vienna! However, I have continued tracking opportunities, including 9 open funding calls, 35 upcoming events, 34 live jobs (11 new, 9 closing this week), including:

• 💰 Connecting Champions for Diversity, …

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This Week in MedEd

7 open funding calls, 31 upcoming events, 37 live jobs (18 new, 7 closing this week), including:

• 💰 Advanced Digital Skills for AI Uptake in Health (up to €3.9M from the European Commission’s Digital Europe Programme, deadline 1 Oct 2026)

• 🎓 AMEE 2026 (Vienna, Austria, 22-26 Aug 2026)

• 💼 Assistant Professor in Fair and Responsible AI (Imp…

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This Week in MedEd

7 open funding calls, 24 upcoming events, 27 live jobs (15 new, 5 closing this week), including:

• 💰 EdTech Accelerator (up to €2.7M for European EdTech startups and SMEs, deadline 1 Oct 2026)

• 🎓 IAMSE 2026 Virtual Forum (online, global, 20-22 Oct 2026)

• 💼 Professor: AI & Health (University of Johannesburg, closes 9 Aug)

The full list, with…

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This Week in MedEd

1 new event, 23 jobs (including 8 new and 2 closing this week), including:

• 💰 Advanced Digital Skills for AI Uptake in Health — up to €3.9M, EU consortia, deadline 1 Oct 2026

• 🎓 EBMA 2026: Annual European Conference on Assessment in Medical Education — Lancaster, UK, 1–3 Dec 2026

• 💼 Associate Dean of Digital Education — BIMM University, L…

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The Substack Post

Against Claudefishing

It’s getting harder to tell what’s real on the internet…

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2 months ago · 6461 likes · 1476 comments · Chris Best

False Positives

We have all seen instances of false positive results, when…

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This Week in MedEd

3 new funding calls, 2 new events, 21 jobs (including 6 new and 3 close this week)

⭐ Closes Today: University of Buckingham is hiring a Senior Lecturer, MLA Lead (Clinical), to lead its Medical Licensing Assessment programme.

The full database, with deadlines, links and eligibility, free for 30 days:

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Most of the debate about AI in medical education has been about learning: how students revise, how we teach, whether a chatbot helps or hinders. Assessment has had less attention, which is odd, because assessment is where the stakes are highest. A wrong answer in a revision session is a teaching moment. A flawed item in a licensing exam is a fairness pr…

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A note before we begin: I wanted to flag the 2026 Edward J. Stemmler M.D. Medical Education Research Grant Program, which supports the research and development of innovative assessment approaches with the potential to advance assessment in medical education. The grant ceiling is $150K. If you want to apply for this grant you have to submit your Letter o…

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Summer is well and truly here in Scotland, and I am looking forward to conference season. I will be attending AMEE in Vienna 🇦🇹 this year to present some exciting work on SimPatient. Please reply to this email if you’re going — it would be great to meet up.

I am also currently tracking 20 international Medical Education conferences and over £250K worth…

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Hello everyone!

Many of you will remember that academics used to have something called ‘summer’, which was a break between academic years when we could planning, think and write about things that we didn’t have time to do during periods of busy teaching. Most of us don’t have much of a summer any more. In my University, assessments run later into June, a…

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Hello everyone.

Before we get started on this week’s roundup, a couple of updates:

I wanted to take a moment to welcome a flurry of new subscribers to this newsletter, which is now routinely read by 7500 people per month from 70 different countries. If you have colleagues who would be interested in this newsletter please share it with them:

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Last year I wrote about the AI tools that have become indispensable to research and teaching, and a number of readers wrote back asking the obvious follow-up question. What about everything else? The schedule, the inbox, the notes, the references, the slowly accumulating backlog of papers that need reading. The plumbing of academic work, in other words, rather than the AI work itself.

It is worth pausing on why this matters. The American Time Use Survey shows that people who work spend an average of 8.1 hours a day doing so, and those who pursue formal education add 5.4 hours on top. Most of us recognise these figures all too well. The point of this productivity toolkit, then, is not that it makes you more virtuous or harder-working. It is that the right tools let you finish the same work in less time, and the hours you reclaim are the ones that belong to your family, your friends, and the parts of yourself that have nothing to do with academia.

A note on framing before we jump in to the list. Most academics no longer have the luxury of choosing their core infrastructure: institutional contracts have pulled almost everyone into the Microsoft 365 ecosystem, with Outlook, Teams, OneDrive, and the rest. That is not a complaint. Microsoft's offer has improved substantially in recent years and the data governance story for institutional users is, on balance, the right one. Where I name a Microsoft tool as the institutional default below, that is recognition of the constraint most readers actually face. Where I name something else as the stand-out, it is because there is a genuine case for paying for, or switching to, the better tool.

Contents

• 📋 Task and Project Management

• 🧠 Notes and Knowledge Management

• 📚 Reference Management

• 📖 Reading and Manual Note-Taking

• ✍️ Writing, Presenting, and Data

• 📅 Calendar and Scheduling

• 🎯 Focus and Deep Work

• 🎙️ Meeting Capture and Transcription

• 🤖 AI Assistants for Recurring Tasks

• ✈️ Travel for Conferences

📋 Task and Project Management

Where your week actually lives. Most academics now run three or more lists in parallel across institutional and personal tools.

Things 3

Best task manager for Apple users who don’t collaborate.

A focused, Apple-only task manager that turns a chaotic week into a calm review. Strong Today and Upcoming lists, lightweight projects, and a level of polish that makes capture genuinely frictionless.

This app uses the Getting Things Done (GTD) method and it has genuinely changed my life. No more panicking about forgotten tasks. No anxiety about future tasks, which always resurface when it’s time to work on them. No more missed deadlines.

If you need to stay in the Microsoft 365 ecosystem then I would recommend your institutional default, which is most likely Microsoft To Do. It’s not all bad, though! The visual design, on iOS at least, is clean and refreshing, and it is tied directly to Outlook flagged emails and Teams tasks, so it earns its place by being already wired in.

If Things 3 is too Apple and Microsoft To Do is too simple, I would suggest looking at Todoist. Cross-platform, with natural-language entry and powerful filters. The right pick for researchers working off Windows or Android, or collaborating across institutions

🧠 Notes and Knowledge Management

The second brain. The space where lecture prep, reading notes, half-formed ideas, and meeting minutes accumulate.

Notion

Best second brain for anyone who gets lost their notes.

What sets Notion apart for academics is not the document editor but the database layer. A linked database of reading items, grant applications, supervision tasks, or course materials behaves like a small research information system, with views, filters, and relationships across them. Once you start using it this way it is difficult to go back.

If you live on Apple devices, Apple Notes has quietly become the most reliable everyday tool. Quick capture, document scanning, handwriting recognition, smart folders, and instant sync across iPhone, iPad, and Mac. Free, fast, and good enough for most users.

If simplicity is your thing, Apple Stickies or Microsoft Sticky Notes is probably what you’re looking for. When the goal is “get this thought out of my head before the next meeting”, a sticky note pinned to the desktop is still the most honest tool. Microsoft’s version is slightly superior: it syncs across your devices via OneDrive.

Microsoft also make OneNote, but it is one of their weaker and clunkier Office products.

📚 Reference Management

Citations, PDFs, and the slow accumulation of “I’ll read this later”. Choice here usually lasts a career.

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