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 (Imperial College London, closes 19 Oct)

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Usually when we read medical education research the papers are single-site and in the general context of reliable electricity, institutional licences, and an assumption that every student owns a laptop. This is obviously not representative of medical education everywhere; the majority of the world’s doctors are trained in settings where none of those things can be taken for granted. This week I have selected papers that describe the state of AI in Medical Education in these contexts, with a focus on infrastructure and readiness.

Adefolalu completed a narrative review of AI in medical education across low- and middle-income countries with sub-Saharan Africa as the primary case1. They identified 2803 records and included 37 in the final synthesis. Of these 26% came from African countries and 38% came from high-income countries with LMIC applicability.

Analysis showed that fewer than 10% of these publications originated from Latin America, Africa or Eastern Europe. Additionally, a 2025 international survey of over 4500 students across 192 heath professions faculties found that more than 75% had received no formal AI education.

The barriers to AI use in medical education in these contexts are infrastructural rather than pedagogical:

• intermittent power

• poor connectivity

• low electronic medical record adoption starving local models of data

• algorithmic bias from training data drawn from WEIRD populations, with worked clinical examples in dermatology, pulse oximetry and cardiovascular risk stratification.

His suggestions for the future are therefore very practical, including mobile-first delivery given smartphone penetration, South-South partnerships rather than default North-South ones, open educational resources to control cost, and ethics teaching grounded in local values,

Abbas et al narrow the same question specifically to UG medical education They identified 210 records from PubMed and Google Scholar published between Jan 2020 and Mar 2025 and sifted them down to 43 articles2. The main finding was that the AI functions as “an amplifier of existing educational practices” rather than as an independent driver of improvement, which means a school with weak curriculum governance gets amplified weak curriculum governance.

The LMIC-specific barriers they name overlap heavily with Adefolalu’s but there is a specific focus on model bias. Models trained on high-income datasets miss local epidemiology, culture and language.. They provide recommendations organised as five pillars, with faculty development and AI literacy as priorities. Their point that faculty need training in critical appraisal of AI outputs rather than in tool operation reminds me a lot of the Feigerlova paper we discused last week3.

They also argue for hybrid assessment, pairing AI evaluation of structured cognitive tasks with human oversight of empathy, communication and professionalism. All of this is very reasonable and aligns with the literature elsewhere, but this is a narrative review with no formal risk-of-bias assessment, only two databases, and almost no quantitative synthesis.

Mamven et al surveyed 143 physician trainers recruited at the West African College of Physicians annual meeting4. Three quarters reported familiarity with AI concepts, but only 9.8% described themselves as ‘very familiar’, and only about 36% were aware of AI tools specifically for medical education. 19% reported ‘frequent use’, mostly for research, and 55% used AI in their role as educators.

They reported a significant gap between belief and provision. 98% thought AI could improve medical education and 90% supported integrating it, yet only 35% had access to AI tools and only 23% had received training.

The named barriers were lack of training (63.4%), tools being unavailable (51.7%) and poor internet connectivity (51%). Frequent AI use was independently associated with holding a higher degree (OR 3.7, 95% CI 1.28 -10.96) and with having attended AI training (OR 2.79, 95% CI 1.01 - 7.72). Nearly everyone (99%) wanted AI as a supplement rather than a replacement.

Al-Madhagi et al ran a similar study, but this time focussed on students rather than educators5. They surveyed 415 undergraduate health professions students at the University of Aden in Yemen using the validated Medical Artificial Intelligence Readiness Scale (MAIRS-MS) . The mean readiness score was 79.82 out of 110, which is a reasonably favourable result and comparable to what has been reported from far better-resourced settings, with the ethics and ability domains scoring highest.

80.2% had prior awareness of AI technologies and 44.8% had used them in a medical context. Male students scored significantly higher on the cognition domain than female students (27.73 versus 26.07, p=0.005) and readiness showed no significant association with age, GPA, academic year or income level.

I know we wouldn’t normally place a great deal of weight on a single-site study, but I think these findings are very interesting. They show that students in one of the most infrastructure-constrained health systems in the world arrive with roughly the same self-assessed readiness and ethical awareness as their peers elsewhere.

So, what’s the common thread that runs through these papers? We know that in high-income settings the conversation is largely about persuading sceptical faculty and restraining over-enthusiastic students. In the settings these papers describe, the enthusiasm is already near-universal and the ethical awareness is already high. What is missing is access and infrastructure . It doesn’t seem to be the case that some places are behind on AI attitudes and need to catch up.

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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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.

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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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The focus of these conversations was ab…

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This week’s roundup examines a collection of recent papers that explore the intersection …

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