Building Research Capacity with AI

Over 25 years ago, the “10/90 gap” was used to illustrate the global imbalance in health research. Only 10% of global research benefited the regions where 90% of preventable deaths occurred. Since then, efforts to improve research capacity in low- and middle-income countries (LMICs)—where 90% of avoidable deaths occurred—have made important gains; nonetheless, significant challenges remain. A quarter of a century later, there are still too few well-trained researchers in LMICs, and their research infrastructure and governance are also inadequate. The scope of the problem increased dramatically in 2025 when governments cut North American and European overseas development assistance (ODA, i.e., foreign aid) precipitously. That aid—however inadequate—supported improvements in research capacity.

Traditional approaches to improving research capacity, such as training workshops and degree scholarship programs, have gone some way to address the expertise challenge. However, they fall short because they are not scalable. The relatively recent introduction of massive open online courses (MOOCs), such as TDR/WHO’s MOOCs in implementation research, goes a long way to overcoming that scalability problem—at least in instruction-based learning. Nonetheless, for many LMIC researchers, major bottlenecks remain because of poor or limited access to mentorship, one-off and quick advice, bespoke training, research assistance, and inter- and intra-disciplinary collaboration. The scalability problem can leave them at a persistent disadvantage compared to their high-income country counterparts. Research is not done well from isolation and ignorance.

The rise of large language model artificial intelligence (LLM-AIs) such as ChatGPT, Mistral, Gemini, Claude, and DeepSeek offers an unprecedented opportunity…and some additional risks. LLM-AIs are advanced AI models trained on vast amounts of text data to understand and generate human-like language. They are flexible, multilingual, and always available (24/7), offering researchers in LMICs immediate access to knowledge and assistance. If used correctly, LLMs could revolutionise approaches to building research capacity and democratise access to skills, knowledge, and global scientific discourse. Many online educational providers already integrate LLM-AIs into their instructional pipelines as tutors and coaches.

Unfortunately, LMICs risk further entrenching or increasing the 10/90 gap if they cannot take advantage of the benefits of LLM-AIs.

AI as a game changer

Researchers in resource-limited settings can access an always-on, massively scalable assistant for the first time. By massively scalable, every researcher could have one or more 24/7, decent research assistants for a monthly subscription of less than $20. They offer scalability and flexibility that traditional human research assistants cannot (and should not) match. However, they are not human and may not fully replicate a human research assistant’s nuanced understanding and critical thinking—and they are certainly less fun to have a cup of coffee with. Furthermore, the effectiveness of LLM-AIs depends on the sophistication of the user, the task complexity and the quality of input the user provides.

I read a recent post on LinkedIn by a UCLA professor decrying the inadequacies of LLM-AIs. However, a quick read of the post revealed that the professor had no idea how to engage appropriately with the technology.

Unfortunately, like all research assistants, senior researchers, and professors, LLM-AIs can be wrong. Like all tools, one needs to learn how to use them with sophistication.

In spite of any inadequacies, LLM-AIs can remove barriers to research participation by offering tutoring on complex concepts, assisting with literature reviews and data analysis, and supporting the writing and editing of manuscripts and grant proposals.

Reid Hoffman, the AI entrepreneur, described on a podcast how he used LLM-AIs to learn about complex ideas. He would upload a research paper onto the platform and ask, “Explain this paper as if to a 12-year-old”. Hoffman could then “chat” with the LLM-AI about the paper at that level. Once comfortable with the concepts, he would ask the LLM-AI to “explain this paper as if to a high school senior”. He could use the LLM-AI as a personal tutor by iterating-up in age and sophistication.

Researchers can also use the LLM-AIs to support the preparation of scientific papers. This is happening already because an explosion of generically dull (and sometimes fraudulent) scientific papers is hitting the market. This explosion has delighted the publishing houses and created existential ennui among the researchers. The problem is not the LLM-AIs—it is in their utilisation, and it will take time for the paper production cycle to settle.

While access to many LLMs requires a monthly subscription, some LLM-AIs, like DeepSeek, significantly lower costs and accessibility barriers by distributing “open weights models”. Researchers can download these open weights models freely and put them on personal or university computer infrastructure without paying a monthly subscription. They make AI-powered research assistance viable for most LMIC research settings, and universities and research institutes can potentially lower the costs further.

LLM-AIs allow researchers in LMICs to become less dependent on high-income countries for training and mentorship, shifting the balance towards scientific self-sufficiency. AI-powered tools could accelerate the development of a new generation of LMIC researchers, fostering homegrown expertise and leadership in relevant global science. They are no longer constrained by the curriculum and interests of high-income countries and can develop contextually relevant research expertise.

The Double-Edged Sword

Despite its positive potential, the entry of LLM-AIs into the research world could have significant downsides. Without careful implementation, existing inequalities could be exacerbated rather than alleviated. High-income countries are already harnessing LLM-AIs at scale, integrating them into research institutions, project pipelines, training, and funding systems. LMICs, lacking the same level of investment and infrastructure, risk being left behind—again. The AI revolution could widen the research gap rather than close it, entrenching the divide between well-resourced and under-resourced institutions.

There is also a danger in how researchers use LLM-AIs. They are the cheapest research assistants ever created, which raises a troubling question: will senior researchers begin to rely on AI to replace the need for training junior scientists? Suppose an LLM-AI can summarise the literature, draft proposals, and assist in the analysis. In that case, there is a real risk that senior researchers will neglect mentorship, training and hands-on learning. Instead of empowering a new generation of LMIC researchers, LLM-AIs could be used as a crutch to maintain existing hierarchies. If institutions see the LLM-AIs as a shortcut to productivity rather than an investment in building research capacity, it could stall the development of genuine human expertise.

Compounding these risks, AI is fallible. LLM-AIs can “hallucinate”, generating false information with complete confidence. They always write with confidence. I’ve never seen one write, “I think this is the answer, but I could be wrong”. They can fabricate references, misinterpret scientific data, and reflect biases embedded in their training data. If used uncritically, they could propagate misinformation and skew research findings.

The challenge of bias is not to be underestimated. LLM-AIs are trained on the corpus of material currently available on the web, reflecting all the biases of the web–who creates the content, what content they create, etc.

Furthermore, while tools like DeepSeek reduce cost barriers, commercial AI models still pose a financial challenge. LMIC institutions will need to negotiate sustainable access to AI tools or risk remaining locked out of their benefits—particularly of the leading edge models. The worst outcome would be a scenario where HICs use AI to accelerate their research dominance while LMICs struggle to afford the very tools that could democratise access.

A Strategic Approach

To ensure LLM-AIs build rather than undermine research capacity in LMICs, they must be integrated strategically and equitably. Training researchers and students in AI literacy is paramount. Knowing how to ask the right questions, validate AI outputs, and integrate results into research workflows is essential. This is not a difficult task, but it takes time and effort, like all learning. The LLM-AIs can help with the task—effectively bootstrapping the learning curve.

Rather than replacing traditional research capacity building, LLM-AIs should be embedded into existing frameworks. MOOCs, mentorship programs, and research fellowships should incorporate LLM-AI-based tutoring, iterative feedback, and language support to enhance—not replace—human mentorship. The focus should be on areas where LLM-AI can offer the greatest immediate impact, such as brainstorming, editing, grant writing support, statistical assistance, and multilingual research dissemination.

Institutions in LMICs should also push for local, ethical LLM-AI development that considers regional needs. This push is easier said than done, particularly in a world of fracturing multilateralism. However, appropriately managed, LLM-AI models can be adapted to recognise and integrate local research priorities rather than merely reinforcing an existing scientific discourse. The fact that a research question is of no interest in high-income countries does not mean it is not critically urgent in an LMIC context.

Finally, securing affordable and sustainable access to AI tools will be essential. Governments, universities, and research institutions must lobby for cost-effective AI licensing models or explore open-source alternatives to prevent another digital divide. Disunited lobbying efforts are weak, but together, across national boundaries, they could have significant power.

An Equity Tipping Point

The LLM-AI revolution is a key juncture for building research capacity in LMICs. Harnessed correctly, LLM-AIs could break down long-standing barriers to participation in science, allowing LMIC researchers to compete on (a more) equal footing. The rise of models like DeepSeek suggests a future where AI is not necessarily a privilege of the few but a democratised resource for the many.

Fair access will not happen automatically. Without deliberate, ethical, and strategic intervention, LLM-AIs could reinforce existing research hierarchies. The key to harvesting the benefits of the technology lies in training researchers, integrating LLM-AIs into programs to build research capacity and securing equitable access to the tools. Done well, LLM-AIs could be a transformative force, not just in scaling research capacity but in redefining who gets to lead global scientific discovery.

LLM-AIs offer an enormous opportunity. They could either empower LMIC researchers to chart their own scientific futures, or they could become another tool to push them further behind.

Acknowledgment: This blog builds upon insights from a draft concept note developed by me (Daniel D. Reidpath), Lucas Sempe, and Luciana Brondi from the Institute for Global Health and Development (Queen Margaret University, Edinburgh), and Anna Thorson from the TDR Research Capacity Strengthening Unit (WHO, Geneva). Our work on AI-driven research capacity strengthening in LMICs informed much of the discussion presented here.

The original draft concept note is accessible here.

US Aid: Strategic Transactionalism

The US Government is showing all the compassion of a loan shark, where the Rubio Rule rules—”What’s in it for me?” Tragically, any international social capital the U.S. built over the past 80 years has been torched in a bonfire of pointless cruelty.

Yesterday, Politico published a draft document obtained from a government aide describing a revamped USAID. It is entirely about the U.S.—safer, stronger, more prosperous—with the benefits to others only arising en passant, if at all.

According to the document, international assistance will focus on investments that deliver “first-order benefits back home [in the U.S.]”. That is, there must be an immediate, directly attributable gain to the U.S. from it’s humanitarian “investment”. This is in marked contrast to the successful decades of soft power developed by the U.S. after World War II.

Global Health would sit in a new agency for International Humanitarian Assistance (IHA).

“By responding rapidly to natural disasters, preventing famines, containing disease outbreaks, and securing peace, IHA would demonstrate American values, prevent instability that could threaten our interests, distinguish ourselves from our geo-political adversaries (such as China), enhance U.S. leadership on the global stage, and increase safety at home.”

That sounds great! I wonder what those Amercian values are? They are not the values of compassion or empathy, nor the values established by the U.S. in the international human rights instrument. Those international values established by the U.S. are the ones that the Trump Administration has “put through the wood chipper”.

The values must be those of “strategic transactionalism”. This is my newly minted term that refers to the idea that no agreement with the U.S. can be trusted because it has shown itself to have no regard for such things. Agreements, contracts, and treaties exist only to the extent that the administration chooses to honour them. Here today, gone tomorrow.

What’s in it for me?

According to the document, “in all cases, countries would have to demonstrate high levels of “commitment” to be eligible for any U.S. assistance engagement”.

“High levels of ‘commitment’” is code in the world of crime bosses and authoritarian leaders for absolute subjugation. A country will do what it is told, when it is told—without question. Give up resources. Cede Territory. Treat some people as less worthy than others.

“Success would be measured by concrete metrics: lives saved, outbreaks of infectious diseases contained, pandemic prevented, famines averted, and measurable increases in in positive perception of the United States in emerging markets.”

The irony of this is that things like “lives saved” is exactly what was being measured before the conflagration. The new metric appears to be “measurable increases in positive perception of the United States in emerging markets”.

Rest assured, any positive perception of the U.S. is now only a form of “strategic transactionalism”. It is performative because money is still money, but anything else from the US risks bully, bluster, and betrayal.

The Star Trek Captain, Jean-Luc Picard as the Borg character Locutus

Resistance is necessary

I complain. A lot. I am not a happy person. But you will never die wondering what I thought or where I stood. Still, complaining isn’t enough, and whinging can feel futile.

The Borg and the rising authoritarian states of the 21st century want you to believe that “resistance is futile.” It isn’t. Resistance is not only necessary; resistance is an obligation.

Small acts of everyday resistance can raise the costs of authoritarianism so high the system collapses. In the late Soviet Union, acts of passive resistance—from workers deliberately slowing down production to citizens openly defying censorship laws—contributed to the erosion of state control. These acts of everyday resistance helped to chip away at the crumbling foundations. Authoritarian regimes rely on compliance to function. When enough people withdraw their cooperation, inefficiency turns into paralysis, and paralysis into collapse. It becomes so grindingly inefficient and ineffective that it fails. The unwillingness of the people to work in the interests of an illegitimate state is that state’s undoing.

Small acts of everyday resistance need not rise to criminality. There are ways of resisting that work, that keep the pressure up, and that allow you to control your level of exposure.

The power of the authoritarian state does not lie in compliance alone. It also lies in isolation—your sense of being alone in your unhappiness. Why do you think the Chinese state is so quick to remove online complaints and hide protests? The protest is not the problem. The protest’s effect is letting others know they are not alone in their unhappiness. And if you do not feel alone, you are also more likely to engage in small acts of everyday resistance.

Work to rule is a classic form of everyday resistance. This tactic has been historically effective in labour movements, such as the bureaucratic slowdowns under oppressive regimes, where workers deliberately followed every regulation to the letter to hinder authoritarian efficiency. Do your job. To the letter. No more. No less. When only one person works to rule, they are a miserable, unhelpful arse. When large numbers of people work to rule, unhappiness shows. It is palpable. In a government department that is engaging in immoral and cruel behaviour (“within the law”), you can slow it down, throw sand in the gearbox, and make it less cruel by being less effective and less efficient.

In the U.S., ICE agents could do their jobs—badly. Administrative staff supporting ICE agents can slow things down by moving paper at an excruciatingly necessary pace. The word “expedite” should be struck from the vocabulary.

Singapore in the late ’80s and ’90s was a highly (overly) regulated society. Many would say it has persisted. But in the ’90s, chewing gum became a tool of everyday resistance. People would stick it over the door sensors on the MRT trains. The doors couldn’t close, and it would bring the system to a grinding halt. The act was small, non-specific in its target, and (back then) unidentifiable.

Posters, protests, badges, public art, and internet memes have all been used to demonstrate everyday resistance. Remembering when the state wants to forget or reimagine a truth is a powerful corrective. Archive the truth on the internet.

In a digital age, careful choices about how and when to use devices, credit cards, and online accounts can disrupt data collection and tracking. Using burner phones where you can get them, paying with cash instead of cards, and setting up anonymous online accounts are small but effective ways to limit surveillance and maintain privacy. Resistance is not about criminality; it is about the right to privacy, the freedom to think, and the quiet power of refusing to comply—to engage in cruelty. Even small acts of everyday resistance remind others they are not alone.

It is possible to resist and chew gum at the same time.

Resources:

If you want some ideas, have a look at these two.

An image of two children in Belgian Congo. One is seated and one is standing. Both children are missing their right hands.

Aid cruelty is not an opportunity

I have followed with genuine interest the responses of some sub-Saharan African (SSA) writers to the collapse of foreign aid in 2025. Whether they reside in SSA or enjoy a diasporic life in the Global North, they have argued that the loss may be an opportunity gifted to the Global South. While millions will die, SSA will at last be able to throw off the multi-billion dollar shackles to which it was so unwillingly chained. How awful to have been placed in the position of choosing between the “n”-word—“no”—and the “y”-word—“Yes!”—when offered money.

The tenor of the writing suggests that in making the offer of aid, countries in the Global South were stripped of agency. They could only rediscover agency when they were stripped of the money. The evil aid system by which the Global North klept [sic] them enthralled has at last been dismantled. The opportunity, long denied, has finally emerged to build health and development systems that “work for Africa”.

You will, I hope, forgive me if I do not join that cheer squad or Greek chorus.

In left-wing politics, there is an aphorism that it is better to suffer exploitation than starvation. To cheer unemployment for the liberating opportunities it provides from the excesses of exploitative capital is as short-sighted as it is stupid. That does not mean exploitation is acceptable. It is not. It must be resisted and fought. But starvation is not the solution.

If foreign aid was a shackle, its sudden removal should be freeing. But stripping away the existing system does not automatically lead to something better. Stretched governments cannot replace the wreckage of collapsed health programs overnight. What may look like liberation on paper is abandonment. A just transition requires negotiation and genuine collaboration. It requires time.

If the goal was to end aid, donor countries could have managed future aid through a phased reduction. The process could include such things as a shift to loans on beneficial terms combined with early debt management and relief. The development of capacity, systems, and infrastructure would need to be a part of it.

When you reach into the water to remove a life-jacket from a drowning man, you have not provided him with an opportunity to learn to swim, nor have you (passively) “let him die”. You have killed him. He may bob above the waves for a few minutes, even an hour. You may helpfully scan the horizon for a bit of passing flotsam for him to cling to. But when exhaustion finally overwhelms him, and he slips beneath the surface, you are a murderer.

When, with the snap of the fingers, a country closes HIV antiretroviral programs—leaving the drugs to rot and expire in warehouses and shop lots—it has not (passively) let people living with HIV/AIDS die. The donor country condemned them to death and waited.

The personal relationship with the individual drowning and the anonymous one with the hundreds of thousands of people on foreign-aid-funded antiretroviral does not change the moral calculus of the death, and it does not mitigate the callousness and wanton cruelty of the murder.

Aid programs are not light switches that donor countries can (or should) turn off on a whim. Cutting funding overnight destroys systems that took decades to build, leaving chaos in their place. The systems may not have been perfect; they may have needed greater local ownership in the design; they may have supported corruption. However, if the goal is genuine self-reliance, the responsible course is a phased, predictable transition that allows for capacity-building, infrastructure development, and systems design and refinement.

Millions have been condemned to death, others to lives of increased hardship and misery. If donor nations refuse to acknowledge their historical responsibility, then at the very least, they must be held accountable for the consequences of their actions today.

The world’s wealthiest countries’ substantial and immediate reduction in foreign aid turns their backs on the international human rights, their international obligations to support the SDG, and the obligation to leave no one behind. The United States (U.S.) led the pack when they put USAID “through the wood chipper”, but others have followed.

“The UK, the Netherlands, and Belgium have announced the largest cuts in [overseas development assistance] ODA history, and the European Commission, France and Germany are expected to follow soon. These cuts are not just minor shifts, but cliffs: at least USD 60 billion by USA and GBP 6 billion by the UK, EUR 8 billion over four years (2025-2028) by the Netherlands, and a possible EUR 20 billion by Germany.”

What is the unifying historical theme of these donor countries? Empire. They did not build their wealth on ingenuity or fair trade alone. Conquest, forced labour, and resource theft was there. They racialised the right to development. The UK drained its colonies of raw materials while imposing economic structures that prioritised British interests over local development. Belgium’s rule over the Congo was so extractive and brutal that its legacy still echoes in governance failures and economic instability today. France has reluctantly and only recently relinquished control over its former colonies, where it maintained economic dominance through ‘Françafrique’ policies that benefited Paris over Dakar.

Slashing aid is not an opportunity. It is abandonment. Do not let them disguise it as anything else. Do not allow the wealthy nations to pat themselves on the back for their cruelty. It is an outrage, and it must be named as such.

The outrage does not erase the agency of recipient countries that agreed to destructive conditionalities attached to receiving aid. It does not forgive the naked corruption that sometimes occurs. It does not excuse the capacity of poor countries to exploit their even poorer neighbours, nor the exploitation of social stratification within their societies.

But none of these realities justify the wholesale destruction of life-saving programs without a plan, without accountability, and without justice. Nations that built their wealth through exploitation cannot now walk away and abandon vulnerable countries, whether they were directly plundered by them or by others. If they do not uphold their obligations, civil society, recipient governments, and international institutions should demand an ethical transition rather than an overnight abandonment that costs millions of lives. Anything less is complicity in death.