In between defending my PhD and starting medical school, I did a post-doctoral fellowship in the research lab of a friendly professor who was willing to hire me for a short (less than one year) stint. I used this time to develop an assay that allowed us to identify which compounds would stop the growth of cancerous cells grown as organoids. This sounds very sexy and probably brings to mind shiny, advanced lab equipment.

In fact, the lab did have some automated equipment, but all they did in practice was collect dust. The person who had been trained to use the equipment had left the lab, and my PI didn’t want to pay for me to be trained on the equipment since I was leaving soon. It would be another 10 years before I got to start actually working with lab automation.

Unfortunately, my story isn’t unique. Most students today are still trained without any exposure to laboratory automation. Although this tooling has become standard to use in the workplace, there are still significant barriers to bringing automation into classrooms.

I’ve worked with dozens of educators around the world who are facing this challenge today. From world-ranked universities to public high schools, the curriculum is built from scratch, with each educator figuring out what to teach without the benefit of data or guidelines on what will be most impactful for their students.

Meanwhile, governments and industry alike are pouring money into laboratory automation, a fire fueled by advances in AI. As a result, we’re scaling the technology without scaling the training - a recipe for expensive equipment collecting dust.

But that’s changing, thanks to the effort of a world-class team of experts across education and industry, with the support of key coalitions like SLAS and the Acceleration Consortium. The Alfred P. Sloan Foundation is funding this initiative, ensuring we have the resources to do this right and maximize the impact.

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The Consequences of Fragmentation

The innovation cost is significant. When I speak to labs with substantial automation about hiring, they all say the same thing: the hardest role to fill is scientists who are adept with automation. Research possibilities go unexplored, not because the technology isn’t ready, but because the workforce isn’t prepared. Without adequate training, those infrastructure investments won’t deliver their potential. Left unaddressed, they become wasted capital. The matching problem compounds this: job descriptions use inconsistent terminology, graduates don’t know how to signal their capabilities, and employers lack reliable ways to evaluate candidates.

Despite great demand, programs that want to teach automation face excess difficulty. Even with supportive administration and nearby colleagues, a professor can find themselves spending countless hours figuring out what topics should even be included in the curriculum. The programs that have managed to develop despite this have had to forge ahead without guideposts. Many of these training programs are incredibly effective and boast perfect job placement rates for their graduates; their results can be replicated, but not if every program has to create everything from scratch. This inefficiency compounds across every level of the system.

What Guidelines Make Possible

There’s a tendency to view guidelines as limiting, that they define a ceiling instead of a floor. But in the current state, the opposite holds more truth: the absence of guidelines today has resulted in the absence of a shared foundation on which to build the industry workforce.

There’s a tendency to view guidelines as limiting, but the opposite is true here. Right now, the absence of guidelines is what constrains us.

Starting from a blank slate is daunting. You have to decide what to teach, how to sequence it, how to assess it, and how to connect it to learning outcomes, all while lacking examples of what works elsewhere. After justifying those decisions to yourself, you must also convince your colleagues and administration. Well-designed guidelines provide a starting point to adapt for your context.

The value extends to existing programs, too. Guidelines create a shared understanding from which to articulate what makes each program distinctive. They facilitate collaboration between institutions. They make it easier to assess outcomes and iterate on curricula.

Then there’s portability. No matter where you graduate from, there’s no universal way to communicate the skills you’ve developed to potential employers. This gets overcome within certain sectors or regions where employers come to know certain programs, but the result is smaller, fragmented pools in which graduates and employers can find one another. Guidelines create a shared language: students can communicate their skills clearly, and hiring managers can evaluate unfamiliar programs confidently.

What We’re Building

I’m not alone in seeing this gap. When I first crossed paths with Josh Kangas, the Co-Director of the Masters of Science in Automated Science Program at Carnegie Mellon University, one of the first things we discussed was the lack of education guidelines and the impact of their absence. Since then, we’ve had the opportunity to collaborate on a few projects, including starting a community through SLAS to bring together those teaching with automation. Through this, we realized we were the ones best positioned to address this gap, and put together our proposal to develop the first set of guidelines for teaching laboratory automation.

Guidelines are only valuable if they’re widely adopted. A key part of our proposal from the beginning has been the development of implementation resources for educators.

The guidelines can only be as good as the data they’re built on. We prioritized breadth of perspective alongside expertise when assembling the drafting committee, and the first order of business was a pair of surveys to reveal what the industry needs and how to overcome the [barriers educators face](link to Systems over Symptoms). An analysis of job descriptions to develop an understanding of career paths agnostic to the variability in titles from industry to industry.

These will allow us to develop education guidelines with consistent terminology that maps to real-world roles, ideally a sort of universal language for automation competence. Educator resources that can adapt to various teaching contexts. Everything will be published under Creative Commons licensing to ensure broad accessibility. This work is collaborative by design, to reflect the full spectrum of automation work, from laboratory technician to principal investigator to business leader. The guidelines will be released in January 2027.

SLAS deserves particular recognition for providing critical infrastructure support throughout development, committing to host the guidelines long-term, and ensuring their ongoing maintenance. Together with support from the Acceleration Consortium and others, we’re building the cross-industry, globally accessible framework this field needs.

Building Infrastructure That Scales

What we’re building addresses this at the systems level. Guidelines themselves are only valuable if they are implemented, so we’re also developing the resources that make adoption straightforward. Not just for new programs, but for strengthening existing programs too. We’re equipped for this task because our community-based approach keeps us focused on real solutions to the biggest problems.

The infrastructure we’re building can ensure that automation knowledge becomes portable, shareable, and resilient. No more reinventing the wheel. No more expensive equipment sitting idle due to a lack of skilled users. The guidelines will be released in January 2027, but the impact starts now with every educator and industry professional who contributes to building this shared foundation.

If you’ve already participated in one of our surveys, thank you. If you’re interested in contributing to the development of these guidelines, please reach out here or on LinkedIn.