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We won’t solve tech’s diversity problem by teaching more people to code

Technovation founder Tara Chklovski argues, “Instead of focusing on how many students learn to code, we should measure what they do with that knowledge.”

We won’t solve tech’s diversity problem by teaching more people to code
[Photos: blacklight_trace/iStock; monkeybusinessimages/IStock]
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It is hardly the norm for a young girl in India to be told she can be anything when she grows up. But as a kid tinkering with engine parts in my dad’s mechanic shop in Delhi, I knew I wanted to be an aerospace engineer. I was lucky that my parents believed in me, but most importantly, I believed in myself. I eventually moved to the U.S. to study aerospace engineering. But as I pursued a master’s and then a PhD, I was struck by how few women were in my program, let alone the engineering field. All too often I would hear my peers brush off working in technical fields, believing they “weren’t good at math.”

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There is a lot of talk in Silicon Valley and beyond about increasing diversity and inclusion (D&I) in tech—including efforts to inspire more girls to study computer science. Yet despite concentrated efforts and millions of dollars in investment over the last decade, the percentage of female and black students in undergraduate computer science programs in the U.S. has actually decreased. The tech industry is still overwhelmingly homogeneous, with companies struggling to attract and retain women and people of color.

It’s time to ask ourselves not only if our investment in bringing underrepresented groups into computer science is working, but also why we are doing it.

The stakes of the D&I problem are only getting higher as artificial intelligence (AI) advances in speed, scale, and impact. AI has the potential to address our most urgent global problems, such as climate change, but solutions need to build resilience for the populations that are closest to these problems. Unfortunately, the field of AI faces an even bigger diversity crisis than the tech industry overall. The answer to “Who builds AI?” has far-reaching consequences, some of which we’re already seeing in systems that perpetuate bias and discrimination.

We can’t afford another decade of confining research and innovation to a small group of mostly white men. After 13 years of working at the intersection of education, tech, and AI, it’s clear to me that improving D&I for the long run—and achieving the potential of technology to unlock a better world—requires much more than putting students through boot camps and coding programs.

We can’t afford another decade of confining research and innovation to a small group of mostly white men.”

Instead of focusing on how many students learn to code, we should measure what they do with that knowledge. What good is developing students’ knowledge if they aren’t able to use it to solve anything relevant to them, or if they aren’t motivated to continue learning and practicing once the program is over?

Studies have shown that students who know the basic syntax of coding—the set of rules that define a programming language—are often unable to apply this knowledge to solve real problems. And, as MIT research has found, young people are motivated by computational action, not just computational thinking.

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To stay engaged, students need opportunities and tools to apply what they learn to their lives. Why should one care about AI, data privacy, or neural networks? Curiosity needs to connect to what each learner finds valuable. Theories such as constructivism, applied apprenticeship, and project-based learning show that the most reliable way to get a novice interested in new topics is to hook them with a meaningful goal.

The team of educators and mentors I work with at Technovation has found that the most reliable and scalable way to encourage this learning is to challenge people to “find a problem in your community.” For instance, we’ve seen a Bolivian family build an AI application to detect CO2 levels in factories and another create a computer-vision-powered “vacuum cleaner” to control the spread of an invasive species in Lake Titicaca. Pursuing a relevant problem with a bigger purpose results in positive feedback and a feeling of self-efficacy.

But a compelling goal isn’t enough to guarantee sustained interest in developing a new skill to advance it, especially a skill as challenging and complex as advanced coding. Continued guidance and support are critical to retaining learners and helping them develop a robust identity as someone who builds things with code, or is a software engineer or computer scientist.

An example of this model is Rare, an organization that helps local communities preserve their natural resources through behavioral change and human-centered design. Another is AI Commons, which connects problem solvers and “problem owners” around the world to address real, local problems using AI. Programs such as these help people develop a sense of purpose while unlocking important skills such as creativity and complex systems thinking. Their hands-on approach to learning can challenge an individual’s notion that they aren’t “cut out” to be an innovator or problem solver.

My hope is not that every kid grows up with dreams of being an engineer or an AI researcher. Instead, I’d like to see a world where more young people imagine themselves as creative thinkers who are capable of solving the problems that impact their communities. I hope that more families encourage their kids to pursue the future they want—and to bring their optimism, curiosity, and hard work to create the world they wish to live in. Only then will the field begin to resemble the people it is capable of helping.


Tara Chklovski is the CEO and founder of Technovation.