ATLAS Colloquium: Designing Computational Systems for Learning and Inclusion by Chinmay Kulkarni

Enabled by the internet, and accelerated by the pandemic, the future of work is already here. Today, we collaborate with distant colleagues we have never met in person, and employers rely on online labor platforms to find freelancers around the world. At the same time, computational work environments largely lack informal social interactions. Consequently, workers struggle to build rapport with colleagues, collaboration networks are siloed, and employers struggle to even evaluate potential workers. Based on my research that has resulted in tools that have helped millions of learners in massive online classes (MOOCs), I argue for a new approach to build computational work environments. Specifically, I show that combining findings from behavioral sciences with computational techniques can create social interactions that scaffold learning and weave these interactions into the fabric of work. In this talk, I demonstrate this approach with systems that help people learn ambiguous skills, foster an environment that welcomes diverse viewpoints to help teams make better decisions, and allow a more inclusive range of employers to benefit from this future of work. Together, these systems point to a future where computing can create work environments that support learning and inclusion better than traditional work possibly could.
Chinmay Kulkarni is an Associate Professor in Human-Computer Interaction at Carnegie Mellon University, whose research introduces technology for scaling education and online work. His lab has created systems that scale feedback and assessment to thousands of learners in massive online classes, systems that extend peer feedback to work contexts where competition may prevent honest feedback, and systems for learning how to adapt to new forms of work. More than 50,000 learners have directly benefited from these systems, and companies as varied as Coursera, Mozilla, and Instagram have adopted the related research findings, benefiting millions more. His lab is also developing community-based design approaches that can also yield scalable socio-technical solutions while still resisting the impulse to position certain community needs as edge cases. This research is currently supported by the NSF, the US Department of Education, and the Office of Naval Research. Past research sponsors include Mozilla and Instagram. Before coming to Carnegie Mellon, he earned a PhD from Stanford's Computer Science Department.
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