Kangas To Co-Lead Development of Automated Science Educational Guidelines
By Aaron Aupperlee
ºÃÉ«ÏÈÉúTV's expertise in automated science education will serve as the foundation of the first-ever guidelines for teaching lab automation.
Joshua Kangas, an associate teaching professor in the Ray and Stephanie Lane Computational Biology Department in the School of Computer Science, will co-lead an effort to develop education standards for automated science through the Society for Laboratory Automation and Screening (SLAS). The work aims to address a growing gap between the rapid adoption of automation technologies, artificial intelligence and formal training pathways for the scientific workforce.
Automated science increasingly integrates robotics, machine learning and artificial intelligence across pharmaceutical, biotechnology, manufacturing and academic research settings. Despite this growth, automation training is often fragmented and institution-specific, creating challenges for educators and employers alike.
“Training in lab automation is kind of the Wild West,” Kangas said. “Very few universities have automation training as part of the curriculum. A lot of the training happens on the job.”
Kangas co-directs CMU’s Automated Science: Biological Experimentation master’s program, the first in the field of biology to focus on lab automation and artificial intelligence. There are no other programs where automated science is the full focus of the curriculum, Kangas said.
Kangas noted that some of the standards will match what ºÃÉ«ÏÈÉúTV is already teaching, and he expects the master’s program curriculum will be a valuable starting point for various aspects of the standards.
The project, Standards for Automated Science Education, received a nearly $200,000 multiyear grant from the Alfred P. Sloan Foundation to establish evidence-based, interdisciplinary guidelines to help educators prepare students for the technical competencies required in modern laboratory environments. Kangas will work with Kennedy McDaniel Bae, executive director of The Glassfloor, which develops education and training programs for the scientific workforce. The two will collaborate with a diverse multinational expert drafting committee drawn from academia, industry and professional networks including SLAS, the University of Toronto's Acceleration Consortium and others.
“The automation infrastructure investments have been massive, but training infrastructure hasn't kept pace,” Bae said. “We're building that missing piece — the first comprehensive framework that bridges traditional education with the convergence of robotics, AI and machine learning.”
Work has already begun with a pair of surveys. One survey went to educators in automated science to determine the current state of automated science education, something that has never been done before, Kangas said. The other survey went to people in industry to determine what skills they expect from graduates of automated science programs.
“Ultimately, we will design a framework to clarify the meaning of various competencies that allow educators to teach them, students to assess their skills and plan their careers, and teachers to have specific guidelines for their courses,” Kangas said.
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