Building and fostering computational thinking skills is a major priority within the Robomatter curriculum. Computational thinking is a problem-solving process more akin to the scientific method than to education catch phrases like active processing, critical thinking, and resourcefulness that can be found in other frameworks. Of course, computational thinking does require active processing, critical thinking, and resourcefulness. But computational thinking is a skilled process by which students seek out and consider problems, organize the information that they have about the problem, test multiple algorithmic solutions for errors and/or refinements, and utilize available tools and resources in ways that enhance all parts of that development process. By looking at computational thinking this way, we can see its relationship to STEM (science, technology, engineering, and mathematics.)
In an integrated STEM classroom, teachers and students work collaboratively to make connections between STEM subjects in a real-world context. The Next Generation Science Standards (NRC 2012) suggest that a closer study of and application of science and engineering practices may help to provide schools with a framework for an integrated STEM classroom.
This real-world context is key. Real-world scientists and engineers use computational thinking to engage with and solve many of the problems and issues that our world faces today. Simply, if teachers are not integrating computational thinking while asking students to apply their knowledge of STEM subjects to science and engineering practices, then they are not being true to the nature in which science, technology, engineering and math are applied by the people who create the innovation in our world today.
An example of real-world science and computational thinking comes from the University of Toronto, where a group of researchers created algorithms that can generate 3D structures of tiny protein molecules. These algorithms have the potential to fundamentally change the drug therapies that are developed for a range of diseases.
” Designing successful drugs is like solving a puzzle, says U of T PhD student Ali Punjani, who helped develop the algorithms. Without knowing the three-dimensional shape of a protein, it would be like trying to solve that puzzle with a blindfold on. The ability to determine the 3D atomic structure of protein molecules is critical in understanding how they work and how they will respond to drug therapies, notes Punjani. 
The combining of computational thinking and science has given rise to new fields of research, like bioinformatics. Bioinformatics combines mathematics, statistics, and computer science to study biological molecules and create tools for understanding biological data. This understanding can lead to, for example, new insights into the genetic basis of diseases.
Similar to bioinformatics is the field of computational genetics. Computational genetics studies the structure of genomes with the aid of computational and statistical analysis.
The pace of innovation due to computation is staggering. Many students today will work in fields that have not even been created yet. In light of this, Robomatter’s curriculum doesn’t focus on tools; Robomatter’s curriculum focuses on elevating students from a surface level to a conceptual understanding of computational thinking. Computational thinking is not only embedded in real-world math and science; it is also changing what it means to be literate in math and science. Therefore, an integrated STEM curriculum that includes computational thinking is essential for all students.
 “New algorithms may revolutionize drug discoveries-and our understanding of life.” Phys.org – News and Articles on Science and Technology. N.p., 6 Feb. 2017. Web.