Social science is the attempt to understand how our interactions with individuals, groups, and society as a whole inform our knowledge, role, and actions and is built using elements of psychology, sociology, political science, economics, and anthropology (Rosenberg, 1988). By incorporating elements from all of these different fields, social science becomes difficult to define, describe, and research using a singular or universally accepted methodology. In general, the goal of science is to understand the world around us through observation and experimentation. Still, social science, which takes root in many other fields, means our observation lens may yield very different results. The field of learning is very much rooted in social science as we attempt to help people know and understand, but again, our definition of what it means to know and understand cannot be defined in a manner that is universally accepted. This leads to new learning theories and assessment practices that are developed every year and continually expand what it means to know and understand. While we do not have the capabilities to know exactly what others are thinking, how they process information, and how they apply that information, we are undeterred in our quest to master our ability to help others know and understand.

Looking back on this past semester, I see that our ability to research and assess what others know is vast, depending on the lens we use for our research. Unlike physics or mathematics, there is no unified theory that applies to how we learn, which is why, as researchers, we recognize the limitations of our studies and understanding. We are, however, entering an exciting time where it will become much easier to discover connections between our findings and other research theories with the emergence of Artificial Intelligence (AI). The ability of AI to analyze data and make connections will enhance how we process data and help us determine insights we might have previously missed due to our narrow lens of focus. Initial breakthroughs in this area will be limited to quantitative data, but as AI improves, its ability to use qualitative data will soon come into play more frequently. As qualitative data is generally analyzed by researchers using a specific lens, AI will be able to apply that lens, potentially leading to many breakthroughs in how we understand learning. While I do not see the ability to use AI in our analysis leading to a unified theory of learning, I do see learning theories becoming much more complex. This increased understanding and the ability of AI to help support learning design will lead to more supportive and expansive learning environments that will be able to support learners like never before. The possibility of learners being left behind will become more difficult as the social science of learning grows by leaps and bounds. It is an exciting time to be a learning practitioner and researcher as we prepare for a world where our capabilities and understanding do not limit us.

Rosenberg, A. (1988). Philosophy of social science (Vol. 2). Boulder, CO: Westview Press.