By: Emily Durning
My previous piece on the Equity and Access in Algorithms, Mechanics, and Optimization (EAAMO) Conference gave an overview of my experience at the conference as a part of the group exploring Social Indicators in the Maker Space Sessions. During these conversations, and when discussing the conference after the fact, every road led back to the small wins idea, and it was natural to begin applying this theme to other projects within RDS@Pitt.
What exactly is meant when I refer to the small wins theme? The idea stems from our conversations around the questions: what are social indicators of progress in social impact research, how recognized are these social indicators, how reliable are they, and how they are measured/quantified. Small wins are easily recognizable when you are close to the project, seeing little bits of progress as each day passes. An example given in our synthesis session was having a reliable phone number for someone who previously was not always reachable. There is security in knowing that a person now has a way to be connected to those around them should they need help and that they can be found if need be. This small win can be indicated by a lack of responses when they are called by some sort of social services, followed by a period of them being responsive. This is an example of an indicator that may be easy to miss, but regardless shows signs of improvement and promise for the future.
There are so many kinds of small wins, and they prevent a completely black and white outlook. Another situation where this concept is applicable is the use of Generative AI, a topic that can become polarizing. In the case of using GenAI within the classroom, especially in the first few years of tools such as ChatGPT, there was one main road taken by almost every professor. This strategy was a complete restriction of these tools in any way shape or form. As you can guess, this policy is extremely difficult to enforce, and often not practical.
What are small wins in this context? First, to identify small wins, it can be useful to identify a goal, and then break down how to get there. Our goal is to enforce the idea that GenAI tools are here to stay, and it is more beneficial to students to adapt to their presence rather than not address them at all. We want there to be an understanding of how these tools can and should be used, while also recognizing their limits and preserving the student’s learning. A small win that would work towards this goal could be re-evaluating the AI clause included in a class syllabus every semester or every school year. This means that as tools are developed and released, the classroom rules will be able to apply. Another small win is a professor sharing how they use GenAI with their students. This opens up room for an honest conversation about how AI can help increase efficiency or help us learn. Small wins are going to vary depending on class subject, class size, and more. Find what goals suit you and your environment, and use small wins to continue working towards them.