Classically, Fitts’ law has been used to estimate the time required to point at targets like icons and hyperlinks. This enables the design of graphical user interfaces (GUIs) that minimize task time. In recent years, the focus has shifted to be more inclusive of not just speed, but also how accurately users can perform tasks. Much of my work has addressed this, leading to several published papers on models for estimating the success rate of tapping targets on smartphone screens. Furthermore, based on one of these models, I have publicly released design-facilitation tools that can estimate tap success rates for web pages and application interfaces. A significant recent innovation I am watching closely is the application of more advanced machine learning methods, such as Bayesian hierarchical modeling and reinforcement learning. I have particularly high expectations for reinforcement learning as a tool from an industrial perspective. This is because reinforcement learning allows us to create agents that can operate a GUI and perform a multitude of tasks at scale. For example, evaluating a pre-release app’s interface currently requires costly user testing. If we could instead have multiple agents with different characteristics, such as simulating users of different ages or operating the interface to discover areas for improvement, it would become an incredibly powerful tool for companies.
Fitts' Law is something we have talked about in class. It says that close and large objects are easier to interact with, and results in the user taking less time on the website. It is very interesting to see this law being applied in the real world. It is expanded here to not only speed, but accuracy. Machine learning is getting faster and more accurate. This will drasctically change business operations, as agents can take over testing and further enchance the product. Overall, there is still so much potential in human-computer interaction.