Socioeconomic status (SES) plays a significant role with education and learning, thus I think it should play a critical role in learner analysis. It can impact other demographic characteristics in this list including, Internet connectivity, access to technology, employment status, educational/grade level, etc. Numerous studies have correlated low SES levels with lower learning performance, low self-efficacy, stress, underrepresented backgrounds, and learning disabilities.

We're seeing a significant disparity in SES when it comes to STEM fields. In fact, one article/study I've seen cites low SES status as the biggest barrier to STEM.

I found this sentence and the next personally meaningful especially with the work that I am involved with in PracticumAI.In fact, (I read this whole section many times and thinking about it!)

With Practicum AI, the majority of learners are from STEM research fields, however we are seeking a wider audience especially from the arts and humanities. Additionally, we have a diverse group across different sociocultural backgrounds.

Our approach to instruction and learning is with the use of 3 features- visual, story-driven, and hands-on learning. By using visuals and narratives in our presentations for conceptual understanding, we need to be mindful of these contextual factors.

Side note: I am also thinking that this is one way of how bias is introduced into AI, that is, within the way we teach AI (yet that is probably up for a different class and discussion).