The primary goal of this project is to enhance student engagement and participation in the learning environment, especially those who have may have been marginalized by conventional approaches to teaching. This project aims to support, encourage, and train faculty to incorporate compassionate pedagogy (CP) and experiential learning (EL) into their teaching, with the goal of promoting student connection and faculty creativity.

Within social work curriculum, the topic of disability is either explicitly absent or medicalized.  The lack of a rich understanding of disability as a cultural experience that intersects with other cultural experiences is concerning given our ethical guidelines of cultural competence and equity.  Additionally, not only are students excluded via this omission, but so are faculty, staff, and social workers working in the field.  Approximately a quarter of the population identifies as having a disability, yet our curriculum barely acknowledges their experiences.

The Computer Science department provides CS classes to the whole university via our Computer Science "Elements" program. The Elements sequence can be started by any student in any major with no prerequisites; later Elements courses only have prerequisites from earlier Elements courses. Some students choose to take just 1 or 2 CS classes in order to learn some programming, and others choose to take 18 hours in order to earn the Elements of Computing Certificate. The CS Elements program serves a very diverse population of students, in terms of backgrounds, majors, and goals.

Student evaluations of teaching (SETs) are one of the official means to gauge the performance and effectiveness of an instructor at higher-education institutions. They are often used for promotion/tenure decisions as well as to guide faculty into becoming effective instructors. But the question is whether, as currently implemented at UT Austin, SETs are reliable indicators of (a) effective teaching, and (b) long-lasting, impactful learning?

The challenges facing modern engineers are more complex than ever. Current engineering issues such as
algorithmic bias, data security and privacy, and sustainability do not only require technically skilled engineers,
but also engineers who are conscious about structural inequities and biases.