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This work-in-progress study describes our grant-funded efforts in developing a computer science faculty learning community (FLC) across six California state institutions. With an emphasis on socially responsible computing (SRC), the faculty development effort that prepares faculty for SRC lesson implementation has integrated social scientists with computer science faculty in the rotating leadership team. It works collaboratively to facilitate dialog around experiences of implementing lessons that focus on social justice and ethical decision-making. Our data-driven FLC and course transformation effort was initiated by finding that retention rates in early computing courses at participating institutions were inequitable across demographic groups. The ultimate goal of the Broadening Participation in Computing Alliance for Socially Responsible Computing is to improve the retention rates of LatinX students by increasing their sense of belonging to the field of computer science[1] through deliberate and intentional connections of curriculum to real-world problems and social issues. For this paper, we focused on the faculty experiences of our most recent summer workshop and our reflection on the FLC implementation process. We present our faculty survey data from June 2024 and introduce reflective focus group findings [2], providing conjectures about the effectiveness of our approach. In the discussion, we build recommendations for collaborative professional development of faculty and discuss next steps. 

ABSTRACT Biophysics is an interdisciplinary pursuit requiring researchers with knowledge and skills in several areas. Optical instruments and computers are fundamental tools in biophysics research to collect and analyze data. We developed a 1-semester Optical Engineering Laboratory course to teach image processing, optical engineering, and research skills to undergraduate students majoring in biology and biochemistry. With the use of development systems on students' laptops and in the cloud, students learned image processing with Python and OpenCV. Each student constructed a microprocessor-based lensless holographic microscope, gaining hands-on experience with optical engineering. The class culminated in original, student-designed research projects. All lectures, hands-on labs, and student research projects were performed both in person and remotely, in response to the COVID-19 pandemic. 

Science students increasingly need programming and data science skills to be competitive in the modern workforce. However, at our university (San Francisco State University), until recently, almost no biology, biochemistry, and chemistry students (from here bio/chem students) completed a minor in computer science. To change this, a new minor in computing applications, which is informally known as the Promoting Inclusivity in Computing (PINC) minor, was established in 2016. Here, we present the lessons we learned from our experience in a set of 10 rules. The first 3 rules focus on setting up the program so that it interests students in biology, chemistry, and biochemistry. Rules 4 through 8 focus on how the classes of the program are taught to make them interesting for our students and to provide the students with the support they need. The last 2 rules are about what happens “behind the scenes” of running a program with many people from several departments involved. 

In order to achieve effective human-AI collaboration, it is necessary for an AI agent to align its behavior with the human's expectations. When the agent generates a task plan without such considerations, it may often result in inexplicable behavior from the human's point of view. This may have serious implications for the human, from increased cognitive load to more serious concerns of safety around the physical agent. In this work, we present an approach to generate explicable behavior by minimizing the distance between the agent's plan and the plan expected by the human. To this end, we learn a mapping between plan distances (distances between expected and agent plans) and human's plan scoring scheme. The plan generation process uses this learned model as a heuristic. We demonstrate the effectiveness of our approach in a delivery robot domain.