SciPy 2025

Generative AI has rapidly changed the landscape of computing and data education. Many learners are utilizing generative AI to assist in learning, so what should educators do to address the opportunities, risks, and potential for their use? The goal of this open discussion session is to bring together community members to unravel these pressing questions in order to not only improve learning outcomes in a variety of diverse contexts: not only students learning in a classroom setting, but also ed-tech or generative AI designers developing new user experiences that aim to improve human capacities, and even scientists interested in learning best practices for communicating results to stakeholders or creating learning materials for colleagues. The open discussion will include ample opportunity for community members to network with each other and build connections after the conference.

In classroom contexts, code interviews are a promising oral assessment of student learning outcomes on take-home programming assignments. Oral assessments allow instructors to observe students’ communication skills, problem-solving processes, and critical thinking abilities in a more interactive and dynamic setting. However, stressfulness of the assessment and inequities for students with diverse abilities and fluency with verbal communication. We propose a more equitable approach to code interviews designed to reduce stress and increase accessibility. We achieve this by introducing concept-check exit tickets and group activities with peers prior to the interview, and additional time to read the question during the interview. When students are given more time to internalize course concepts, they are able to plan their answers in advance and better prepare for technical communication. While these elements were introduced specifically for the classroom, we believe that they can also be generalized to technical and professional interviews.