Evidence-based changes to instruction can lead to better student outcomes and performance. For this reason, instructors are interested in collecting data about student experience and outcomes. However, the process of data collection and analysis can be time-intensive for instructors, making it challenging to gather data necessary to improve their classes. Moreover, when data collection does happen, it often centers quantitative data, but this systematically devalues students' experiences. Therefore, we sought to develop a tool that aids instructors in processing and analyzing qualitative data pertaining to students’ class experiences. As part of our research project, we have developed an R-shiny based data processing app that integrates Artificial Intelligence to summarize findings from open response questions on student surveys. This tool is intended to alleviate the time-intensive nature of analyzing student responses to open-ended survey questions. In order to validate the accuracy of AI-generated summaries, we compared them to manual summaries generated using in vivo qualitative coding methods. We find that our app generates responses that are comparable to the manually generated summaries. The summaries include prominent themes along with details about student experiences within those themes. With this tool, instructors can gather real-time data, even in large classes, eliminating the concern of the time-intensive process of manually reviewing responses. By developing this tool, we hope to empower instructors to explore diverse questions that provide them with valuable insights on how to optimize the structure of their classes to improve student experience and outcomes.

General anesthesia (GA) is administered as a sedative in nearly 60,000 surgeries daily in the United States. Yet, there is a very limited understanding about how GA impacts brain activity, leading to induced loss of consciousness and pain sensation. Preliminary work in the Heshmati lab has highlighted key subcortical structures that are engaged during anesthesia, but it remains unclear how activity in these regions and across the brain regulates awareness or pain sensation as anesthesia is induced (“induction”), maintained at a steady state (“maintenance”) and removed (“emergence”), as is done during surgeries. My work aims to identify the neural circuits that regulate the loss of consciousness and pain sensation during GA by recording local field potentials (LFP) from mice as they undergo volatile anesthetic isoflurane (ISO). During LFP recordings, I will insert small electrodes into highlighted regions of interest, to capture low-frequency extracellular voltage signals generated by the synchronized activity of nearby neural populations during the three periods of interest: induction, maintenance, and emergence from isoflurane GA. I will analyze the amplitude fluctuations and frequency patterns to identify synchronized oscillations within subregions and assess the level of synchrony, or coherence, across different regions. Given previous findings on the shared and opposed involvement of subcortical regions in pain and anesthesia, I expect to observe coherence among some of the regions, such as the amygdala and hypothalamus, but potentially anti-correlation within specific subsections, such as central vs. basolateral amygdala. Through these experiments, I will be able to monitor the effects of isoflurane anesthesia through a temporally-defined electrophysiological lens, capturing real-time activation dynamics of large neural populations across induction and recovery from anesthesia. Thus, my research aims to further develop our understanding of the brain under GA, by providing novel insight into the neural circuits regulating wakefulness and pain during surgical procedures.