Congratulations to all of our presenters for their excellent work!
]]>Rain was awarded the Biochemistry and Molecular Biology Junior Research Award, which is given to a BMB major with outstanding research performance and dedication. Rain was honored for her tenacity and progress in research.
Chris was awarded the Clarence Denoon Award, which is given to a rising senior for excellence in the natural sciences. Chris was awarded due to his progress in lab as well as his outstanding performance in the classroom.
]]>In this piece, I discuss how I’ve used an opioids case study to teach introductory cell and molecular biology.
I am a big fan of case studies, and I’m happy to chat with anyone interested in learning more!
]]>Ismar presented both a lightning talk in a session entitled “Leveraging AI for understanding and designing protein folding and function” as well as a poster.
]]>There is a lot of anxiety that genAI tools will jeopardize learning gains from homework assignments. Using Bloom’s Taxonomy, we analyzed where genAI succeeds and where it struggles.
We compared the performance of ChatGPT-4o and Harvard PhD students in designing molecular biology experiments. Initially, we predicted that as you move higher in Bloom’s Taxonomy, ChatGPT would perform worse. However, that is not what we observed.
The main difference we observed in performance was at the “apply” level of Bloom’s. While students (mean = 87%) performed two letter grades better than ChatGPT (mean = 67%) on experimental design worksheets, all of that difference can be explained by a difference in performance at the “apply” level. We posit that apply-level questions require multi-step, compositional reasoning, which large language models are not well-suited for. Therefore, this finding is likely broadly applicable across LLMs.
At the “create” level, genAI consistently generated reasonable hypotheses for given scenarios. However, the hypotheses often lacked the specificity and testability we would expect of PhD-level work.
We also observed that ChatGPT-4o struggled to interpret data presented graphically. Anecdotally, ChatGPT-5.4-Thinking has dramatically improved at data interpretation. This is one of many examples emphasizing how educators must be mindful of the rapidly developing genAI landscape.
We offer a few suggestions to educators. One that I’ve used in my teaching is increasing my expectation for precision in students’ answers. GenAI is great at producing reasonable-sounding answers, but precision can be lacking. Use rubrics that reward careful justification for answers.
The class we describe in the paper uses “chalk talks” as a key assessment. Oral exams are valuable assessment tools that maximize student ownership of their answers. The class is also an example of a “flipped classroom,” which gives frequent opportunities for formative assessments.
All in all, many examples of “good” pedagogy (e.g., active learning, flipped classrooms, universal design) are also beneficial for maintaining learning gains in the era of genAI.
]]>Kalynn was awarded the Willie M. Reams Award for Outstanding Achievement, one of the Biology Department’s highest honors. Kalynn was honored for her research efforts in the Richardson Lab, her outstanding performance in the classroom, and her contributions to the Peters Lab.
Chris was awarded the Biochemistry and Molecular Biology Sophomore Research Award. Chris was honored for his outstanding potential to perform research, based on his progress so far in the Peters Lab.
Congratulations to both Chris and Kalynn!
]]>The grant will be used to:
In addition, the grant provides support for the creation of a new course at the University of Richmond in advanced molecular biology.
Altogether, this grant will help train the next generation of scientists in America and will contribute to our understanding of how memories form.
]]>Peters JJ. 2024. An environmental justice fictional case study for engaging non-majors in human biology. J Microbiol Biol Educ. 0:e00005-24. https://doi.org/10.1128/jmbe.00005-24
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