Chatbot responses suggest that hypothetical biology questions are harder than realistic ones.

The biology education literature includes compelling assertions that unfamiliar problems are especially useful for revealing students' true understanding of biology. However, there is only limited evidence that such novel problems have different cognitive requirements than more familiar problems. Here, we sought additional evidence by using chatbots based on large language models as models of biology students. For human physiology and cell biology, we developed sets of realistic and hypothetical problems matched to the same lesson learning objectives (LLOs). Problems were considered hypothetical if (i) known biological entities (molecules and organs) were given atypical or counterfactual properties (redefinition) or (ii) fictitious biological entities were introduced (invention). Several chatbots scored significantly worse on hypothetical problems than on realistic problems, with scores declining by an average of 13%. Among hypothetical questions, redefinition questions appeared especially difficult, with many chatbots scoring as if guessing randomly. These results suggest that, for a given LLO, hypothetical problems may have different cognitive demands than realistic problems and may more accurately reveal students' ability to apply biology core concepts to diverse contexts. The Test Question Templates (TQT) framework, which explicitly connects LLOs with examples of assessment questions, can help educators generate problems that are challenging (due to their novelty), yet fair (due to their alignment with pre-specified LLOs). Finally, ChatGPT's rapid improvement toward expert-level answers suggests that future educators cannot reasonably expect to ignore or outwit chatbots but must do what we can to make assessments fair and equitable.

Three Steps to Adapt Case Studies for Synchronous and Asynchronous Online Learning.

Pandemic SARS-CoV-2 has ushered in a renewed interest in science along with rapid changes to educational modalities. While technology provides a variety of ways to convey learning resources, the incorporation of alternate modalities can be intimidating for those designing curricula. We propose strategies to permit rapid adaptation of curricula to achieve learning in synchronous, asynchronous, or hybrid learning environments. Case studies are a way to engage students in realistic scenarios that contextualize concepts and highlight applications in the life sciences. While case studies are commonly available and adaptable to course goals, the practical considerations of how to deliver and assess cases in online and blended environments can instill panic. Here we review existing resources and our collective experiences creating, adapting, and assessing case materials across different modalities. We discuss the benefits of using case studies and provide tips for implementation. Further, we describe functional examples of a three-step process to prepare cases with defined outcomes for individual student preparation, collaborative learning, and individual student synthesis to create an inclusive learning experience, whether in a traditional or remote learning environment.

Applications of microarrays with toxicologically relevant genes (tox genes) for the evaluation of chemical toxicants in Sprague Dawley rats in vivo and human hepatocytes in vitro.

Microarrays with toxicologically relevant genes (tox genes) have been developed in our laboratory for toxicogenomics studies in rat, dog and man. The genes were chosen using published information as well as a discovery process for genes responsive to toxic treatments using transcription profiling experiments conducted with rats and dogs. In addition to published information human tox genes were derived from rat tox genes based on gene homology. Using the microarray with rat-specific tox genes, a database containing gene expression, histopathology, and clinical chemistry findings has been generated for 89 compounds. Analysis of the database indicates that treatment with toxic compounds induces specific gene expression patterns. Dose- and time-dependent response relationships in gene expression were observed for treatment with toxic compounds. Gene expression at 24h was found to correlate well with organ toxicity observed at 72 h. Mining of the database led to the selection of specific groups of genes (predictive gene sets) whose expression patterns are predictive of organ toxicity with a high degree of accuracy (approximately 90%). The data also provide insight on toxic mechanism and gene regulation pathways. For instance, carbon tetrachloride and chloroform treatments were found to decrease the expression of the cytochrome P450 isoform 3A1 gene while enhancing the expression of the multiple drug resistance gene MDR1 in liver, clearly demonstrating that the CYP3A1 and MDR1 genes were not co-regulated as postulated by some researchers. This approach, the use of gene expression as an endpoint to define organ toxicity, is extended to the definition of human drug toxicity using primary human hepatocytes as a test system. Preliminary results demonstrate that the toxic drug, troglitazone, can be clearly distinguished from the less toxic analogues, rosiglitazone and pioglitazone based on their effects on tox gene expression in human hepatocytes. Our results with both rats in vivo and human hepatocytes in vitro suggest that microarrays with toxicologically relevant genes can be used routinely for the evaluation of chemical toxicity.