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Technological tools, like virtual assistants (aka chatbots), have been ubiquitous in people’s day to day. The challenge becomes how educators leverage digital omnipresence to benefit the learning environment. Using a curated chatbot allows educators to reach more students with instructor-approved information, particularly in large classrooms. Students can receive direct responses and guidance towards course materials, and educators may have less to manage by automating routine queries to a chatbot. Data from the 293 collected logs from 232 unique student users provide insight into the information students are interested in when tasked to complete an essay assignment contextualizing chemistry through a sustainability lens. Using process mining to show how students seek information, the extracted 5185 events from the logs created 204 unique pathways from students’ actions in the curated chatbot. Additional text mining was done on the 116 freeform queries students typed into the curated chatbot. Results from both analyses showed that students were primarily sought information on the sustainability context of the writing assignment in their queries and that the curated chatbot can provide personalized assistance, responding to students’ unique pathways of seeking help. A selection of subsets of student users’ chatbot interactions, limitations of the study, and extension of the curated chatbot use in other classroom tasks and settings were discussed. 

In a technology-centric world, leveraging digital tools such as chatbots allows educators to engage students in ways that may be more accessible for both parties, particularly in large lecture classrooms. This report details the development of an interactive web-based chatbot to curate content for writing about chemistry in context. Students were assigned a 500-word paper where they discuss general chemistry concepts through the lens of a timely, sustainability-related topic, i.e., water footprint, carbon footprint, or embodied carbon. Discussed herein are the development of the decision tree, the chatbot’s components, and results from the initial implementation in a large lecture general chemistry classroom. Over 78% of the 347 enrolled students (271) used the chatbot over 350 times in the 3 weeks leading up to the assigned due date of the paper. Eighty-three percent of the interactions were captured for further analysis, which showed that 22% of students used the chatbot more than once. Forty-six percent of recorded interactions were used to aid students in developing or refining their idea for the assignment. The curated chatbot technology reported here for writing assignments in chemistry can be readily adapted to other aspects of coursework in chemistry.