Implementing a Course-Specific Chatbot in Undergraduate Biochemistry

Abstract

Generative artificial intelligence (AI) tools are increasingly available to students, but their educational value depends on how they are configured, constrained, and integrated into course practices. This technology report describes a no-code workflow for implementing a course-specific chatbot in upper-division undergraduate biochemistry using a custom GPT configured with instructor-generated lecture slides, class transcripts, handouts, syllabus information, and custom instructions. The workflow emphasized course alignment, frequent updating, instructor validation, assessment scoping, and student prompt guidance. The chatbot was used in biochemistry I lecture across two consecutive fall offerings and extended to biochemistry II lecture for follow-up use. Student survey responses indicated substantial adoption and generally positive perceptions; peer-led supplemental instruction attendance and course-grade data are presented only as contextual information. The report highlights a practical, transferable approach for using no-code custom GPT tools to extend course-aligned study support while preserving instructor oversight and existing academic support structures.