Integrating ChatGPT into Biochemistry Education: A Practical Guide to Developing Interactive Learning Applications

To improve the accessibility and ease of use of rigorously derived enzyme-kinetics routines for learners with limited programming experience, we developed a graphical, web-based Michaelis-Menten and inhibition simulator in Python using Streamlit. The interface was assembled through a human-in-the-loop workflow in which concise, goal-directed prompts to ChatGPT yielded small, behavior-preserving code patches via an error-message feedback loop, while all quantitative results were computed by audited, deterministic functions rather than the language model. This approach enabled students and instructors without extensive coding backgrounds to explore substrate saturation, inhibition classes, and linearized diagnostics within a stable, classroom-ready environment. In addition to interface scaffolding, ChatGPT supported practical tasks such as environment configuration, Streamlit setup, and brief, equation-aware explanatory text aligned with course materials. The work serves as a template for undergraduate and postgraduate courses in biochemistry and chemical education, demonstrating how conversational assistance can lower the barrier to creating domain-specific teaching tools without requiring advanced software training. Sample lesson scaffolds illustrate undergraduate activities on varying substrate and inhibitor concentrations and graduate exercises involving Lineweaver-Burk or Eadie-Hofstee analysis and parameter interpretation.