Schema Study: A Large Language Model (LLM) Application for Asynchronous Student Learning and Inquiry

Undergraduate biology educators face a critical challenge: providing immediate, personalized formative feedback to increasingly large, diverse classes. Large Language Models (LLMs) offer potential solutions, but open-ended chat interfaces pose challenges including curricular misalignment and equity gaps. We developed Schema Study, a free, no-code, open-source web application where instructors upload course terms and context via a single spreadsheet to create an AI-powered chatbot. Our LLM tutor uses evidence-based teaching practices and Socratic questioning to deepen understanding, correct misconceptions, and encourage students to find connections among course concepts. During Winter 2025, we integrated Schema Study into an introductory biology course, embedding it within structured assignments and updating content weekly. Pre- and post-surveys (N=225) indicated strong student satisfaction; 72% would reuse Schema Study in future biology courses. Each additional day per week students used Schema Study more than doubled the likelihood they would recommend it. Schema Study enhanced students' AI self-efficacy and their belief that AI is relevant to their education and careers. Through iterative, classroom-based refinement, we updated the application based on student feedback, highlighting best practices for integrating LLM chatbots: clear structured messaging, AI literacy training, curricular alignment, and scaffolded active learning opportunities. The tool provides formative practice through question-led dialogue; independent performance is evaluated in secure assessments outside the app. Schema Study offers a scalable, accessible strategy for biology educators to leverage generative AI's benefits while mitigating its risks. Primary Image: A student studying on their computer at home. Image generated by OpenAI GPT-4o. Accessed May 04, 2025.