The Prompt culture in Generative AI for Active Learning

Academic writing is an important part of scholarly development and a key opportunity to practice scientific, critical thinking. Among its types such as seminar papers, term papers, case studies, and project reports—essay writing stands out for its open-ended nature. These formats challenge students to apply their present knowledge to real-world or simulated scenarios, often without a clearly defined path to a solution. This ambiguity showcases professional environments, where complete information is rarely available. With the advent of AI systems like Gemini, Copilot and ChatGPT, the landscape of academic writing has shifted. Unlike traditional programming, these applications allow users to issue instructions in natural language, making them more accessible collaborators. However, to use them effectively, users must learn to craft clear and purposeful prompts. This paper introduces the BITE approach—Behavioral instructions, Interaction modeling, Task elaboration, and External context—as a practical framework for writing effective and uncomplicated prompts. Prompts in the GPT world are instructions in natural language. We do not have to learn programming-specific syntax to get the machine to do something but can speak to it more or less in the same way as we would speak to a human collaborator. The BITE method supports active learning by encouraging students to engage critically with AI applications/tools during their writing process. Preliminary testing show that applying BITE leads to more coherent and desirable outputs and improved writing efficiency compared to unstructured prompting strategies used in both academic and industry settings.