FOUR PROMPTING TECHNIQUES FOR ANALYTICAL WORK WITH TEACHING AND LEARNING RESOURCES
DOI:
https://doi.org/10.31110/fmo2026.v41i2-04Keywords:
large language models, prompting techniques, instructional materials, Deming cycle (PDCA), Role Prompting, Plan and-Solve, Chain-of-Verification, Self-RefineAbstract
Formulation of the problem. Teachers are increasingly using large language models (LLMs) to enhance their teaching and learning resources (TLRs). However, most of them have no formal training in artificial intelligence and therefore rely on unsystematic approaches. The prompting techniques described in research literature can activate specific capabilities of LLMs, making interactions more predictable and manageable. Yet most existing taxonomies classify these techniques according to the model’s internal logic, whereas a teacher’s work is typically organized around the type of textual task being addressed. This highlights the need for a more purposeful selection of prompting techniques that support analytical work with teaching and learning resources (TLRs).
Materials and methods. The research was conducted as an exploratory study, leading to the development of a practical toolkit for teachers. The study proceeded through three stages: a theoretical analysis and synthesis of the relevant literature, the development of a procedure for selecting appropriate prompting techniques, and pilot testing. The selection was based on the taxonomy by Schulhoff et al. (2025), which includes 58 textual prompting techniques.
Results. The study produced a practical toolkit featuring four prompting techniques: Role Prompting, Plan-and-Solve, Chain-of-Verification, and Self-Refine, organized according to the logic of the Deming cycle (Plan → Do → Check → Act). The toolkit is implemented through standardized prompt templates adapted to four dimensions of analytical work with TLRs: content, structural and logical organization, language and style, and evaluation. It can be used in two ways: a procedural mode, in which all four techniques are applied sequentially, and an autonomous mode, in which a teacher selects a single technique based on the specific task at hand.
Conclusion. The four selected techniques transform interaction with LLMs into a clear and well-defined set of steps. They provide teachers with a structured approach to analytical work with TLRs, regardless of their level of technical expertise. At the same time, any results generated by the model should always be critically evaluated by the user.
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Copyright (c) 2026 Таяна Дєордіца, Марина Вороніна, Раїса Гладушина, Ольга Єпіфанова, Олена Козьменко, Володимир Толмачов
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