Study of the Impact of Prompt Engineering on the Quality of Generated Content for Educational Programs
Year: 2025
Type: article
Abstract: This study analyzes how prompt engineering affects the quality of language model–generated content for educational programs. It evaluates various prompting methods across modern models, from compact to large-scale, using a dataset of 8,269 programs. A structural- semantic gradation of components by interpretability was identified: goals, disciplines, and learning outcomes were highly interpretable (high cosine similarity), while components like the Atlas of the profession were less so. Prompting methods showed varying effectiveness: few-shot learning worked best for goals, and chain of thought for professional standards. A key finding is the "specialized advantage": a LoRA-adapted model outperformed larger, non-specialized ones, challenging the idea that bigger always means better. The study offers practical strategies for selecting models and prompts, aiding educational analytics and future research into AI-based interpretation of complex educational content
Source: Bulletin of D Serikbayev EKTU
Authors: Ayanbek Serikov, Andrii Biloshchytskyi
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4414858992
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