Transforming Assessment with Self-Correcting AI Pipelines: Towards Adaptive and Validated LMS Practices

Sergiy Yevseyev

doi:10.29207/resti.v10i4.7191

Authors

Sergiy Yevseyev Ivano-Frankivsk National Technical University of Oil and Gas

DOI:

https://doi.org/10.29207/resti.v10i4.7191

Keywords:

dynamic tracking, flexible verification, distance education, knowledge testing, AI-driven assessment development

Abstract

The purpose of the study was to develop an effective approach to automated generation and validation of educational assessment materials in the context of distance learning using Large Language Models (LLM). While current research often overlooks the dynamic transformation of evaluation practices using sequential AI applications, this study addresses this gap by introducing a novel self-correcting AI pipeline that iteratively refines and validates questions based on structured feedback. As part of the study, an experimental methodology combined qualitative and quantitative methods, including computer modelling. Researchers conducted a comparative analysis of the results from three different LLMs: ChatGPT-4o, Google Gemini PRO 1.5, and Claude 3.5 Sonnet. Finally, the generated questions were validated based on pedagogical expediency, age compliance, and Bloom’s taxonomy. To test the effectiveness of the approach, three stages of research were conducted. In the first stage, it was found that only 23% of questions generated without updated prompts met the stated criteria. In the second stage, after introducing clarifications to the prompts, this indicator increased to 63%. The highest results were achieved in the third stage, where an iterative hint refinement model using structured feedback was implemented: Claude 3.5 Sonnet achieved 92% of valid questions with the minimum number of clarifications (16), ChatGPT-4o 80% (40 clarifications), and Gemini 72%, but with the highest number of corrections (108). The process included question generation, answer verification, external validation, and iterative correction. The findings showed that effective AI-based development of multi-level computer science questions requires high-quality language models, clear instructions, and automated verification of cognitive level. The proposed algorithm enables educational institutions, platform developers, and teachers to generate assessment materials adapted to students’ age and knowledge levels in distance or blended learning

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