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ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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AUTOMATING THE ASSESSMENT OF TECHNICAL WRITING SKILLS VIA AI-POWERED TOOLS IN IT EDUCATION

Authors: Adila Tadjibayeva; Zilola Yahyoqulova; Worldly Knowledge Publishing Centre;

AUTOMATING THE ASSESSMENT OF TECHNICAL WRITING SKILLS VIA AI-POWERED TOOLS IN IT EDUCATION

Abstract

This article investigates the potential of AI-powered tools for automating the assessment of technical writing skills in IT education. The study reviews current developments in automated writing assessment, identifies key competencies required for technical communication in computing disciplines, and proposes an AI-based framework for evaluating student submissions. The framework integrates NLP techniques, machine learning algorithms, and rubric-based assessment methods to evaluate grammar, coherence, structure, technical accuracy, and readability. The article further discusses the reliability, validity, and educational implications of automated assessment systems. The findings suggest that AI-powered assessment can significantly reduce instructor workload, provide timely feedback, and support personalized learning experiences while maintaining acceptable levels of assessment accuracy. However, challenges related to transparency, fairness, and ethical implementation remain important considerations for educational institutions.

Keywords

artificial intelligence, automated writing assessment, technical writing, IT education, natural language processing, educational technology, large language models.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average