Development of a smart textbook in informatics with interactive learning support and Kazakh language integration based on artificial intelligence technologies

2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.53894/ijirss.v8i3.7020
Data confidence 85 high
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

This article presents a project for developing interactive models of an educational resource for determining the semantic proximity between the ontology of knowledge extracted from a knowledge base based on a given question and the ontology generated from a student's answer in computer science. A general methodology for developing software has been proposed. This methodology will form the basis for the automated creation of smart textbooks on computer science in the Kazakh language using ontological models and thesauri. These educational resource models are suitable for any type of educational process (Blended Learning Technology (BLT), full-time, part-time). For example, the article describes an online smart textbook that will adapt to the student's individual learning path by providing personalized text, audio, and video materials, asking questions, and evaluating answers with an indication of percentage accuracy. A pedagogical experiment has been conducted to assess the students' performance. The online smart textbook will adapt to the student's learning style by providing personalized text, audio, and video materials. It will also ask questions and evaluate answers with an indication of percentage accuracy. In addition, completed assignments were analyzed to assess students' progress using static digital materials and standard computer testing systems for a dynamic, intelligent learning environment and knowledge assessment. The results show that the proposed methodology has great potential for increasing student engagement in studying the formalization and processing of the grammar of the Kazakh language using production rules and, based on them, developing a grammar processor, creating ontologies, thesauri, and knowledge bases on the content of “Computer Science”.