Development of an interface for intelligent gesture control to improve user experience and increase the efficiency of interaction with a computer. This paper proposes a gesture recognition system based on artificial intelligence using convolutional neural networks (CNN). The system comprises three stages: pre-processing, optimal frame determination, and gesture category identification. The extracted features used are independent of movement, scaling, and rotation, providing greater flexibility to the system. The suggested gesture control technology, known as Kazakh Sign Language (KSL) for Kazakh alphabets, eliminates the need for additional devices, enabling users to interact with the system naturally. Experiments demonstrated that the proposed KSL system can accurately recognize Kazakh language alphabet letters with a high precision of 97.3%, owing to the utilization of artificial intelligence and CNN to enhance the accuracy and effectiveness of gesture control. Gestures, a type of visual formation, are perceivable by computers through machine learning models. The selection of methods and systems for recognizing Kazakh sign language gestures was accompanied by addressing various challenges related to language-specific orthographic and gestural features. The developed gesture control interface for human-computer interaction is applied in the field of inclusive education, aiming to assist deaf and hard-of-hearing children in learning sign language.

Ensuring legislative consistency is increasingly difficult as legal systems expand and change more frequently, and the problem is sharper where two language versions are equally binding. This systematic review examines artificial intelligence applied to legal and legislative texts, following the PRISMA 2020 guidelines. Searches of Scopus and the Web of Science Core Collection for English-language peer-reviewed journal articles (January 2019–May 2026) returned 497 records, of which 231 studies met the inclusion criteria; 102 supplied the core analytical evidence and 129 characterized the wider research landscape. Publication output rose sharply after 2024 without a matching improvement in reporting and methodological completeness. Within the reviewed corpus, legal summarization and judgment prediction are the most established directions, whereas natural language inference, legal knowledge representation and compliance verification remain underdeveloped. Only four studies released code or datasets, and although thirteen addressed multilingual legal texts, none of them examined bilingual legislative corpora in which both versions carry equal legal force. Work in the reviewed corpus therefore advances individual legal natural language processing (NLP) tasks but offers no integrated framework for detecting contradictions in bilingual legislation. We propose a conceptual four-layer architecture combining legislative text normalization, knowledge-graph representation, hybrid semantic reasoning and explainable human oversight, and identify research priorities for reliable and transparent AI-assisted legislative analysis.

Legal question answering is usually evaluated as if the law were static. In Kazakhstan, it changed twice within one year: a new Constitution replaced the 1995 text on 1 July 2026, and a new Tax Code replaced its predecessor on 1 January 2026, both reusing article numbers for different norms. We release a dated corpus of 41 Kazakhstani acts parsed to article level in both official languages (12,235 Russian and 11,973 Kazakh articles) and a benchmark of 150 question-answer pairs in seven layers, two of which score declining to answer and asking for a date. Two practising lawyers confirmed every pair. Comparing a no-retrieval baseline, retrieval-augmented generation and a tool-using agent, we find that retrieval rather than generation accounts for most citation error, measured against each pair’s source articles: given the gold article, the model cites it in 92% of cases, and retrieval alone reduces citations of non-existent norms from 31% to zero. The agent adds nothing on this measure and abstains far less often. Dense retrieval improves citation recall but nearly doubles the repealed norms presented as current; date-aware ranking removes them for every retriever, although under dense retrieval it also removes three historically required citations. The retrieval findings are reproduced on two open models.