In recent years, Artificial Intelligence (AI) has rapidly entered the social sphere, including higher education. However, not all cultures perceive it in the same way. This comparative empirical study analyzes how students from two religiously homogeneous countries: Uzbekistan (predominantly Muslim) and Poland (predominantly Catholic) perceive AI in general and as a learning tool. Unlike most studies that focus on technical access or digital literacy, this paper examines the perception of AI from the perspective of historically shaped religious and cultural traditions after the collapse of the Soviet system. The study put forward and tested a research meta-hypothesis: historically shaped cultural and religious traditions should have a statistically significant effect on the perception of Artificial Intelligence by students from Muslim and Catholic countries. We used a standardized Likert-scale survey. The sample consisted of 439 undergraduate students (not majoring in IT). Professional and gender biases were excluded to focus on cultural and religious variables. The z-test at the 90% confidence level was used to test the four key hypotheses. The results showed AI than students from Poland. This difference in perception applies to AI both in general and in the educational context. This difference reflects stable ideas about knowledge, traditions and technological progress. The obtained results have theoretical implications and practical significance .
In this paper, we describe the defining identities of a variety of binary perm algebras which is a subvariety of the variety of alternative algebras. Moreover, we construct a basis of the free binary perm algebra. In addition, we describe subalgebras of binary perm algebras under commutator, which are related to Malcev algebras.
This paper presents the first prototype of a speech-to-sign language translation system for Kazakh Sign Language (KRSL). The proposed pipeline integrates the NVIDIA FastConformer model for automatic speech recognition (ASR) in the Kazakh language and addresses the challenges of sign language translation in a low-resource setting. Unlike American or British Sign Languages, KRSL lacks publicly available datasets and established translation systems. The pipeline follows a multi-stage process: speech input is converted into text via ASR, segmented into phrases, matched with corresponding gestures, and visualized as sign language. System performance is evaluated using word error rate (WER) for ASR and accuracy metrics for speech-to-sign translation. This study also introduces the first KRSL dataset, consisting of 1200 manually recreated signs, including 95% static images and 5% dynamic gesture videos. To improve robustness under resource-constrained conditions, a Weighted Hybrid Similarity Score (WHSS)-based gesture matching method is proposed. Experimental results show that the FastConformer model achieves an average WER of 10.55%, with 7.8% for isolated words and 13.3% for full sentences. At the phrase level, the system achieves 92.1% accuracy for unigrams, 84.6% for bigrams, and 78.3% for trigrams. The complete pipeline reaches 85% accuracy for individual words and 70% for sentences, with an average latency of 310 ms. These results demonstrate the feasibility and effectiveness of the proposed system for supporting people with hearing and speech impairments in Kazakhstan.
The rise of Artificial Intelligence (AI)-generated text has led to the development of numerous detection tools to distinguish between human and machine-authored content. However, the effectiveness of these tools, especially against manipulated texts, remains uncertain. This study evaluates nine widely used AI detection tools-Turnitin, ZeroGPT, Detecting-AI.com, GPTZero, QuillBot, Grammarly, Sapling, Copyleaks, and Originality.ai-using texts from four large language models-ChatGPT, DeepSeek, Gemini, and Grok-as well as human-written samples. Initial findings indicate that commercial tools, such as Copyleaks and Originality.ai, achieved near-perfect detection rates, while free tools, including Grammarly and QuillBot, performed less reliably, with some as low as 63.0%. On the other hand, paraphrasing and Non-Native English Speakers (NNES)-style rewriting techniques reduced detection accuracy across most detectors. Turnitin dropped to 45.7%, while Grammarly fell to 19.0% in some cases. Only Copyleaks, GPTZero, and Sapling maintained strong performance under obfuscation. The study highlights three issues: inconsistent detector performance, the impact of obfuscation, and ethical risks, including bias and false positives. The study suggests that while some detectors offer robust baseline performance, combining them with pedagogical strategies and policies is essential to uphold academic integrity.
Machine translation for low-resource languages such as Kazakh remains a complex task due to the scarcity of training data, intricate morphological structures, and culturally specific linguistic characteristics. This study presents the first extensive exploration of fine-tuning large language models for automated post-editing of Kazakh translations. We introduce KazPE, a carefully curated and annotated dataset that includes 10,008 training sentences and 311 test sentences spanning six domains: the medical, scientific, journalistic, oral, fiction, and legal. The dataset features detailed error classifications across 9 linguistic categories. Our method fine-tunes GPT-4.1 mini using supervised learning to enhance translation quality by systematically correcting targeted errors. According to human evaluations, conducted on a continuous 0-1 scale, the fine-tuned model achieves an average quality score of 0.84, surpassing the baseline score of 0.80, corresponding to a 5% relative improvement. The greatest improvements are observed in handling morphological and lexical errors, as well as in domain-specific texts-particularly in legal (+17%) and medical (+22%) domains. In addition, the translations were evaluated using the automatic metrics: BLEU, TER and METEOR. The fine-tuned model shows improvements across all automatic metrics (BLEU, TER, METEOR), which confirms better n-gram overlap with reference texts, fewer edits needed, and enhanced lexical and semantic alignment with the reference texts. Comprehensive error analysis shows that the fine-tuning process effectively mitigates challenges related to Kazakh's agglutinative morphology and specialized terminology, while preserving accuracy on already correct sentences. This research establishes the first structured evaluation framework for Kazakh translation post-editing and offers valuable guidance for enhancing machine translation in morphologically rich, low-resource languages. To facilitate further progress in Turkic language processing, we publicly release the KazPE dataset, trained models, and evaluation framework.
