Large Language Models (LLMs) are now widely used to draft, revise, paraphrase, and polish text, making the detection of AI-generated writing increasingly difficult. This systematic literature review synthesizes peer-reviewed and high-quality studies published between 2023 and 2026 on AI-obfuscated, AI-refined, and humanized text. From 1,002 records, 26 primary studies were retained after screening and quality assessment. The review organizes the literature through a seven-dimensional taxonomy. Overall, the evidence shows that many detectors perform well on clean or in-distribution AI-text but become less reliable when the text is paraphrased, humanized, or collaboratively edited. The review also highlights recurring fairness concerns, especially for non-native English writers, and finds that current benchmarks often do not fully capture realistic mixed-authorship and adversarial settings. These results suggest that AI-text detection should be treated as one supportive signal rather than a stand-alone judgment, particularly in high-stakes academic or professional contexts.
Batyr Sharimbayev, S. Kadyrov· Engineering, Technology &...· 0 citations
This scoping review maps how artificial intelligence (AI) is being connected to teacher competence in recent research. The review was based on 33 peer-reviewed articles published in 2022–2026 and identified through a bounded Web of Science search. Its purpose was not to evaluate intervention effectiveness, but to describe the extent, range, and nature of the available evidence on AI, machine learning (ML), and learning analytics (LA) in teacher assessment, modeling, and professional development within this indexed corpus. The mapped literature suggests two broad lines of work. One uses AI, ML, LA, and computational psychometrics to assess teaching practice, model teacher development, or measure AI-TPACK-related competence. The other treats AI as part of what teachers themselves need to know and do. Instruments represented in the corpus, such as TAICS, T-GAIC, AI-SRLS, AI-TPACK, and RAIS, broaden the concept of competence to include AI literacy, self-efficacy, ethical reasoning, readiness, and teacher–AI co-teaching. The review found frequent use of supervised machine learning, regularized regression, EFA, CFA, SEM, and learning analytics, but limited reported use of explainable AI, subgroup fairness analysis, multimodal validation, and longitudinal designs. Quality appraisal indicated stronger support for measurement-structure claims than for causal claims about professional-development effectiveness or high-stakes AI deployment.
N. Baizhanov, Batyr Sharimbayev, Zhairan Churbanova et al.· Frontiers in Artificial Inte...· 0 citations
Test set results show that Decoding-Enhanced Bert with Disentangled Attention (DeBERTa) achieves the highest macro F1 − Score of 85.48%, surpassing the previously top-ranked Multi-Task Learning (MTL) system, which attains a macro F1 of 83.07%.
Batyr Sharimbayev, S. Kadyrov· Journal of Advances in Infor...· 0 citations