DATA SCIENCE-BASED APPROACHES TO AI-GENERATED CONTENT DETECTION AND THEIR IMPLICATIONS FOR THE ADVANCEMENT OF PEDAGOGICAL EDUCATION IN THE CONTEXT OF DIGITAL TRANSFORMATION
DOI:
https://doi.org/10.55439/EIT/vol13_iss7/734Kalit so‘zlar:
data science, machine learning, natural language processing, ai-generated content detection, digital transformation, pedagogical education, academic integrity, hybrid analytical models, semantic analysis, educational technology, digital literacy, ethical standards, content verification, higher education, intelligent systems, generative ai, online publications, sustainable education development.Abstrak
This article examines the integration of Data Science-based approaches for detecting AI-generated content in academic and online publications and explores their implications for the advancement of pedagogical education in the era of digital transformation. The proliferation of generative AI models presents new challenges for ensuring the authenticity, reliability, and academic integrity of educational resources. By employing Natural Language Processing (NLP), machine learning algorithms, and hybrid analytical frameworks, it becomes possible to identify patterns, linguistic markers, and semantic anomalies that distinguish machine-generated text from human-authored materials. The study highlights how these technological solutions can support educators in maintaining quality standards, fostering digital literacy, and strengthening ethical practices in teaching and research. Furthermore, the integration of AI-detection systems into pedagogical processes is considered a critical component of digital transformation, offering opportunities to modernize curricula, enhance student engagement, and establish a more resilient educational ecosystem. The findings suggest that Data Science not only provides technical tools for content verification but also serves as a strategic driver for the sustainable development of pedagogical education.
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