Detecting AI-Generated Text in Student Submissions Using Multi-Modal Classification

The rapid proliferation of generative Artificial Intelligence (AI) tools, particularly Large Language Models (LLMs) such as ChatGPT, has introduced unprecedented challenges to academic integrity in higher education. Students increasingly utilize these AI systems to generate essays, reports, and assignments, creating an urgent need for robust detection mechanisms that can identify AI-generated content in academic submissions. This study presents a comprehensive multi-modal classification approach that integrates multiple feature extraction techniques including stylometric analysis, linguistic pattern recognition, and semantic coherence measurement to detect AI-generated text with enhanced accuracy. By employing Convolutional Neural Networks (CNNs) for local feature extraction, recurrent neural architectures for sequential pattern analysis, and fusion-based ensemble learning methods that combine multiple classification pathways, our proposed framework achieves detection accuracy of 94.3 percent on a corpus of authentic student submissions and AI-generated counterparts. The multi-modal approach addresses limitations of single-modality detection systems by capturing diverse textual characteristics including vocabulary diversity, syntactic complexity, semantic consistency, and discourse structure patterns that distinguish human and AI writing. Experimental results demonstrate that AI-generated texts exhibit statistically significant differences in lexical diversity metrics, n-gram patterns, and topic coherence measures compared to authentic student writing. Furthermore, this research investigates the challenges of detection evasion strategies including paraphrasing and hybrid authorship scenarios where students modify AI-generated content. The findings underscore both the potential and limitations of current detection technologies while providing practical recommendations for educational institutions seeking to maintain academic integrity in the age of generative AI.