Authors : Hiroko Yamashita; Lukas Meier

Volume/Issue : Volume 10 - 2025, Issue 10 - October

Google Scholar : https://tinyurl.com/3wb52r9v

Scribd : https://tinyurl.com/4wy8pyr2

DOI : https://doi.org/10.38124/ijisrt/25oct1328

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Abstract : 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.

Keywords : AI-Generated Text Detection, Academic Integrity, Multi-Modal Classification, Convolutional Neural Networks, Natural Language Processing, ChatGPT, Machine Learning, Student Submissions, Text Classification.

References :

1. Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in education and teaching international, 61(2), 228-239.
2. Sun, T., Yang, J., Li, J., Chen, J., Liu, M., Fan, L., & Wang, X. (2024). Enhancing auto insurance risk evaluation with transformer and SHAP. IEEE Access.
3. Ma, Z., Chen, X., Sun, T., Wang, X., Wu, Y. C., & Zhou, M. (2024). Blockchain-based zero-trust supply chain security integrated with deep reinforcement learning for inventory optimization. Future Internet, 16(5), 163.
4. Dergaa I, Chamari K, Zmijewski P, Saad HB. From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing. Biology of Sport. 2023;40(2):615-622.
5. Uzun L. ChatGPT and academic integrity concerns: Detecting artificial intelligence generated content. Language Education and Technology. 2023;3(1):45-54.
6. Ardito, C. G. (2025). Generative AI detection in higher education assessments. New Directions for Teaching and Learning, 2025(182), 11-28.
7. Dwivedi YK, Kshetri N, Hughes L, et al. So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management. 2023;71:102642.
8. Halaweh, M. (2023). ChatGPT in education: Strategies for responsible implementation. Contemporary educational technology, 15(2).
9. Pegoraro A, Kumari K, Fereidooni H, Sadeghi AR. To ChatGPT, or not to ChatGPT: That is the question. arXiv preprint. 2023;arXiv:2304.01487.
10. Cao, W., Mai, N. T., & Liu, W. (2025). Adaptive knowledge assessment via symmetric hierarchical Bayesian neural networks with graph symmetry-aware concept dependencies. Symmetry, 17(8), 1332.
11. Wu Y, Zhang X, Ren H. Improving text classification performance through multimodal representation. Pattern Recognition and Computer Vision. 2024;15037:312-325.
12. Cao, L. (2025). A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content. arXiv preprint arXiv:2504.02898.
13. Abimannan, S., El-Alfy, E. S. M., Chang, Y. S., Hussain, S., Shukla, S., & Satheesh, D. (2023). Ensemble multifeatured deep learning models and applications: A survey. IEEE Access, 11, 107194-107217.
14. Weber-Wulff D, Anohina-Naumeca A, Bjelobaba S, et al. Testing of detection tools for AI-generated text. International Journal for Educational Integrity. 2023;19:26.
15. Ge, Y., Wang, Y., Liu, J., & Wang, J. (2025). GAN-Enhanced Implied Volatility Surface Reconstruction for Option Pricing Error Mitigation. IEEE Access.
16. Zheng, W., & Liu, W. (2025). Symmetry-Aware Transformers for Asymmetric Causal Discovery in Financial Time Series. Symmetry, 17(10), 1591.
17. Tan, Y., Wu, B., Cao, J., & Jiang, B. (2025). LLaMA-UTP: Knowledge-Guided Expert Mixture for Analyzing Uncertain Tax Positions. IEEE Access.
18. Liu, Y., Ren, S., Wang, X., & Zhou, M. (2024). Temporal logical attention network for log-based anomaly detection in distributed systems. Sensors, 24(24), 7949.
19. Ren, S., Jin, J., Niu, G., & Liu, Y. (2025). ARCS: Adaptive Reinforcement Learning Framework for Automated Cybersecurity Incident Response Strategy Optimization. Applied Sciences, 15(2), 951.
20. Zhang, Q., Chen, S., & Liu, W. (2025). Balanced Knowledge Transfer in MTTL-ClinicalBERT: A Symmetrical Multi-Task Learning Framework for Clinical Text Classification. Symmetry, 17(6), 823.
21. Mai, N. T., Cao, W., & Liu, W. (2025). Interpretable knowledge tracing via transformer-Bayesian hybrid networks: Learning temporal dependencies and causal structures in educational data. Applied Sciences, 15(17), 9605.
22. Chen, S., Liu, Y., Zhang, Q., Shao, Z., & Wang, Z. (2025). Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions. Advanced Intelligent Systems, 2400898.
23. Mai, N. T., Cao, W., & Wang, Y. (2025). The global belonging support framework: Enhancing equity and access for international graduate students. Journal of International Students, 15(9), 141-160.
24. Naini, I., & Ulya, R. H. (2025). Reasoning Patterns and Sentence Construction Errors in Students’ Scholarly Articles: A Content Analysis of Academic Writing in Padang City. AL-ISHLAH: Jurnal Pendidikan, 17(2).
25. Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT. 2019;4171-4186.
26. Wang, Y., Ding, G., Zeng, Z., & Yang, S. (2025). Causal-Aware Multimodal Transformer for Supply Chain Demand Forecasting: Integrating Text, Time Series, and Satellite Imagery. IEEE Access.
27. Long, S., He, X., & Yao, C. (2021). Scene text detection and recognition: The deep learning era. International Journal of Computer Vision, 129(1), 161-184.
28. Qiu, L. (2025). Reinforcement Learning Approaches for Intelligent Control of Smart Building Energy Systems with Real-Time Adaptation to Occupant Behavior and Weather Conditions. Journal of Computing and Electronic Information Management, 18(2), 32-37.
29. Zhang, H. (2025). Physics-Informed Neural Networks for High-Fidelity Electromagnetic Field Approximation in VLSI and RF EDA Applications. Journal of Computing and Electronic Information Management, 18(2), 38-46.
30. Qiu, L. (2025). Multi-Agent Reinforcement Learning for Coordinated Smart Grid and Building Energy Management Across Urban Communities. Computer Life, 13(3), 8-15.
31. Li, J., Fan, L., Wang, X., Sun, T., & Zhou, M. (2024). Product demand prediction with spatial graph neural networks. Applied Sciences, 14(16), 6989.
32. Qiu, L. (2025). Machine Learning Approaches to Minimize Carbon Emissions through Optimized Road Traffic Flow and Routing. Frontiers in Environmental Science and Sustainability, 2(1), 30-41.