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All Journal Journal of Digital Learning and Distance Education Scientific Journal of Computer Science
AIRHIAVBERE, Augustine Osazee
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Humanities Computer Science & IT Languange, Linguistic, Communication & Media Social Sciences Other

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DistilBERT-Based Detection of AI-Generated Text in Online Assessments: Ethical and Pedagogical Implications EJENARHOME, Prosper Otega; Oise, Godfrey Perfectson; AIRHIAVBERE, Augustine Osazee; Odimayomi, Joy Akpowehbve
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION Vol. 4 No. 9 (2026): Journal of Digital Learning and Distance Education (JDLDE)
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/jdlde.v4i9.651

Abstract

The rapid shift toward online and distance learning has positioned digital assessment as a cornerstone of higher education while also challenging academic integrity due to the accessibility of generative artificial intelligence (GenAI). While technical research into AI detection is expanding, there remains a critical gap in understanding how detection outcomes can be ethically and pedagogically integrated into digital learning environments. This study evaluates a fine-tuned DistilBERT-based model for detecting AI-generated text, situating its technical performance within a learning-centered framework. Using a large-scale dataset of over 28,000 human-written and AI-generated essays, the model demonstrated exceptional robustness, achieving an overall accuracy of 99%, an AUC of 0.9999, and balanced F1 scores of 0.99. Beyond technical metrics, this research redefines AI detection by shifting the narrative from a punitive, surveillance-oriented mechanism to a supportive learning analytics tool. By interpreting detection results alongside instructional indicators, the study demonstrates how these technologies can inform assessment redesign, enhance transparency, and foster learner trust. The findings contribute to the field of digital education by providing a roadmap for the responsible integration of AI detection into assessment ecosystems, ensuring that technological precision serves the broader goals of fairness and pedagogical innovation.
A Convolutional Neural Network Framework for Intelligent Intrusion Detection Oise, Godfrey Perfectson; Olanrewaju, Babatunde Seyi; Orukpe, Oshoiribhor Austin; Pius, Kevin Chinedu; Airhiavbere, Augustine Osazee
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.404

Abstract

The rapid expansion of cloud computing, Internet of Things (IoT), and distributed network environments has significantly increased vulnerability to sophisticated cyber threats, exposing the limitations of traditional signature-based intrusion detection systems. Although deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown promising performance in intrusion detection, challenges related to validation transparency, statistical reliability, and interpretability remain inadequately addressed. This study proposes an intelligent CNN-based intrusion detection framework designed to improve detection accuracy, robustness, and model explainability. The framework is evaluated using the UNSW-NB15 benchmark dataset, which reflects realistic modern cyber-attack scenarios. A comprehensive preprocessing pipeline involving data cleaning, categorical encoding, feature normalization, and data reshaping is applied to enhance learning efficiency. To ensure unbiased evaluation, stratified k-fold cross-validation and an independent held-out test set are employed. Experimental results demonstrate that the proposed CNN achieves a test accuracy of 91.8%, with balanced precision, recall, and F1-score across benign and malicious traffic classes. Multi-class detection analysis further confirms the model’s capability to distinguish among diverse attack categories. Statistical validation using mean performance metrics, standard deviation, and confidence intervals demonstrates stable generalization performance. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) is used to enhance interpretability by identifying network-level features that influence classification decisions. An ablation study further validates the effectiveness of key architectural components. The results indicate that the proposed framework provides a reliable, scalable, and interpretable solution for intelligent intrusion detection in modern high-dimensional network environments.
Co-Authors EJENARHOME, Prosper Otega Odimayomi, Joy Akpowehbve Oise, Godfrey perfectson Olanrewaju, Babatunde Seyi Orukpe, Oshoiribhor Austin Pius, Kevin Chinedu