With the rise of generative artificial intelligence (GenAI), academic dishonesty in classrooms has skyrocketed, yet the existing solutions for detecting such dishonesty often fall short. Standard "AI detectors" merely analyze one text at a time, failing to account for students' previous writings, which risks erroneous predictions. Meanwhile, existing token-based authorship verification (AV) models fail to analyze the nuances in writing styles that truly distinguish authorship. To fill this existing gap, we propose a novel AV framework that combines token-level stylometric features (e.g., POS tag patterns) with handcrafted stylistic features (e.g., sentence structure variation) to construct a comprehensive feature set. Using both benchmark corpora and real-world high school student essays, we trained multiple machine learning classifiers using the proposed feature set. Our initial experiments show that our approach outperforms the standard token-only baselines by over 25%, while offering interpretable, style-based insights. These preliminary results highlight the importance of nuanced stylistic features and suggest that a holistic AV system can provide educators with more reliable and transparent detection tools. Looking ahead, we plan to extend this work with large language models and multi-agent approaches to further enhance robustness and adaptability.