Integrating Generative AI into Cybersecurity Education: A Study of OCR and Multimodal LLM-assisted Instruction

• URL: http://arxiv.org/abs/2509.02998v1
• Date: Wed, 03 Sep 2025 04:16:50 GMT
• Title: Integrating Generative AI into Cybersecurity Education: A Study of OCR and Multimodal LLM-assisted Instruction
• Authors: Karan Patel, Yu-Zheng Lin, Gaurangi Raul, Bono Po-Jen Shih, Matthew W. Redondo, Banafsheh Saber Latibari, Jesus Pacheco, Soheil Salehi, Pratik Satam,
• Abstract summary: This full paper describes an LLM-assisted instruction integrated with a virtual cybersecurity lab platform.<n>With rising emphasis on robotics, automation, AI, and security, re-skilling and up-skilling are essential.<n>We present a generative AI instructional assistant integrated into a prior experiential learning platform.
• Score: 0.5394948236100675
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This full paper describes an LLM-assisted instruction integrated with a virtual cybersecurity lab platform. The digital transformation of Fourth Industrial Revolution (4IR) systems is reshaping workforce needs, widening skill gaps, especially among older workers. With rising emphasis on robotics, automation, AI, and security, re-skilling and up-skilling are essential. Generative AI can help build this workforce by acting as an instructional assistant to support skill acquisition during experiential learning. We present a generative AI instructional assistant integrated into a prior experiential learning platform. The assistant employs a zero-shot OCR-LLM pipeline within the legacy Cybersecurity Labs-as-a-Service (CLaaS) platform (2015). Text is extracted from slide images using Tesseract OCR, then simplified instructions are generated via a general-purpose LLM, enabling real-time instructional support with minimal infrastructure. The system was evaluated in a live university course where student feedback (n=42) averaged 7.83/10, indicating strong perceived usefulness. A comparative study with multimodal LLMs that directly interpret slide images showed higher performance on visually dense slides, but the OCR-LLM pipeline provided comparable pedagogical value on text-centric slides with much lower computational overhead and cost. This work demonstrates that a lightweight, easily integrable pipeline can effectively extend legacy platforms with modern generative AI, offering scalable enhancements for student comprehension in technical education.

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