Page 237 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470

Fig.1 Proposed Work
Additionally, these platforms facilitate collaboration among learners by offering virtual teamwork opportunities. Students can
participate in group exercises to solve cybersecurity challenges, mimicking real-world team dynamics in professional settings.
The use of AI further enables intelligent matchmaking, pairing learners with complementary skill sets to enhance collaboration
and learning outcomes.

In conclusion, AI-powered eLearning platforms are reshaping the landscape of cybersecurity education by offering
personalized, up-to-date, and scalable solutions. By integrating advanced technologies, such platforms ensure learners are
better equipped to navigate the ever-evolving cybersecurity landscape. These innovations have the potential to address the
skills gap in the industry while fostering a highly competent and adaptive workforce capable of safeguarding the digital world.
IV. PROPOSED RESEARCH MODEL
This research model aims to explore and establish how AI-powered eLearning platforms can revolutionize cybersecurity
education, addressing the growing need for skilled professionals capable of countering emerging threats. The model comprises
several interconnected components that together provide a structured framework for the research.

1. Foundations of the Research
Ø Problem Statement: There is a significant gap between the demand for cybersecurity experts and the availability of
trained professionals. Traditional education methods fail to meet the pace of evolving cyber threats.
Ø Objective: To develop and validate an AI-powered eLearning framework that enhances cybersecurity education by
personalizing content, fostering practical skills, and ensuring continuous adaptability to new threats.

Ø Scope: The model focuses on both technical and pedagogical innovations, leveraging AI to enhance engagement, scalability,
and effectiveness in cybersecurity learning.
2. Key Components of the Model
Ø AI-Powered Personalization: AI algorithms will analyze learner profiles, performance, and preferences to create
personalized learning paths. This includes identifying weaknesses, suggesting tailored content, and optimizing pacing to
match individual needs.
Ø Virtual Labs and Simulations: The platform will integrate practical exercises like simulated cyberattacks and defense
scenarios, allowing learners to practice real-world skills in a safe environment.

Ø Real-Time Feedback and Assessment: The system will provide instant feedback on performance, highlighting areas of
improvement and tracking progress. AI will also suggest supplementary materials based on performance metrics.

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