# Generative AI-Enhanced Intelligent Tutoring System for Graduate Cybersecurity Programs
## 3 Platform Implementation  
As discussed in earlier research, there are notable gaps with regards to the application of GenAI ITS and, due to the limited scope, this reduces the realisable advantage of generalisation that comes with advances in pre-trained LLMs today. The decision to adopt a multi-agent orchestration approach using specialised generative AI tools, such as GPT-4, Streamlit, and Relevance AI, was driven by previous limitations identified in existing ITS solutions, primarily those employing traditional, pre-trained transformer models. For instance, the CyATP solution, while innovative, utilised a singular, static dataset (CROND), thus limiting its adaptability and scalability. Conversely, tools like ChatGPT for Educators and Duolingo MAX demonstrate the effectiveness of dynamic personalisation and adaptive content generation in educational contexts but have not yet been evaluated extensively in graduate-level cybersecurity education. We aim to address concerns of scalability, generalised enough to be applied across the multidisciplinary field, with an added contribution of holistic alignment to industry concerns (e.g., educator adoption, educational context, cybersecurity skills gap). Below, we detail a proposed platform as a solution, a conceptual user flow, the features and applications aligned with opportunities and objectives, and, in addition, a method for the evaluation of key criteria identified in earlier works.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**GenAI Solution** & **Key Characteristics** & **Identified Gaps** \\ \hline \multirow{4}{*}{ChatGPT for Educators [5]} & GPT-based platform offering personalised content generation and adaptive evaluations aimed at enhancing educator-driven learning outcomes through tailored, instructional support. & Limited in scalability and generalisability within specialised domains, particularly graduate-level cybersecurity education. The solution’s effectiveness in addressing multidisciplinary contexts requires further validation. \\ \hline \multirow{4}{*}{CyATP [18]} & Uses Natural Language Generation (NLG) with the Cyber-Range Organisation and Design (CROND) dataset at JAIST to generate quizzes and educational games, enhancing student engagement and learning effectiveness. Includes a framework for evaluating tutoring platforms. & Relics on a single static dataset (CROND), limiting scalability and generalisability. Does not adequately address broader multidisciplinary cybersecurity education contexts or dynamic learning content generation. \\ \hline \multirow{4}{*}{Duolingo LSTM Birdbrain [23]} & Employs birdbrain Long Short-Term Memory (LSTM) AI models to tailor personalised learning paths based on individual user needs, creating adaptive educational environments. & The applicability and adaptability to specialised, highly technical fields such as cybersecurity education remain unexplored. \\ \hline \multirow{4}{*}{Duolingo MAX [24]} & Advanced learning assistant using the latest GenAI advancements to enhance personalised education experiences, combining language learning with AI-driven instructional strategies and real-time adaptive feedback. & Limited empirical evidence of effectiveness in graduate-level, multidisciplinary education contexts, particularly for cybersecurity. \\ \hline \multirow{4}{*}{[PERSON] AI-Driven Gamification [26]} & Implements standardised methodologies and gamification using AI co-pilots to deliver personalised exercises and promote active learner engagement, continuous improvement, and tailored educational experiences in cybersecurity training. & Does not fully address scalability, generalisability across multidisciplinary contexts, and the specific challenges of integrating gamification with robust, dynamic, LLM-based content generation frameworks. \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary table of reviewed generative AI solutions, their key characteristics, and identified gaps.