This study assesses workforce preparedness for AI in the GCC region, using socio-technical systems theory to analyze national AI strategies and initiatives in KSA, UAE, Qatar, Kuwait, Bahrain, and Oman. The research combines TF-IDF analysis, case studies of MBZUAI and SDAIA Academy, and scenario planning to evaluate the balance between technical capacity and social alignment. The study identifies a potential two-track talent system and emphasizes the importance of regulatory convergence for successful AI adoption.
A survey of 92 library and information science (LIS) professionals in the UAE reveals strong cognitive AI competencies but gaps in behavioral and normative competencies related to AI biases and ethics. The study identifies a disconnect between the perceived importance of AI skills and the effectiveness of current training programs. It recommends that library training programs address AI ethics and biases.
The study analyzes over 1,000 images generated by ImageFX, DALL-E V3, and Grok for 56 Saudi professions, finding significant gender imbalances and cultural inaccuracies. DALL-E V3 exhibited the strongest gender stereotyping, with 96% male depictions, particularly in leadership and technical roles. The research underscores the need for diverse training data and culturally sensitive evaluation to ensure equitable AI outputs that accurately reflect Saudi Arabia's labor market and culture.
This paper discusses the integration of AI into education, emphasizing a transdisciplinary approach that connects AI instruction to the broader curriculum and community needs. It delves into the AI program developed for Neom Community School in Saudi Arabia, where AI is taught as a subject and used to learn other subjects through the International Baccalaureate (IB) approach. The proposed method aims to make AI relevant throughout the curriculum by integrating it into Units of Inquiry.
This paper introduces a framework that combines machine learning for multi-class attack detection in IoT/IIoT networks with large language models (LLMs) for attack behavior analysis and mitigation suggestion. The framework uses role-play prompt engineering with RAG to guide LLMs like ChatGPT-o3 and DeepSeek-R1, and introduces new evaluation metrics for quantitative assessment. Experiments using Edge-IIoTset and CICIoT2023 datasets showed Random Forest as the best detection model and ChatGPT-o3 outperforming DeepSeek-R1 in attack analysis and mitigation.
The ArabJobs dataset is a new corpus of over 8,500 Arabic job advertisements collected from Egypt, Jordan, Saudi Arabia, and the UAE. The dataset contains over 550,000 words and captures linguistic, regional, and socio-economic variation in the Arab labor market. It is available on GitHub and can be used for fairness-aware Arabic NLP and labor market research.
This paper introduces the AI Pentad model, comprising humans/organizations, algorithms, data, computing, and energy, as a framework for AI regulation. It also presents the CHARME²D Model to link the AI Pentad with regulatory enablers like registration, monitoring, and enforcement. The paper assesses AI regulatory efforts in the EU, China, UAE, UK, and US using the CHARME²D model, highlighting strengths and weaknesses.