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.
MBZUAI has launched the Ruwwad AI Scholars (RAIS) program, a postdoctoral fellowship for Emirati Ph.D. graduates to undertake two-year, fully-funded research positions at leading global institutions. The program aims to cultivate local talent in AI and computational research, with the goal of strengthening participants' eligibility for faculty positions at MBZUAI. The fellowship covers a stipend, research funds, insurance, relocation support, and conference travel.
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.
The Symposium on Data Mining and Applications (SDMA 2014) was organized by MEGDAM to foster collaboration among data mining and machine learning researchers in Saudi Arabia, GCC countries, and the Middle East. The symposium covered areas such as statistics, computational intelligence, pattern recognition, databases, Big Data Mining and visualization. Acceptance was based on originality, significance and quality of contribution.
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.
A new culturally inclusive and linguistically diverse dataset called Palm for Arabic LLMs is introduced, covering 22 Arab countries and featuring instructions in both Modern Standard Arabic (MSA) and dialectal Arabic (DA) across 20 topics. The dataset was built through a year-long community-driven project involving 44 researchers from across the Arab world. Evaluation of frontier LLMs using the dataset reveals limitations in cultural and dialectal understanding, with some countries being better represented than others.
Researchers introduce ASAD, a new large-scale, high-quality Arabic Sentiment Analysis Dataset based on 95K tweets with positive, negative, and neutral labels. The dataset is launched with a competition sponsored by KAUST offering a total of 17000 USD in prizes. Baseline models are implemented and results reported to provide a reference for competition participants.