This paper introduces an AI framework for autonomous assessment of student work, addressing policy gaps in academic practices. A survey of 117 academics from the UK, UAE, and Iraq reveals positive attitudes toward AI in education, particularly for autonomous assessment. The study also highlights a lack of awareness of modern AI tools among experienced academics, emphasizing the need for updated policies and training.
This research explores the use of generative AI, specifically ChatGPT, to create student assessments that align with academic accreditation standards, such as those of the National Center for Academic Accreditation in Saudi Arabia and ABET. The study introduces a method for mapping verbs used in questions to educational outcomes, enabling AI to produce and validate accreditation-compliant questions. A survey of faculty members in Saudi universities showed high acceptance rates for AI-generated exam questions and AI assistance in editing existing questions.
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.
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 Regulatory Knowledge Graph (RKG) for the Abu Dhabi Global Market (ADGM) regulations, constructed using language models and graph technologies. A portion of the regulations was manually tagged to train BERT-based models, which were then applied to the rest of the corpus. The resulting knowledge graph, stored in Neo4j, and code are open-sourced on GitHub to promote advancements in compliance automation.