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 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.
KAUST President Jean-Lou Chameau spoke at the Times Higher Education MENA Universities Summit in Doha, Qatar. He shared his experiences from Caltech and Georgia Tech, emphasizing KAUST's historic undertaking. KAUST's research output leads Saudi Arabia and surpassed other Arab institutes in 2014 according to the Nature Index report. Why it matters: The summit and KAUST's participation highlight the increasing role of universities in driving economic diversification and knowledge creation in the MENA region.
KAUST President Jean-Lou Chameau spoke at the 5th Annual Saudi International Technology Incubation Conference in Riyadh. He emphasized that universities are catalysts for innovation through a commitment to excellence in education and research. KAUST was created to be a model for advanced education, scientific research, and economic development. Why it matters: The discussion highlights the crucial role of universities like KAUST in fostering innovation and economic growth in transitional economies like those in the GCC.
This article discusses the increasing concerns about the interpretability of large deep learning models. It highlights a talk by Danish Pruthi, an Assistant Professor at the Indian Institute of Science (IISc), Bangalore, who presented a framework to quantify the value of explanations and the need for holistic model evaluation. Pruthi's talk touched on geographically representative artifacts from text-to-image models and how well conversational LLMs challenge false assumptions. Why it matters: Addressing interpretability and evaluation is crucial for building trustworthy and reliable AI systems, particularly in sensitive applications within the Middle East and globally.
Researchers from the National Center for AI in Saudi Arabia investigated the sensitivity of Large Language Model (LLM) leaderboards to minor benchmark perturbations. They found that small changes, like choice order, can shift rankings by up to 8 positions. The study recommends hybrid scoring and warns against over-reliance on simple benchmark evaluations, providing code for further research.