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 International Education


Domain-Grounded Evaluation of LLMs in International Student Knowledge

Daitx, Claudinei, Amar, Haitham

arXiv.org Artificial Intelligence

Large language models (LLMs) are increasingly used to answer high-stakes study-abroad questions about admissions, visas, scholarships, and eligibility. Yet it remains unclear how reliably they advise students, and how often otherwise helpful answers drift into unsupported claims (``hallucinations''). This work provides a clear, domain-grounded overview of how current LLMs behave in this setting. Using realistic questions set drawn from ApplyBoard's advising workflows -- an EdTech platform that supports students from discovery to enrolment -- we evaluate two essentials side by side: accuracy (is the information correct and complete?) and hallucination (does the model add content not supported by the question or domain evidence). These questions are categorized by domain scope which can be a single-domain or multi-domain -- when it must integrate evidence across areas such as admissions, visas, and scholarships. To reflect real advising quality, we grade answers with a simple rubric which is correct, partial, or wrong. The rubric is domain-coverage-aware: an answer can be partial if it addresses only a subset of the required domains, and it can be over-scoped if it introduces extra, unnecessary domains; both patterns are captured in our scoring as under-coverage or reduced relevance/hallucination. We also report measures of faithfulness and answer relevance, alongside an aggregate hallucination score, to capture relevance and usefulness. All models are tested with the same questions for a fair, head-to-head comparison. Our goals are to: (1) give a clear picture of which models are most dependable for study-abroad advising, (2) surface common failure modes -- where answers are incomplete, off-topic, or unsupported, and (3) offer a practical, reusable protocol for auditing LLMs before deployment in education and advising contexts.


History of CNN & its impact in the field of Artificial Intelligence

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Hubel and Wiesel's research in the 1950s and 1960s showed that cat visual cortices include neurons that react to tiny parts of the visual field separately. The region of visual space within which visual inputs impact the firing of a single neuron is known as its receptive field while the eyes are not moving. Neighboring cells have receptive fields that are comparable and overlap. The size and location of receptive fields vary consistently across the cortex to generate a full map of visual space. The contralateral visual field is represented by the cortex in each hemisphere.


The power of human connection in decision-making in a data driven international student recruitment

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We need career planners, and not just people to get admission! Thanks to the pandemic, which pushed the limits of online aggregators and EdTech companies in international student recruitment. We are witnessing technology slowly making a powerful impact on student recruitment; though the industry is yet to witness the full power of Artificial Intelligence and Automation. Building AI platforms are going to be cheaper and replicating a technology model doesn't require a big innovation. The industry is going to be dumped with too much data for recruiters and students.


Machine Learning with Python: from Linear Models to Deep Learning

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Who can take this course? Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. Who can take this course?


Data and AI Fundamentals

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Organizations are increasingly adopting AI as a way to enable data-driven decision making, and as a great source of automated predictions that will potentially generate interesting savings or new sources of revenue. Even our personal devices such as smartphones or voice assistants are already leveraging AI technologies. However, the level of AI maturity within the companies varies a lot, as well as the needs for AI-savvy professionals. Reality is that not everyone needs to be an AI expert or a data scientist. Companies need other kinds of profiles for which at least AI knowledge is required, such as product managers or top executives managing innovation initiatives. This course is designed to give you an introduction to the amazing world of Artificial Intelligence.


Statistical Learning

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This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science.


MIT organises two-day workshop on Artificial intelligence

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It is aimed at providing industry exposure to students where experts from reputed organisations guided students to get a deeper understanding of artificial intelligence and machine learning techniques. Madhusudhan Govindaraju, Professor, Department of Computer Science and Vice Provost for International Education and Global Affairs (IEGA), Binghamton University, New York was the chief guest. He mentioned about opportunities available for MAHE students and faculty at Binghamton University. M. D. Venkatesh, Vice Chancellor, MAHE, also spoke at the event, a release mentioned.


Artificial Intelligence (AI) for Earth Monitoring - FutureLearn

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This is a fast-changing and critical time for Earth Observation (EO), especially for those involved in its use for climate and meteorology. On this course, you'll get a comprehensive overview of the Copernicus Programme and the wealth of EO data it provides, as well as how AI and ML are transforming the interpretation of EO data. You'll learn about the Copernicus data and services and the massive amounts of Earth observation data that are collected every day from space, covering the oceans, land, atmosphere and, over longer periods, the climate. You'll then learn basic AI and ML concepts and types, exploring how they have transformed many aspects of the EO'value chain'. This includes automatic feature extraction, new ways of processing very large data sets, and the development of new products and services.


Data Science: Machine Learning and Predictions

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One of the principal responsibilities of a data scientist is to make reliable predictions based on data. When the amount of data available is enormous, it helps if some of the analysis can be automated. Machine learning is a way of identifying patterns in data and using them to automatically make predictions or decisions. In this data science course, you will learn basic concepts and elements of machine learning. The two main methods of machine learning you will focus on are regression and classification.


CS50's Introduction to Artificial Intelligence with Python

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AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly. This course will enable you to take the first step toward solving important real-world problems and future-proofing your career. CS50's Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs.