Oral assessments, more security checks and speedier marking are all on the cards as generative artificial intelligence (AI) could transform exams for the next generation of students. As the 2025 exam season drew to a close with GCSE students picking up their results on Thursday, after mostly sitting traditional pen and paper exams, AI is already changing the landscape. Exam preparation is undergoing a revolution, with students increasingly creating personal AI tutors, available around the clock to generate learning materials to suit individual needs that potentially lead to better results. "Using AI can give a student a much better understanding of a subject because they can ask those questions they wouldn't ask in class, or at odd hours, without being judged," said Dr Andrew Rogoyski of the Surrey Institute for People-Centred AI. "It really took off this summer," said Sandra Leaton Gray, a professor of education futures at University College London's Institute of Education. "So they're able to talk to it about the marking frameworks that are in use and upload those, and then they're able to do sample answers on their own. And then they're able to say to the AI: 'How would you improve the answer?' It's like having a tireless tutor."

This paper presents a comprehensive investigation into the capability of Large Language Models (LLMs) to successfully complete a semester-long undergraduate control systems course. Through evaluation of 115 course deliverables, we assess LLM performance using ChatGPT under a "minimal effort" protocol that simulates realistic student usage patterns. The investigation employs a rigorous testing methodology across multiple assessment formats, from auto-graded multiple choice questions to complex Python programming tasks and long-form analytical writing. Our analysis provides quantitative insights into AI's strengths and limitations in handling mathematical formulations, coding challenges, and theoretical concepts in control systems engineering. The LLM achieved a B-grade performance (82.24\%), approaching but not exceeding the class average (84.99\%), with strongest results in structured assignments and greatest limitations in open-ended projects. The findings inform discussions about course design adaptation in response to AI advancement, moving beyond simple prohibition towards thoughtful integration of these tools in engineering education. Additional materials including syllabus, examination papers, design projects, and example responses can be found at the project website: https://gradegpt.github.io.

Pupils should be made to do some of their coursework'in class under direct supervision', exam boards have said - amid fears students are cheating their way through school. Recently, breakthroughs in artificial intelligence such as ChatGPT have led to concerns that young people may use them to achieve higher grades. The program is able to create writing and other content – such as coursework or essays - almost indistinguishable from that of a human. The Joint Council for Qualifications (JCQ), which represents the UK's major exam boards, has published guidance for teachers and assessors on'protecting the integrity of qualifications' in the context of AI use. Schools should make pupils aware of the risks of using AI and the possible consequences of using it'inappropriately' in assessment, the guidance said.

The field of natural language processing (NLP) has been transformed by massive pre-trained language models. They form the basis of all state-of-the-art systems across a wide range of tasks and have shown an impressive ability to generate fluent text and perform few-shot learning. At the same time, these models are hard to understand and give rise to new ethical and scalability challenges. In this course, students will learn the fundamentals about the modeling, theory, ethics, and systems aspects of large language models, as well as gain hands-on experience working with them. Where: Class will by default be in person at 200-002 (History Corner).

The process of earning the PhD is very different from the process of earning a bachelor's or a master's degree. It is more like an internship or a job. The first two or so years of any PhD program will be largely coursework, but even at this stage you will be balancing spending time on your courses against spending time on research – either because you are rotating through different labs, because you are performing research for a qualifier, or because your advisor is attaching you to an existing research project to give you some experience and mentorship before you develop your own project. This means that getting a PhD is not actually a way to avoid "getting a job" or to "stay in school" – it is actually a job. This also means that just because you are good at or enjoy coursework does not mean you will necessarily enjoy or excel in a PhD program, and just because you struggled with coursework does not mean you will not flourish in a PhD program.

The OSSU curriculum is a complete education in computer science using online materials. It's for those who want a proper, well-rounded grounding in concepts fundamental to all computing disciplines, and for those who have the discipline, will, and (most importantly!) good habits to obtain this education largely on their own, but with support from a worldwide community of fellow learners. It is designed according to the degree requirements of undergraduate computer science majors, minus general education (non-CS) requirements, as it is assumed most of the people following this curriculum are already educated outside the field of CS. The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc., but specifically chosen to meet the following criteria. When no course meets the above criteria, the coursework is supplemented with a book.

Artificial intelligence is getting a little smarter every day. Digital learning continues its expansion at all levels of education. Bots are designed to make our lives easier, more informative, and more interesting. Their machine learning capabilities make them a promising technology in education. The knowledge base of chatbots will only grow, and the bots themselves will be able to learn along with students.

We're expanding freeCodeCamp's Python section into a full-blown data science curriculum and we need your help. Currently, these data science skills are only taught at a few universities around the world. Our goal is for anyone, anywhere to be able to learn these advanced mathematics and machine learning concepts and that data scientists use in the field. You'll be able to code your way through a series of hundreds of Python projects to earn certifications in: You'll be able to build these projects in your browser or offline in your operating system. You'll use Jupyter Notebooks filled with interactive graphs, datasets, and automated tests to check your code.

The Artificial Intelligence (AI) Residency Program is a one-year research training position designed to give you hands-on experience with artificial intelligence research while working in Facebook AI. This program is designed for applicants who seek advanced experience in artificial intelligence research, under the mentorship of an established researcher, with the long-term goal of obtaining an AI research scientist/engineer role. The AI Residency application is open to all fields of study, including areas ostensibly unrelated to machine learning such as (but not limited to) math, physics, finance, economics, linguistics, computational social science, neuroscience and bioinformatics. We seek people from a diverse range of technical backgrounds who have prior coding experience and are passionate about AI research. Candidates for the residency should have a foundational knowledge in ML and AI research, and have plans to pursue a career in research after completing the program.

On Sept. 9, during the DOD's semi-annual Artificial Intelligence Symposium and Exposition, Secretary of Defense Mark Esper affirmed that the Joint Artificial Intelligence Center (JAIC) in partnership with the Naval Postgraduate School (NPS) and Defense Acquisition University will collaboratively develop an intensive six-week pilot course delivered to more than 80 defense acquisition professionals of all ranks and grades. "These trainees will learn how to apply AI and data science skills to our operations," Esper said in his remarks. "With the support of Congress, the Department plans to request additional funding for the services to grow this effort over time and deliver an AI-ready workforce to the American people." Just as the university's highly-regarded Harnessing Artificial Intelligence video course paved the way for its support of the pilot course, NPS is well positioned to support Esper's declaration for further workforce development through its existing Data Science Certificate, and an upcoming similar certificate program in Artificial Intelligence. In the ongoing effort to expand the Navy's knowledge and expertise in the fields of data science and artificial intelligence, NPS faculty have developed courses that enable students to quickly gain insights in these critical disciplines.