Things to Do in L.A. Tap to enable a layout that focuses on the article. What counts as cheating with AI? Teachers are grappling with how to draw the line This is read by an automated voice. Please report any issues or inconsistencies here . Teachers say AI cheating is "off the charts," but research shows cheating rates remain unchanged since before ChatGPT. Schools favor "AI literacy" and redesigning assignments to encourage ethical technology use.

The emergence of generative AI, particularly large language models (LLMs), has opened the door for student-centered and active learning methods like project-based learning (PBL). However, PBL poses practical implementation challenges for educators around project design and management, assessment, and balancing student guidance with student autonomy. The following research documents a co-design process with interdisciplinary K-12 teachers to explore and address the current PBL challenges they face. Through teacher-driven interviews, collaborative workshops, and iterative design of wireframes, we gathered evidence for ways LLMs can support teachers in implementing high-quality PBL pedagogy by automating routine tasks and enhancing personalized learning. Teachers in the study advocated for supporting their professional growth and augmenting their current roles without replacing them. They also identified affordances and challenges around classroom integration, including resource requirements and constraints, ethical concerns, and potential immediate and long-term impacts. Drawing on these, we propose design guidelines for future deployment of LLM tools in PBL.

Knowledge distillation (KD) improves the performance of a low-complexity student model with the help of a more powerful teacher. The teacher in KD is a black-box model, imparting knowledge to the student only through its predictions. This limits the amount of transferred knowledge. In this work, we introduce a novel Knowledge Explaining Distillation (KED) framework, which allows the student to learn not only from the teacher's predictions but also from the teacher's explanations. We propose a class of superfeature-explaining teachers that provide explanation over groups of features, along with the corresponding student model. We also present a method for constructing the superfeatures. We then extend KED to reduce complexity in convolutional neural networks, to allow augmentation with hidden-representation distillation methods, and to work with a limited amount of training data using chimeric sets. Our experiments over a variety of datasets show that KED students can substantially outperform KD students of similar complexity.

OpenAI released ChatGPT in November 2022. Since then, it's generated a lot of hype, debate, and fear-mongering about the continued rise of artificially intelligent systems in creative industries. In December, Stack Overflow banned it for consistently giving incorrect answers to programming questions. Even OpenAI's CEO Sam Altman doesn't think it's that good; he tweeted last month that "ChatGPT is incredibly limited, but good enough at some things to create a misleading impression of greatness," and that it's "a mistake to be relying on it for anything important right now."

"Effective teaching may be the hardest job there is," said American psychiatrist William Glasser. Indeed, teachers bear the brunt of shaping the formative years of every child, a high-stakes role in society that trickles down into the success of every other profession and industry. But as the skills required of the worker today become increasingly diverse, teaching has also become more complex. This has made teaching more stressful than ever before. In September 2021, more than 80 per cent of teachers reported having their mental health negatively impacted, with 80.6 per cent indicating they worked more than 45 hours a week, reported The Straits Times.

Schools around the world are slowly adopting artificial intelligence to better tailor teaching to individual kids. Critics are skeptical that this technology is as effective as promised. Could surveilling students in this way do more harm than good?

The A-level and GCSE exams fiasco exposes the significant problems in using algorithms to make complicated, life-changing decisions. As we rely more on AI and automation, "smart" systems are as likely to create as many problems as they solve. AI is not all bad nor will recent problems pause its growth, rather organisations continue to hit the accelerator on AI technologies on the promise of rapid delivery of value. So, does a fast percolation of AI technologies throughout business and public sector bodies mean we can dispose of the human talent in their analytics teams? No. Experience shows people can comfortably integrate new technologies into their jobs and lives; AI and automation are no exception.

Computers are devices that can be instructed to carry out arbitrary sequences of arithmetic or logical operations automatically. Their ability to follow generalized sets of operations, which are called programs, enables them to perform an extremely wide range of tasks. Take a look below for 30 more fun and interesting facts about computers. It was designed to protect their research work. Based on the abacus, which was a counting device invented in China over 4,000 years ago, the original computers were designed to deal with large numbers faster and more accurately than humanly possible.

Editor: Jerome Feldman's "Essay Concerning Robotic Understanding" (AI Magazine, Fall 1990) shows a remarkable naivete about humans. Although he admits to some limitations on human understanding (understanding/h): "We actually use understanding/h loosely, normally excluding infants, idiots and so on. We acknowledge that there are strong limitations on the extent to which we can convey understanding/h across barriers of gender, race and culture." If we are using understanding/h in Locke's sense, to mean reason, with all its eighteenth century freight, including the exclusion of women and blacks from the category of reasoning beings, there are, strangely enough, no barriers to this category for machines. After all, Boole later invented his logic to help mechanize the process of jurisprudence.

The RoboCupJunior division of RoboCup is now entering its third year of international participation and is growing rapidly in size and popularity. This article first outlines the history of the junior league since it was first demonstrated in Paris at RoboCup-1998 and describes how it has evolved into the international sensation it is today. Although the popularity of the event is self-evident, we are working to identify and quantify the educational benefits of the initiative. The remainder of the article focuses on describing our efforts to encapsulate these qualities, highlighting results from a pilot study conducted at RoboCupJunior-2000 and presenting new data from a subsequent study of RoboCupJunior-2001. In 1998, Lund and Pagliarini demonstrated the idea of a children's league for RoboCup, using robots constructed and programmed with the Lego Mindstorms kit that could play soccer (Land and Pagliarini 1998).