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.

In this paper, we introduce Rule-Guided Feedback (RGF), a framework designed to enhance Large Language Model (LLM) performance through structured rule adherence and strategic information seeking. RGF implements a teacher-student paradigm where rule-following is forced through established guidelines. Our framework employs a Teacher model that rigorously evaluates each student output against task-specific rules, providing constructive guidance rather than direct answers when detecting deviations. This iterative feedback loop serves two crucial purposes: maintaining solutions within defined constraints and encouraging proactive information seeking to resolve uncertainties. We evaluate RGF on diverse tasks including Checkmate-in-One puzzles, Sonnet Writing, Penguins-In-a-Table classification, GSM8k, and StrategyQA. Our findings suggest that structured feedback mechanisms can significantly enhance LLMs' performance across various domains.

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."

Almost immediately, educators began experimenting with the tool. While the bot's answers to academic questions weren't perfect, they were awfully close to what teachers would expect from many of their students. How long, educators wonder, will it be before students begin using the site to write essays or computer code for them?

Feature As word of students using AI to automatically complete essays continues to spread, some lecturers are beginning to rethink how they should teach their pupils to write. Writing is a difficult task to do well. The best novelists and poets write furiously, dedicating their lives to mastering their craft. The creative process of stringing together words to communicate thoughts is often viewed as something complex, mysterious, and unmistakably human. No wonder people are fascinated by machines that can write too.

"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.

While AI-powered solutions have been in the EdTech industry for some time, the pandemic drastically shifted the landscape, forcing educators/learners to rely on technology more than ever before. AI technologies have the power to optimise both learning and teaching, thus paving the way for the education sector to evolve. Here are a few points on how AI in education can and will shape and define the teaching-learning ecosystem and experience for the future. Routine chores such as grading, evaluating, filing paperwork, making progress reports and organizing resources for lectures will get automated by AI, reducing the time-to-task of teachers and freeing up time for developing students' higher-order thinking and skills. There are already adaptive learning software and digitised programs for students.

PlayVS, which first started in 2018 and has since raised more than $106 million in venture capital, holds commercial licenses for nine games, marketing itself as a "turnkey" solution to esports. In 2018, the company started a contract with the streaming network for the National Federation of State High School Associations (NFHS), a rulemaking body in scholastic sports, to be the organization's platform for esports competitions. At the time, PlayVS was a three-person start-up. Now, the company employs more than 100 people and has operating contracts with 21 state athletic associations affiliated with the NFHS, along with a number of groups outside of the federation, according to the PlayVS website.