Few inventions and innovations have genuinely transformed education at large, particularly by enhancing access to knowledge. Notable among these are the advent of writing around 3300 BCE, which facilitated the transmission of knowledge across generations and cultures; the Gutenberg printing press in approximately 1440 CE, which greatly simplified the duplication and dissemination of ideas and knowledge, thereby encouraging wider literacy and education; the large-scale deployment of the World Wide Web in the late 1990s and early 2000s, which allowed for rapid, affordable, and accessible information sharing via the Internet, especially through online encyclopedias such as Wikipedia; and, more recently, the public emergence of Large Language Models (LLMs) [1] in 2022, such as ChatGPT (Chat Generative Pre-Trained Transformer) [2], which have made information access even more straightforward. However, LLMs differ from previous inventions that facilitated the spread of information and knowledge in several key ways [3, 4]. While writing, the printing press, and the Internet primarily made information more accessible, LLMs provide an array of additional functions, such as multi-language translation, summarisation, simplification of complex information, and advanced writing capabilities to structure and organise content. In other words, LLMs assist people not only with accessing information but also with tasks traditionally considered cognitive.

Canopy CMO Yaron Litwin discusses how criminals are using deepfake technology to blackmail teens and generate child pornography. Parents of girls at a New Jersey high school said their daughters were humiliated after they learned fake pornographic images of themselves generated with the use of Artificial Intelligence (AI), were circulated among classmates. Female students at Westfield High, NJ, learned about the images after observing sophomore boys whispering and acting "weird" on October 16, the Wall Street Journal reported. A few days later, one boy confessed to some of the girls that at least one student had used real photos, found on social media, to generate fake nude images of the female classmates through an AI website. The student then reportedly shared these images with other male students in a group text.

Holistically measuring societal biases of large language models is crucial for detecting and reducing ethical risks in highly capable AI models. In this work, we present a Chinese Bias Benchmark dataset that consists of over 100K questions jointly constructed by human experts and generative language models, covering stereotypes and societal biases in 14 social dimensions related to Chinese culture and values. The curation process contains 4 essential steps: bias identification via extensive literature review, ambiguous context generation, AI-assisted disambiguous context generation, snd manual review \& recomposition. The testing instances in the dataset are automatically derived from 3K+ high-quality templates manually authored with stringent quality control. The dataset exhibits wide coverage and high diversity. Extensive experiments demonstrate the effectiveness of the dataset in detecting model bias, with all 10 publicly available Chinese large language models exhibiting strong bias in certain categories. Additionally, we observe from our experiments that fine-tuned models could, to a certain extent, heed instructions and avoid generating outputs that are morally harmful in some types, in the way of "moral self-correction". Our dataset and results are publicly available at \href{https://github.com/YFHuangxxxx/CBBQ}{https://github.com/YFHuangxxxx/CBBQ}, offering debiasing research opportunities to a widened community.

Girls routinely get better grades than boys with'identical competence' because they are neater and easier to teach, a new study has revealed. Researchers from the University of Trento in Italy compared the results of almost 40,000 students aged 15 and 16 for a series of standardised tests. They found that girls are regularly marked more highly than boys, despite being of equal competence. The researchers suggest that teachers may be unconsciously rewarding students exhibiting traditionally female behaviours, such as quietness and neatness, which make teaching easier. Ilaria Lievore, a PhD candidate in Sociology, said: 'There is a strong correlation between having higher grades and desirable educational outcomes, such as gaining admission to good colleges or having a lower probability of dropping out of school.

The idea of including the topic of artificial intelligence in school curricula begins in the far north-east of Italy, specifically from the "Bunarrotti" secondary school in Monfalcone, where Dean Vincenzo Kaiko He also talked about creating a real network of schools that intend to offer educational courses to their students on this subject. Vincenzo Kaiko explains it Data science and artificial intelligence They are scientific disciplines closely related and related to other fields of knowledge such as mathematics, natural sciences, humanities, and economics, which together represent the most interesting frontier of new information and communication technologies. Integration of the study of data science and artificial intelligence into the high school track – Monfalcone School Principal adds It can allow male and female students to gain important basic knowledge in rapidly expanding fields of science and technology, both in terms of broadening their cultural background and in terms of orientation towards university studies. The study of these two disciplines also allows for logical development – mathematical skills, analytical and abstract skills, ability to solve problems and creativity, in an interdisciplinary and mutually enriching relationship both with mathematics, physics and the natural sciences, and with the humanistic disciplines." There are currently four Italian schools that have independently started secondary school curriculum studies with the aim of data science and artificial intelligence: these are Maserati High Schools in Foggera, Volta in Reggio Calabria and Galilei in Trento.

Data-driven predictive models are increasingly used in education to support students, instructors, and administrators. However, there are concerns about the fairness of the predictions and uses of these algorithmic systems. In this introduction to algorithmic fairness in education, we draw parallels to prior literature on educational access, bias, and discrimination, and we examine core components of algorithmic systems (measurement, model learning, and action) to identify sources of bias and discrimination in the process of developing and deploying these systems. Statistical, similarity-based, and causal notions of fairness are reviewed and contrasted in the way they apply in educational contexts. Recommendations for policy makers and developers of educational technology offer guidance for how to promote algorithmic fairness in education.

The Duke University professor was at a statistics conference last year when, she said, she witnessed Steven Scott, a senior artificial intelligence (AI) researcher at Google, make sexual advances on one of her female students. According to Heller, when she spoke to Scott later at an event dinner, he was defensive and told the professor that she should be nice to him considering that he had secured her a Google-funded faculty research award. Artificial Intelligence has various definitions, but in general it means a program that uses data to build a model of some aspect of the world. This model is then used to make informed decisions and predictions about future events. The technology is used widely, to provide speech and face recognition, language translation, and personal recommendations on music, film and shopping sites.

Veronica Rivera signed up for the introduction to computer science class at Harvey Mudd College mostly because she had no choice: It was mandatory. Programming was intimidating and not for her, she thought. She expected the class to be full of guys who loved video games and grew up obsessing over how they were made. There were plenty of those guys but, to her surprise, she found the class fascinating. She learned how to program a computer to play "Connect Four" and wrote algorithms that could recognize lines of Shakespeare and generate new text with similar sentence patterns. When that first class ended, she signed up for the next level, then another and eventually declared a joint major of computer science and math.