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Artificial intelligence (AI) is transforming elementary STEM education, yet evidence remains fragmented. This systematic review synthesizes 258 studies (2020-2025) examining AI applications across eight categories: intelligent tutoring systems (45% of studies), learning analytics (18%), automated assessment (12%), computer vision (8%), educational robotics (7%), multimodal sensing (6%), AI-enhanced extended reality (XR) (4%), and adaptive content generation. The analysis shows that most studies focus on upper elementary grades (65%) and mathematics (38%), with limited cross-disciplinary STEM integration (15%). While conversational AI demonstrates moderate effectiveness (d = 0.45-0.70 where reported), only 34% of studies include standardized effect sizes. Eight major gaps limit real-world impact: fragmented ecosystems, developmental inappropriateness, infrastructure barriers, lack of privacy frameworks, weak STEM integration, equity disparities, teacher marginalization, and narrow assessment scopes. Geographic distribution is also uneven, with 90% of studies originating from North America, East Asia, and Europe. Future directions call for interoperable architectures that support authentic STEM integration, grade-appropriate design, privacy-preserving analytics, and teacher-centered implementations that enhance rather than replace human expertise.

Malicious URLs pose significant security risks as they facilitate phishing attacks, distribute malware, and empower attackers to deface websites. Blacklist detection methods fail to identify new or obfuscated URLs because they depend on pre-existing patterns. This work presents a hybrid deep learning model named GNN-GAT-LSTM that combines Graph Neural Networks (GNNs) with Graph Attention Networks (GATs) and Long Short-Term Memory (LSTM) networks. The proposed architecture extracts both the structural and sequential patterns of the features from data. The model transforms URLs into graphs through a process where characters become nodes that connect through edges. It applies one-hot encoding to represent node features. The model received training and testing data from a collection of 651,191 URLs, which were classified into benign, phishing, defacement, and malware categories. The preprocessing stage included both feature engineering and data balancing techniques, which addressed the class imbalance issue to enhance model learning. The GNN-GAT-LSTM model achieved outstanding performance through its test accuracy of 0.9806 and its weighted F1-score of 0.9804. It showed excellent precision and recall performance across most classes, particularly for benign and defacement URLs. Overall, the model provides an efficient and scalable system for detecting malicious URLs while demonstrating strong potential for real-world cybersecurity applications.

One of the major problems in Machine Learning (ML) and Artificial Intelligence (AI) is the fact that the probability distribution of the test data in the real world could deviate substantially from the probability distribution of the training data set. When this happens, the predictions of an ML system or an AI agent could involve large errors which is very troublesome and undesirable. While this is a well-known hard problem plaguing the AI and ML systems' accuracy and reliability, in certain applications such errors could be critical for safety and reliability of AI and ML systems. One approach to deal with this problem is to monitor and measure the deviation in the probability distribution of the test data in real time and to compensate for this deviation. In this paper, we propose and explore the use of Kolmogorov-Smirnov (KS) Test for measuring the distribution shift and we show how the KS distance can be used to quantify the distribution shift and its impact on an AI agent's performance. Our results suggest that KS distance could be used as a valuable statistical tool for monitoring and measuring the distribution shift. More specifically, it is shown that even a distance of KS=0.02 could lead to about 50\% increase in the travel time at a single intersection using a Reinforcement Learning agent which is quite significant. It is hoped that the use of KS Test and KS distance in AI-based smart transportation could be an important step forward for gauging the performance degradation of an AI agent in real time and this, in turn, could help the AI agent to cope with the distribution shift in a more informed manner.

Legal experts analyze the challenging defense strategy for Tyler Robinson, who allegedly shot Charlie Kirk at Utah Valley University, as prosecutors prepare evidence for trial.

Abstract--The use of Machine Learning (ML) and Artificial Intelligence (AI) in smart transportation networks has increased significantly in the last few years. Among these ML and AI approaches, Reinforcement Learning (RL) has been shown to be a very promising approach by several authors. However, a problem with using Reinforcement Learning in Traffic Signal Control is the reliability of the trained RL agents due to the dynamically changing distribution of the input data with respect to the distribution of the data used for training. This presents a major challenge and a reliability problem for the trained network of AI agents and could have very undesirable and even detrimental consequences if a suitable solution is not found. Several researchers have tried to address this problem using different approaches. In particular, Meta Reinforcement Learning (Meta RL) promises to be an effective solution. In this paper, we evaluate and analyze a state-of-the-art Meta RL approach called MetaLight and show that, while under certain conditions MetaLight can indeed lead to reasonably good results, under some other conditions it might not perform well (with errors of up to 22%), suggesting that Meta RL schemes are often not robust enough and can even pose major reliability problems. As cities become more populated and the number of vehicles on their roads increases, the problem of efficiently controlling the flow of vehicles to reduce travel times and CO2 emissions is becoming a top priority. For this reason, in recent years, research in Traffic Signal Control has gained significant momentum as the quest to develop better Traffic Signal Control algorithms intensified. Specifically, Deep Reinforcement Learning (Deep RL) gained much attention in the research community as it better captures the sequential decision-making nature of the problem.

In the indictment, prosecutors claim Tyler Robinson planned Kirk's killing in advance, citing rooftop surveillance, engraved bullets, and a written note as they seek the death penalty. A TV monitor displays a picture of Tyler Robinson, a suspect in the killing of Charlie Kirk in Orem, Utah. Utah County prosecutors on Tuesday charged Tyler Robinson in the shooting death of conservative activist Charlie Kirk at Utah Valley University, a murder officials say was politically motivated. They intend to seek the death penalty. Utah County Attorney Jeff Gray announced the indictment at a midday news conference, listing charges of aggravated murder, felony discharge of a firearm causing serious bodily injury, and commission of a violent offense in the presence of a child.

Plus: ICE deploys secretive phone surveillance tech, officials warn of Chinese surveillance tools in US highway infrastructure, and more. Right-wing internet personality and Turning Point USA cofounder Charlie Kirk was shot and killed on Wednesday during a speaking engagement at Utah Valley University in Orem, Utah. After a chaotic 24-hour manhunt, the FBI named 22-year-old Utah resident Tyler Robinson as a suspect in the murder. As polarization and political violence continues to increase in the US, a new platform from the Public Service Alliance is offering tools like data-removal services and threat monitoring to public servants who increasingly need to defend themselves and their data. Meanwhile, new research this week warned that the number of US investors putting money into invasive commercial spyware rose significantly in 2024.

Lawmakers pressure social media companies to remove Charlie Kirk assassination footage as platforms struggle with content moderation of graphic violence.

Red-flag speech Charlie Kirk's'assassin' gave at family dinner Stephen King deletes post and apologizes for his'lies' about Charlie Kirk Fired MSNBC analyst DEFENDS vile Charlie Kirk comments as network bosses address'unacceptable' on-air remarks Grandma of Charlie Kirk's'assassin' breaks silence to reveal why the FBI MUST have the wrong man Denise Richards' ex Aaron Phypers shares photos of their $3.5M mansion that is CLUTTERED with junk I ran the Trump shooting task force. I was shocked by all the security blunders at Charlie Kirk's event... here are the crucial steps they missed Outrage erupts after Amazon sells book about Charlie Kirk's assassination HOURS after the shooting SCOTT JENNINGS: My friend Charlie Kirk is dead because of the Left's hateful speech... and they dare tell us to be silent. Tearful meteorologist's emotional plea to fans as it is revealed she's been laid off Shoppers rushing to Dollar Tree for $1.50 bargain they say looks straight out of Williams Sonoma AMANDA PLATELL: William and Kate have become the Prince and Princess of Boring. This is why they need to stop skulking around and take a leaf out of Harry's book - or risk Charles's wrath'They could be married before Christmas': Chris Hughes' father reveals what the family really think of his son's controversial relationship with JoJo Siwa... and why couple could be about to take huge step Bannon torches Kash Patel for undeserved'glazing' after Kirk killer turned HIMSELF in Outrage erupts after Amazon sells book about Charlie Kirk's assassination HOURS after the shooting Outrage and online conspiracy theories spread after a book about Charlie Kirk's assassination was listed for sale on Amazon. On Wednesday, social media users shared images of a book available on Amazon titled ' The Shooting of Charlie Kirk: A Comprehensive Account of the Utah Valley University Attack, the Aftermath, and America's Response.'