The vision of a universal AI tutor has remained elusive, despite decades of effort. Could LLMs be the game-changer? We overview novel issues arising from developing a nationwide AI tutor. We highlight the practical questions that point to specific gaps in our scientific understanding of the learning process.

The AIoT-Based Smart Education System integrates Artificial Intelligence and IoT to address persistent challenges in contemporary classrooms: attendance fraud, lack of personalization, student disengagement, and inefficient resource use. The unified platform combines four core modules: (1) a dual-factor authentication system leveraging RFID-based ID scans and WiFi verification for secure, fraud-resistant attendance; (2) an AI-powered assistant that provides real-time, context-aware support and dynamic quiz generation based on instructor-supplied materials; (3) automated test generators to streamline adaptive assessment and reduce administrative overhead; and (4) the EcoSmart Campus module, which autonomously regulates classroom lighting, air quality, and temperature using IoT sensors and actuators. Simulated evaluations demonstrate the system's effectiveness in delivering robust real-time monitoring, fostering inclusive engagement, preventing fraudulent practices, and supporting operational scalability. Collectively, the AIoT-Based Smart Education System offers a secure, adaptive, and efficient learning environment, providing a scalable blueprint for future educational innovation and improved student outcomes through the synergistic application of artificial intelligence and IoT technologies.

Grounded in critical realism and using narrative inquiry, this article explores this article explores the long - term consequences of the COVID - 19 pandemic and the rapid proliferation of artificial intelligence within higher education. Through the analysis of student narratives collected in Iranian university settings, the study reveals that learning experiences during and after the pandemic, coupled with unprepared exposure to AI tools, have generated hidden yet impactful layers of educational inequality and cognitive disorientation. These twin phenomena have not only disrupted traditio nal structures of learning but also carry the potential to deepen existing epistemic and social disparities. The article argues that the university can only fulfill its mission if it develops the epistemic tools necessary to trace layered realities and eng age seriously with the often - silenced narratives embedded in students' lived experiences. Such an approach requires a fundamental rethinking in regard to university's role in a rapidly changing educational and technological landscape.

Former Education Secretary Bill Bennett discusses the Supreme Court case that will evaluate parents' rights to opt out of classes where LGBTQ books are being used in the curriculum on'The Story.' President Donald Trump signed multiple Executive Orders relating to education Wednesday afternoon, with several tied to the theme of returning meritocracy back to the education system. The orders, seven in total, included actions to integrate artificial intelligence into K-12 school curricula, reforms to school discipline and accreditation guidelines, requirements related to the disclosure of foreign funding to schools and enhancements to the country's workforce development programs. Trump's slew of education-focused orders also included another directive demanding an end to DEI ideology in schools, specifically the use of "disparate impact theory," on top of his previous executive order from January ordering an end to DEI-like programming and ideology in K-12 schools. An Executive Order setting up a White House initiative supporting the efficiency and effectiveness of Historically Black Colleges and Universities was also signed by the president on Wednesday. President Donald Trump holds an executive order relating to education in the Oval Office of the White House, Wednesday, April 23, 2025, in Washington, as Commerce Secretary Howard Lutnick, Labor Secretary Lori Chavez-DeRemer and Education Secretary Linda McMahon watch.

The integration of new literature into the English curriculum remains a challenge since educators often lack scalable tools to rapidly evaluate readability and adapt texts for diverse classroom needs. This study proposes to address this gap through a multimodal approach that combines transformer-based text classification with linguistic feature analysis to align texts with UK Key Stages. Eight state-of-the-art Transformers were fine-tuned on segmented text data, with BERT achieving the highest unimodal F1 score of 0.75. In parallel, 500 deep neural network topologies were searched for the classification of linguistic characteristics, achieving an F1 score of 0.392. The fusion of these modalities shows a significant improvement, with every multimodal approach outperforming all unimodal models. In particular, the ELECTRA Transformer fused with the neural network achieved an F1 score of 0.996. Unimodal and multimodal approaches are shown to have statistically significant differences in all validation metrics (accuracy, precision, recall, F1 score) except for inference time. The proposed approach is finally encapsulated in a stakeholder-facing web application, providing non-technical stakeholder access to real-time insights on text complexity, reading difficulty, curriculum alignment, and recommendations for learning age range. The application empowers data-driven decision making and reduces manual workload by integrating AI-based recommendations into lesson planning for English literature.

Knowledge Tracing (KT) is a critical task in online education systems, aiming to monitor students' knowledge states throughout a learning period. Common KT approaches involve predicting the probability of a student correctly answering the next question based on their exercise history. However, these methods often suffer from performance degradation when faced with the scarcity of student interactions in new education systems. To address this, we leverage student interactions from existing education systems to mitigate performance degradation caused by limited training data. Nevertheless, these interactions exhibit significant differences since they are derived from different education systems. To address this issue, we propose a domain generalization approach for knowledge tracing, where existing education systems are considered source domains, and new education systems with limited data are considered target domains. Additionally, we design a domain-generalizable knowledge tracing framework (DGKT) that can be applied to any KT model. Specifically, we present a concept aggregation approach designed to reduce conceptual disparities within sequences of student interactions from diverse domains. To further mitigate domain discrepancies, we introduce a novel normalization module called Sequence Instance Normalization (SeqIN). Moreover, to fully leverage exercise information, we propose a new knowledge tracing model tailored for the domain generalization KT task, named Domain-Generalizable Relation-based Knowledge Tracing (DGRKT). Extensive experiments across five benchmark datasets demonstrate that the proposed method performs well despite limited training data.

Educational Data Mining has become extremely popular among researchers in last decade. Prior effort in this area was only directed towards prediction of academic performance of a student. Very less number of researches are directed towards predicting employability of a student i.e. prediction of students performance in campus placements at an early stage of enrollment. Furthermore, existing researches on students employability prediction are not universal in approach and is either based upon only one type of course or University/Institute. Henceforth, is not scalable from one context to another. With the necessity of unification, data of professional technical courses namely Bachelor in Engineering/Technology and Masters in Computer Applications students have been collected from 17 states of India. To deal with such a data, a unified predictive model has been developed and applied on 17 states datasets. The research done in this paper proves that model has universal application and can be applied to various states and institutes pan India with different cultural background and course structure. This paper also explores and proves statistically that there is no significant difference in Indian Education System with respect to states as far as prediction of employability of students is concerned. Model provides a generalized solution for student employability prediction in Indian Scenario.

To keep children from becoming the lost generation of workers, an expert has recommended that parents teach them to use AI at the age of six. Ed Broussard, Managing director at Tomoro AI, helps companies navigate a market powered by artificial intelligence and has shared skills the younger generation will need to live in a world that is quickly being engulfed by it. The AI expert has shared other skills children will need such as being able to think without the internet and focusing on jobs that do not currently exist. 'I often joke with clients, the best person to hire into their firm is the person who just cheated on their university exams using AI - they've already learned how to use AI to get great results,' said Broussard. He added" 'Employers of the future will need native AI users, where utilizing AI to work faster, better and smarter is second nature.

Six months into his first year in high school, he dropped out. For more than a year, he isolated himself in his Huntington Beach bedroom where he became addicted to video games and anonymously vented his anger online with racist and misogynistic screeds, haunted by suicidal thoughts and fantasies about hurting others. His health deteriorated as he binged on pepperoni pizza, grew obese and developed terrible rashes. Today, Chida, 38, is Gov. Gavin Newsom's chief deputy Cabinet secretary, a key member of the team building an ambitious plan to reshape public education through a 50-billion continuum of services to create a healthy foundation for children and a path to meaningful jobs at the end. Chida was the chief architect of five-year compacts with the University of California and California State University, pledging financial stability in exchange for gains in graduation rates, access and affordability.

Naivasha, Kenya – Caroline Njau comes from a family of farmers who tend to fields of maize, wheat, and potatoes in the hilly terrain near Nyahururu, 180 kilometres (112 miles) north of the capital Nairobi. But Njau has chosen a different path in life. Seated in her living room with a cup of milk tea, she labels data for artificial intelligence (AI) companies abroad on an app. The sun rises over the unpaved streets of her neighbourhood as she flicks through images of tarmac roads, intersections and sidewalks on her smartphone while carefully drawing boxes around various objects; traffic lights, cars, pedestrians, and signposts. The designer of the app – an American subcontractor to Silicon Valley companies – pays her 3 an hour.