The rapid digital transformation of Fourth Industrial Revolution (4IR) systems is reshaping workforce needs, widening skill gaps, especially for older workers. With growing emphasis on STEM skills such as robotics, automation, artificial intelligence (AI), and security, large-scale re-skilling and up-skilling are required. Training programs must address diverse backgrounds, learning styles, and motivations to improve persistence and success, while ensuring rapid, cost-effective workforce development through experiential learning. To meet these challenges, we present an adaptive tutoring framework that combines generative AI with Retrieval-Augmented Generation (RAG) to deliver personalized training. The framework leverages document hit rate and Mean Reciprocal Rank (MRR) to optimize content for each learner, and is benchmarked against human-generated training for alignment and relevance. We demonstrate the framework in 4IR cybersecurity learning by creating a synthetic QA dataset emulating trainee behavior, while RAG is tuned on curated cybersecurity materials. Evaluation compares its generated training with manually curated queries representing realistic student interactions. Responses are produced using large language models (LLMs) including GPT-3.5 and GPT-4, assessed for faithfulness and content alignment. GPT-4 achieves the best performance with 87% relevancy and 100% alignment. Results show this dual-mode approach enables the adaptive tutor to act as both a personalized topic recommender and content generator, offering a scalable solution for rapid, tailored learning in 4IR education and workforce development.

A Human-in-the-Loop (HITL) approach leverages generative AI to enhance personalized learning by directly integrating student feedback into AI-generated solutions. Students critique and modify AI responses using predefined feedback tags, fostering deeper engagement and understanding. This empowers students to actively shape their learning, with AI serving as an adaptive partner. The system uses a tagging technique and prompt engineering to personalize content, informing a Retrieval-Augmented Generation (RAG) system to retrieve relevant educational material and adjust explanations in real time. This builds on existing research in adaptive learning, demonstrating how student-driven feedback loops can modify AI-generated responses for improved student retention and engagement, particularly in STEM education. Preliminary findings from a study with STEM students indicate improved learning outcomes and confidence compared to traditional AI tools. This work highlights AI's potential to create dynamic, feedback-driven, and personalized learning environments through iterative refinement.

The prevalence of hearing aids is increasing. However, optimizing the amplification processes of hearing aids remains challenging due to the complexity of integrating multiple modular components in traditional methods. To address this challenge, we present NeuroAMP, a novel deep neural network designed for end-to-end, personalized amplification in hearing aids. NeuroAMP leverages both spectral features and the listener's audiogram as inputs, and we investigate four architectures: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Convolutional Recurrent Neural Network (CRNN), and Transformer. We also introduce Denoising NeuroAMP, an extension that integrates noise reduction along with amplification capabilities for improved performance in real-world scenarios. To enhance generalization, a comprehensive data augmentation strategy was employed during training on diverse speech (TIMIT and TMHINT) and music (Cadenza Challenge MUSIC) datasets. Evaluation using the Hearing Aid Speech Perception Index (HASPI), Hearing Aid Speech Quality Index (HASQI), and Hearing Aid Audio Quality Index (HAAQI) demonstrates that the Transformer architecture within NeuroAMP achieves the best performance, with SRCC scores of 0.9927 (HASQI) and 0.9905 (HASPI) on TIMIT, and 0.9738 (HAAQI) on the Cadenza Challenge MUSIC dataset. Notably, our data augmentation strategy maintains high performance on unseen datasets (e.g., VCTK, MUSDB18-HQ). Furthermore, Denoising NeuroAMP outperforms both the conventional NAL-R+WDRC approach and a two-stage baseline on the VoiceBank+DEMAND dataset, achieving a 10% improvement in both HASPI (0.90) and HASQI (0.59) scores. These results highlight the potential of NeuroAMP and Denoising NeuroAMP to deliver notable improvements in personalized hearing aid amplification.

In recent years, large language models have demonstrated remarkable capabilities in natural language understanding and generation. However, these models often struggle with hallucinations and maintaining long term contextual relevance, particularly when dealing with private or local data. This paper presents a novel architecture that addresses these challenges by integrating an orchestration engine that utilizes multiple LLMs in conjunction with a temporal graph database and a vector database. The proposed system captures user interactions, builds a graph representation of conversations, and stores nodes and edges that map associations between key concepts, entities, and behaviors over time. This graph based structure allows the system to develop an evolving understanding of the user preferences, providing personalized and contextually relevant answers. In addition to this, a vector database encodes private data to supply detailed information when needed, allowing the LLM to access and synthesize complex responses. To further enhance reliability, the orchestration engine coordinates multiple LLMs to generate comprehensive answers and iteratively reflect on their accuracy. The result is an adaptive, privacy centric AI assistant capable of offering deeper, more relevant interactions while minimizing the risk of hallucinations. This paper outlines the architecture, methodology, and potential applications of this system, contributing a new direction in personalized, context aware AI assistance.

Despite a rich history of investigating smartphone overuse intervention techniques, AI-based just-in-time adaptive intervention (JITAI) methods for overuse reduction are lacking. We develop Time2Stop, an intelligent, adaptive, and explainable JITAI system that leverages machine learning to identify optimal intervention timings, introduces interventions with transparent AI explanations, and collects user feedback to establish a human-AI loop and adapt the intervention model over time. We conducted an 8-week field experiment (N=71) to evaluate the effectiveness of both the adaptation and explanation aspects of Time2Stop. Our results indicate that our adaptive models significantly outperform the baseline methods on intervention accuracy (>32.8\% relatively) and receptivity (>8.0\%). In addition, incorporating explanations further enhances the effectiveness by 53.8\% and 11.4\% on accuracy and receptivity, respectively. Moreover, Time2Stop significantly reduces overuse, decreasing app visit frequency by 7.0$\sim$8.9\%. Our subjective data also echoed these quantitative measures. Participants preferred the adaptive interventions and rated the system highly on intervention time accuracy, effectiveness, and level of trust. We envision our work can inspire future research on JITAI systems with a human-AI loop to evolve with users.

Earlier this year, Cochlear, the manufacturer of cochlear implants, announced a collaboration with Google and Australian Hearing Hub members, the National Acoustic Laboratories (NAL), Macquarie University, the Shepherd Centre, and NextSense. The aim is to improve existing hearing-assistance technologies, like hearing aids and cochlear implants, and to develop new solutions for folks experiencing hearing loss. There's a growing awareness that it's important to protect our hearing. Nevertheless, the world faces a hearing loss crisis. According to the World Health Organization, more than 1.5 billion people worldwide live with hearing loss today (430 million with disabling hearing loss), but it predicts that by 2050, those figures will grow to 2.5 billion and 700 million, respectively.

In the world of healthcare, the use of Artificial Intelligence (AI) is a game-changer. AI has been making waves across industries, and healthcare is no exception. It is now clear that AI has the potential to transform the way medical care is delivered, making it more personalized than ever before. By breaking down traditional barriers, AI is poised to revolutionize the healthcare industry. Personalized medical care has always been the ideal goal of healthcare providers.

Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7 percent and stopped auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants.

How AI Is Used in Education Artificial intelligence can optimize and improve any process it touches, and education is no exception. AI's decision-making capabilities introduce new possibilities to every aspect of studying. Here are some examples: Personalized learning and smart scheduling. AI makes it possible to develop a truly individualized approach. It can quickly analyze every student's learning style and preferences and then create a detailed, personalized academic plan.

Using AI in education holds many benefits for both students and teachers: One can access learning resources from anywhere, at any time. Time-consuming, tedious tasks such as record keeping or grading multiple-choice tests can be completed through Artificial Intelligence automation. Technologies like Artificial Intelligence, Data Science, Machine Learning, and more are now a part of our everyday lives. Teachers and learners are already benefitting from machine learning capabilities, improving access to information, and enhancing learning. This article features how Artificial intelligence identifies students who need extra help.