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In the quest for equitable resource distribution within food banks and their partner agencies, understanding the dependencies of these agencies on food banks emerges as a critical factor. This study investigates the intricate dynamics influencing agency dependency ratios, exploring the complex factors that shape the demand for food resources. Leveraging historical self-reported dependency ratio data, this preliminary study employs predictive modeling using Multiple Linear Regression to forecast agency dependencies on food banks. The primary objective is to discern the underlying factors that significantly impact agency dependency ratios. Employing Least Absolute Shrinkage and Selection Operator (LASSO) as a feature selection technique, the study identifies the key variables that capture the essence of the dataset. Identifying the variables that contribute the most to the model paves the way for robust predictive modeling. This study offers a comprehensive approach to understanding and predicting agency dependencies on food banks. The findings hold significant implications for non-profit hunger relief organizations, aiding in strategic decision- making for equitable resource distribution. 

AI, robotics, and automation are reshaping many industries, including the Architecture, Engineering, and Construction (AEC) industries. For students aiming to enter these evolving fields, comprehensive and accessible training in high-tech roles is becoming increasingly important. Traditional robotics education, while often effective, usually necessitates small class sizes and specialized equipment. On-the-job training introduces safety risks, particularly for inexperienced individuals. The integration of advanced technologies for training presents an alternative that reduces the need for extensive physical resources and minimizes safety concerns. This paper introduces the Intelligent Learning Platform for Robotics Operations (IL-PRO), an innovative project that integrates the use of Artificial Intelligence (AI), Virtual Reality (VR), and game-assisted learning for teaching robotic arms operations. The goal of this project is to address the limitations of traditional training through the implementation of personalized learning strategies supported by Adaptive Learning Systems (ALS). These systems hold the potential to transform education by customizing content to cater to various levels of understanding, preferred learning styles, past experiences, and diverse linguistic and socio-cultural backgrounds.Central to IL-PRO is the development of its ALS, which uses student progress variables and multimodal machine learning to infer students’ level of understanding and automate task and feedback delivery. The curriculum is organized into modules, starting with fundamental robotic concepts, and advancing to complex motion planning and programming. The curriculum is guided by a learner model that is continuously refined through data collection. Furthermore, the project incorporates gaming elements into its VR learning approach to create an engaging educational environment. Thus, the learning content is designed to engage students with simulated robots and input devices to solve sequences of game-based challenges. The challenge sequences are designed similarly to levels in a game, each with increasing complexity, in order to systematically incrementally build students' knowledge, skills, and confidence in robotic operations. The project is conducted by a team of interdisciplinary faculty from Florida International University (FIU), the University of California Irvine (UCI), the University of Hawaii (UH) and the University of Kansas-Missouri (UKM). The collaboration between these institutions enables the sharing of resources and expertise that are essential for the development of this comprehensive learning platform. 

The increasing environmental concerns call for more sophisticated and integrated educational methods. For sustainable outcomes, understanding and navigating complex environmental factors is essential. By imparting knowledge about environmental data and its applications, students can be better prepared to address environmental issues.The Augmented Learning for Environmental Robotics Technologies (ALERT) program introduces an educational method using augmented reality (AR) and artificial intelligence (AI). It provides students, particularly those in architecture, engineering, and construction (AEC), with an immersive learning experience focused on environmental data and robotics. Considering the significant environmental footprint of the AEC sector—emanating from energy-intensive buildings, roads, and infrastructures—the ALERT initiative strives to instill a comprehensive understanding of environmental data collection and visualization. This is done with the aim of promoting data-centric design and construction for a more eco-friendly built environment.In the ALERT program, AR is employed to fashion an augmented learning space where students can engage with both real-time and past environmental data. They learn to set up environmental sensors, collect data, and visualize it to unearth hidden trends and connections. Additionally, AI ensures a tailored learning journey for each student, offering optimal challenges and support. This innovative blend of AR and AI not only offers an enriching learning experience but also prepares AEC students to be at the forefront of transformative shifts, especially those influenced by advancements like robotic automation, fostering a profound understanding of environmental data.This paper outlines the preliminary stages of the ALERT project, detailing its foundational research. Topics include the educational theories guiding the creation of a groundbreaking Intelligent Learning System (ILS) and curriculum, as well as the projected impact of the program. ALERT emerges as a promising venture, potentially empowering students with the expertise to reduce the ecological footprint of infrastructure, paving the way for a greener future.