As Computational Thinking (CT) continues to permeate younger age groups in K-12 education, established CT platforms such as Scratch face challenges in catering to these younger learners, particularly those in the elementary school (ages 6-12). Through formative investigation with Scratch experts, we uncover three key obstacles to children's autonomous Scratch learning: artist's block in project planning, bounded creativity in asset creation, and inadequate coding guidance during implementation. To address these barriers, we introduce ChatScratch, an AI-augmented system to facilitate autonomous programming learning for young children. ChatScratch employs structured interactive storyboards and visual cues to overcome artist's block, integrates digital drawing and advanced image generation technologies to elevate creativity, and leverages Scratch-specialized Large Language Models (LLMs) for professional coding guidance. Our study shows that, compared to Scratch, ChatScratch efficiently fosters autonomous programming learning, and contributes to the creation of high-quality, personally meaningful Scratch projects for children.

Our focus is on projects, i.e., business processes, which are emerging as the economic drivers of our times. Differently from day-to-day operational processes that do not require detailed planning, a project requires planning and resource-constrained scheduling for coordinating resources across sub- or related projects and organizations. A planner in charge of project planning has to select a set of activities to perform, determine their precedence constraints, and schedule them according to temporal project constraints. We suggest a data-driven project planning approach for classes of projects such as infrastructure building and information systems development projects. A project network is first learned from historical records. The discovered network relaxes temporal constraints embedded in individual projects, thus uncovering where planning and scheduling flexibility can be exploited for greater benefit. Then, the network, which contains multiple project plan variations, from which one has to be selected, is enriched by identifying decision rules and frequent paths. The planner can rely on the project network for: 1) decoding a project variation such that it forms a new project plan, and 2) applying resource-constrained project scheduling procedures to determine the project's schedule and resource allocation. Using two real-world project datasets, we show that the suggested approach may provide the planner with significant flexibility (up to a 26% reduction of the critical path of a real project) to adjust the project plan and schedule. We believe that the proposed approach can play an important part in supporting decision making towards automated data-driven project planning.

The first question pops into our mind is "what is digital twin technology?" A virtual replica of a tangible object is called a "digital twin." It could be anything basic like a piece of furniture or something as complex as an automobile or a manufacturing production line. All the components of the object are simulated by the digital twin to provide a virtual proxy. What advantages do digital twin offer?

Hive, the productivity platform provider, today announced the public release of HiveMind that uses Artificial Intelligence (AI) to automatically create a project plan in a matter of seconds. As Artificial Intelligence models are increasingly being integrated into content and note-taking platforms, Hive is pioneering the usage of the models' capacity for continuous learning and logical decision-making based on in-depth data. Modeled on six years of successful customer projects, HiveMind automatically sets out the steps to accomplish any goal, expediting project planning and execution. It has the ability to create project tasks based on simple suggestions, set next steps from received emails and reply based on the inbound email's content. "Today, superior performance in the marketplace comes from the depth of data you possess, and the ability to apply it quickly," said John Furneaux, Hive co-founder and CEO.

Artificial intelligence explained: This article describes and explains the concepts and terminology behind what is today being termed as artificial intelligence. Further, it illustrates how these concepts relate to the field of project management, offering opportunity for better, more effective project planning and control. There are many definitions of artificial intelligence or AI. One of the funniest definitions I have run across is "AI is whatever hasn't been done yet" -- now there's a vague and unhelpful answer! One of the more useful definitions I have found is "AI is the ability of a computer program or a machine to think and learn. In general use, the term "artificial intelligence" means a machine which mimics human cognition."

While artificial intelligence (AI) is already effectively assisting human developers at every level of the development process, software development will only get better as it is about to undergo a huge change. Artificial intelligence is revolutionizing the way developers work, resulting in significant productivity, quality and speed increases. Everything -- from project planning and estimation to quality testing and the user experience -- can benefit from AI algorithms. AI will undoubtedly impact how developers create applications and how users interact with them in the modern environment. As organizations become more interested in AI technologies, artificial intelligence will certainly affect the future of software development.

As we look ahead to 2020, it's time to embrace the fact that traditional project planning tools must evolve to meet the changing needs of planners and help them work smarter, not harder. Today's advanced planning solutions that incorporate both artificial and human intelligence enable planners to work more efficiently and create more achievable plans. The current state of project planning needs an overhaul, and I believe it should be in the form of what's called predictive planning. Think about it: You don't have to do things like manually check your Word document for spelling errors, explicitly type an entire recipient's email address, or calculate the miles of a trip every time, do you? That's because today's computers can store and, more importantly, recall knowledge.

Contractors and major construction companies are each trying to achieve broadly the same business objectives, like streamlining projects, mitigating risks (financial and human), saving time and costs, and ensuring that planning and project engineering are accurate. The tools many use can be very different, irrespective of role in the process. It's a surprising fact that even some globe-straddling multinational construction conglomerates run most of their daily operations on Microsoft Excel, given the well-publicized inherent inaccuracy of that particular platform. Is it also surprising to find a tiny contracting company in a niche area of construction running a cutting-edge SaaS to ensure its operations are run smoothly? Project management, engineering, and construction in general involve huge numbers of variables that make ensuring efficient levels of productivity and progress extraordinarily tricky.

To date, pavement management software products and studies on optimizing the prioritization of pavement maintenance and rehabilitation (M&R) have been mainly focused on three parameters; the pre-treatment pavement condition, the rehabilitation cost, and the available budget. Yet, the role of the candidate projects' spatial characteristics in the decision-making process has not been deeply considered. Such a limitation, predominately, allows the recommended M&R projects' schedule to involve simultaneously running but spatially scattered construction sites, which are very challenging to monitor and manage. This study introduces a novel approach to integrate pavement segments' spatial coordinates into the M&R prioritization analysis. The introduced approach aims at combining the pavement segments with converged spatial coordinates to be repaired in the same timeframe without compromising the allocated budget levels or the overall target Pavement Condition Index (PCI). Such a combination would result in minimizing the routing of crews, materials and other equipment among the construction sites and would provide better collaborations and communications between the pavement maintenance teams. Proposed herein is a novel spatial clustering algorithm that automatically finds the projects within a certain budget and spatial constrains. The developed algorithm was successfully validated using 1,800 pavement maintenance projects from two real-life examples of the City of Milton, GA and the City of Tyler, TX.

The infrastructure and construction industry is undergoing a lot of seismic transformations that will change its essential character and redefine the industry – which has conventionally traversed at a snail's pace in incorporating innovative technology – to maximize utility, boost productivity and streamline delivery. BIM is already playing a crucial role in this drastic transformation of the construction industry and making it more efficient, reliable, accurate and cost-effective as well as sustainable. The further digitalization and automation of the construction industry would involve synchronization of Artificial Intelligence (AI) and BIM. As per a recent study by McKinsey, there is both tremendous scope and an unparalleled demand for specific technology solutions based on AI-powered algorithms and analytic methods. AI would assist the construction industry in combatting some of the biggest recurring challenges that it has to face, including project schedule delay, accuracy margin, and safety considerations.