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Automated planning is traditionally the domain of experts, utilized in fields like manufacturing and healthcare with the aid of expert planning tools. Recent advancements in LLMs have made planning more accessible to everyday users due to their potential to assist users with complex planning tasks. However, LLMs face several application challenges within end-user planning, including consistency, accuracy, and user trust issues. This paper introduces VeriPlan, a system that applies formal verification techniques, specifically model checking, to enhance the reliability and flexibility of LLMs for end-user planning. In addition to the LLM planner, VeriPlan includes three additional core features—a rule translator, flexibility sliders, and a model checker—that engage users in the verification process. Through a user study (𝑛 = 12), we evaluate VeriPlan, demonstrating improvements in the perceived quality, usability, and user satisfaction of LLMs. Our work shows the effective integration of formal verification and user-control features with LLMs for end-user planning tasks. 

The widespread adoption of Large Language Models (LLMs) and LLM-powered agents in multi-user settings underscores the need for reliable, usable methods to accommodate diverse preferences and resolve conflicting directives. Drawing on conflict resolution theory, we introduce a user-centered workflow for multi-user personalization comprising three stages: Reflection, Analysis, and Feedback. We then present MAP—a Multi-Agent system for multi-user Personalization—to operationalize this workflow. By delegating subtasks to specialized agents, MAP (1) retrieves and reflects on relevant user information, while enhancing reliability through agent-toagent interactions, (2) provides detailed analysis for improved transparency and usability, and (3) integrates user feedback to iteratively refine results. Our user study findings (𝑛 = 12) highlight MAP’s effectiveness and usability for conflict resolution while emphasizing the importance of user involvement in resolution verification and failure management. This work highlights the potential of multi-agent systems to implement user-centered, multi-user personalization workflows and concludes by offering insights for personalization in multi-user contexts. 

Intergenerational co-creation using technology between grandparents and grandchildren can be challenging due to differences in technological familiarity. AI has emerged as a promising tool to support co-creative activities, offering flexibility and creative assistance, but its role in facilitating intergenerational connection remains underexplored. In this study, we conducted a user study with 29 grandparent-grandchild groups engaged in AI-supported story creation to examine how AI-assisted co-creation can foster meaningful intergenerational bonds. Our findings show that grandchildren managed the technical aspects, while grandparents contributed creative ideas and guided the storytelling. AI played a key role in structuring the activity, facilitating brainstorming, enhancing storytelling, and balancing the contributions of both generations. The process fostered mutual appreciation, with each generation recognizing the strengths of the other, leading to an engaging and cohesive co-creation process. We offer design implications for integrating AI into intergenerational co-creative activities, emphasizing how AI can enhance connection across skill levels and technological familiarity. 

With the increasing prevalence of online learning, adapting education to diverse learner needs remains a persistent challenge. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), promise powerful tools and capabilities to enhance personalized learning in online educational environments. In this work, we explore how LLMs can improve personalized learning experiences by catering to individual user needs toward enhancing the overall quality of online education. We designed personalization guidelines based on the growing literature on personalized learning to ground LLMs in generating tailored learning plans. To operationalize these guidelines, we implemented LearnMate, an LLM-based system that generates personalized learning plans and provides users with real-time learning support. We discuss the implications and future directions of this work, aiming to move beyond the traditional one-size-fits-all approach by integrating LLM-based personalized support into online learning environments. 

Objective: Physical and cognitive workloads and performance were studied for a corrective shared control (CSC) human–robot collaborative (HRC) sanding task. Background: Manual sanding is physically demanding. Collaborative robots (cobots) can potentially reduce physical stress, but fully autonomous implementation has been particularly challenging due to skill, task variability, and robot limitations. CSC is an HRC method where the robot operates semiautonomously while the human provides real-time corrections. Methods: Twenty laboratory participants removed paint using an orbital sander, both manually and with a CSC robot. A fully automated robot was also tested. Results: The CSC robot improved subjective discomfort compared to manual sanding in the upper arm by 29.5%, lower arm by 32%, hand by 36.5%, front of the shoulder by 24%, and back of the shoulder by 17.5%. Muscle fatigue measured using EMG, was observed in the medial deltoid and flexor carpi radialis for the manual condition. The composite cognitive workload on the NASA-TLX increased by 14.3% for manual sanding due to high physical demand and effort, while mental demand was 14% greater for the CSC robot. Digital imaging showed that the CSC robot outperformed the automated condition by 7.16% for uniformity, 4.96% for quantity, and 6.06% in total. Conclusions: In this example, we found that human skills and techniques were integral to sanding and can be successfully incorporated into HRC systems. Humans performed the task using the CSC robot with less fatigue and discomfort. Applications: The results can influence implementation of future HRC systems in manufacturing environments. 

Physics-based simulations are essential for designing autonomous construction equipment, but preparing models is time-consuming, requiring the integration of mechanical and geometric data. Current automatic modeling methods for modular robots are inadequate for construction equipment. This paper explores automating the modeling process by integrating mechanical data into 3D computer-aided design (CAD) models. A template library is developed with hierarchy and joint templates specific for equipment. During model generation, appropriate templates are selected based on the equipment type. Unspecified joint template data is extracted from technical specifications using a large language model (LLM). The 3D CAD model is then converted into a Universal Scene Description (USD) model. Users can adjust the part names and hierarchy within the USD model to align with the hierarchy template, and joint data is automatically integrated, resulting in a simulation-ready model. This method reduces modeling time by over 87 % compared to manual methods, while maintaining accuracy. 

The integration of deep learning (DL) into construction applications holds substantial potential for enhancing construction automation and intelligence. However, successful implementation of DL necessitates the acquisition of substantial data for training. The acquisition process can be error-prone, time-consuming, and impractical. For this reason, synthetic simulated data (SSD) has emerged as a promising alternative. While various strategies have been developed to generate such data, a systematic review and evaluation are lacking to aid researchers and professionals in selecting appropriate strategies for their applications. To fill this gap, this paper conducts a comprehensive literature review related to SSD generation and applications, and develops a guideline for strategy selection. Two hundred and eight articles are identified from the academic database Web of Science by using PRISMA. After thoroughly analyzing the literature, seven SSD generation strategies are identified and evaluated across six metrics. Based on the performance of each strategy, a guideline is synthesized as a decision tree. Users only need to follow the steps and answer the questions in the decision tree, and then they will get the recommended SSD generation strategy. We demonstrate the guideline’s effectiveness by comparing its recommendations with the strategies chosen by researchers in existing DL construction applications and achieve a matching rate of 82%. 

Collaborative robots (cobots) are increasingly utilized within the manufacturing industry. However, despite the promise of collaboration and easier programming when compared to traditional industrial robots, cobots introduce new interaction paradigms that require more thought about the environment and distribution of tasks to fully realize their collaboration capabilities. Due to these additional requirements, these collaboration capabilities are underutilized in current manufacturing. Therefore, to make cobots more accessible and easy to use, new systems need to be developed that support users during interaction. In this research, we propose a set of tools that target cobot use for multiple groups of individuals that use them, to better support users and simplify cobot collaboration. 

Robots are ubiquitous in small-to-large-scale manufacturers. While collaborative robots (cobots) have significant potential in these settings due to their flexibility and ease of use, proper integration is critical to realize their full potential. Specifically, cobots need to be integrated in ways that utilize their strengths, improve manufacturing performance, and facilitate use in concert with human workers. Efective integration requires careful consideration and the knowledge of roboticists, manufacturing engineers, and business administrators. We propose an approach involving the stages of planning, analysis, development, and presentation, to inform manufacturers about cobot integration within their facilities prior to the integration process. We contextualize our approach in a case study with an SME collaborator and discuss insights learned. 

Handheld kinesthetic haptic interfaces can provide greater mobility and richer tactile information as compared to traditional grounded devices. In this paper, we introduce a new handheld haptic interface which takes input using bidirectional coupled finger flexion. We present the device design motivation and design details and experimentally evaluate its performance in terms of transparency and rendering bandwidth using a handheld prototype device. In addition, we assess the device's functional performance through a user study comparing the proposed device to a commonly used grounded input device in a set of targeting and tracking tasks.