Older adults have increasingly turned to conversational AI as a source of emotional support. However, little is known about how emotionally supportive interactions are experienced in everyday use, particularly when AI systems limit, redirect, or intervene during these interactions. We interviewed 18 older adults about their experiences using conversational AI for emotional support, examining when they turn to AI, how they engage during emotionally vulnerable moments, and how they respond when support feels disrupted. Our findings show that older adults often rely on AI when other forms of social support feel inaccessible. However, current safety-related interventions can redirect interactions in ways that participants experience as interruptions to emotional engagement or as shifts in control away from them. Such disruptions can undermine older adults’ ability to remain emotionally engaged and, in some cases, contribute to emotional distress. We discussed design implications for emotionally supportive conversational AI, emphasizing the need for safety interventions that are enacted within older adults’ social contexts, align with users’ emotional pacing, and preserve their sense of agency.

Recent Large Language Model (LLM) based AI can exhibit recognizable and measurable personality traits during conversations to improve user experience. However, as human understandings of their personality traits can be affected by their interaction partners’ traits, a potential risk is that AI traits may shape and bias users’ self-concept of their own traits. To explore the possibility, we conducted a randomized behavioral experiment. Our results indicate that after conversations about personal topics with an LLM-based AI chatbot using GPT-4o default personality traits, users’ self-concepts aligned with the AI’s measured personality traits. The longer the conversation, the greater the alignment. This alignment led to increased homogeneity in self-concepts among users. We also observed that the degree of self-concept alignment was positively associated with users’ conversation enjoyment. Our findings uncover how AI personality traits can shape users’ self-concepts through human-AI conversation, highlighting both risks and opportunities. We provide important design implications for developing more responsible and ethical AI systems.

Non-native speakers (NNSs) face significant language barriers in multilingual communication with native speakers (NSs). While AI-mediated communication (AIMC) tools offer efficient one-time assistance, they often overlook opportunities for NNSs’ continuous language acquisition. We introduce ChatLearn, an enhanced AIMC system that leverages NNSs’ communication difficulties as learning opportunities. Beyond comprehension and expression assistance, ChatLearn simultaneously captures NNSs’ language challenges, and subsequently provides them with spaced review as the conversation progresses. We conducted a mixed-methods study using a communication task with 43 NNS-NS pairs, after which ChatLearn NNSs recalled significantly more expressions than the baseline group, while there was no substantial decline in communication experience. Our findings highlight the value of contextual learning in NNS-NS communication, providing a new direction for AIMC systems that foster both immediate collaboration and continuous language development.

Chatbots are increasingly integrated into people's lives and are widely used to help people. Recently, there has also been growing interest in the reverse direction-humans help chatbots-due to a wide range of benefits including better chatbot performance, human well-being, and collaborative outcomes. However, little research has explored the factors that motivate people to help chatbots. To address this gap, we draw on the Computers Are Social Actors (CASA) framework to examine how chatbot anthropomorphism-including human-like identity, emotional expression, and non-verbal expression-influences human empathy toward chatbots and their subsequent prosocial behaviors and intentions. We also explore people's own interpretations of their prosocial behaviors toward chatbots. We conducted an online experiment (N = 244) in which chatbots made mistakes in a collaborative image labeling task and explained the reasons to participants. We then measured participants' prosocial behaviors and intentions toward the chatbots. Our findings revealed that human identity and emotional expression of chatbots increased participants' prosocial behavior and intention toward chatbots, with empathy mediating these effects. Qualitative analysis identified two motivations for participants' prosocial behaviors: empathy for the chatbot and perceiving the chatbot as human-like. We discuss the implications of these results for understanding and promoting human prosocial behaviors toward chatbots.

Non-native speakers (NNSs) often face speaking challenges in real-time multilingual communication, such as struggling to articulate their thoughts. To address this issue, we developed an AI-based speaking assistant (AISA) that provides speaking references for NNSs based on their input queries, task background, and conversation history. To explore NNSs' interaction with AISA and its impact on NNSs' speaking during real-time multilingual communication, we conducted a mixed-method study involving a within-subject experiment and follow-up interviews. In the experiment, two native speakers (NSs) and one NNS formed a team (31 teams in total) and completed two collaborative tasks-one with access to the AISA and one without. Overall, our study revealed four types of AISA input patterns among NNSs, each reflecting different levels of effort and language preferences. Although AISA did not improve NNSs' speaking competence, follow-up interviews revealed that it helped improve the logical flow and depth of their speech. Moreover, the additional multitasking introduced by AISA, such as entering and reviewing system output, potentially elevated NNSs' workload and anxiety. Based on these observations, we discuss the pros and cons of implementing tools to assist NNS in real-time multilingual communication and offer design recommendations.

Multi-agent systems, systems with multiple independent AI agents working together to achieve a common goal, are becoming increasingly prevalent in daily life. Drawing inspiration from the phenomenon of human group social influence, we investigate whether a group of AI agents can create social pressure on users to agree with them, potentially changing their stance on a topic. We conducted a study in which participants discussed social issues with either a single or multiple AI agents, and where the agents either agreed or disagreed with the user's stance on the topic. We found that conversing with multiple agents increased the social pressure felt by participants, and caused a greater shift in opinion towards the agents' stances on the conversation topics. Our study shows the potential advantages of multi-agent systems over single-agent platforms in causing opinion change. We discuss the resulting possibilities for multi-agent systems that promote social good, as well as potential malicious actors using these systems to manipulate public opinion.

Human-machine systems (HMSs) are dedicated to integrating intelligent human decisions with machine operations to achieve synergistic operational functionality. We focus on constraint-following control within the HMS, considering potential uncertainties, environmental disturbances, and limited operational space. A hierarchical hybrid control scheme is proposed, consisting of a preemption algorithm and a human decision algorithm. Specifically, the preemption algorithm relies on online state feedback from mechanical system signals, such as position and velocity, which can be implemented in hardware or software; the human decision algorithm takes inputs from electrophysiological signals or language commands. In this development, a Lagrangian density function is constructed that integrates optimal decision making with a uniformly bounded threshold. The intelligent decision-making problem in the HMS is creatively analyzed and mathematically solved leveraging variational calculus, resulting in the analytical expression of the optimal membership function associated with human decisions. Furthermore, a series of numerical simulation experiments are conducted using a bionic upper-limb prosthetic system as an example, and the comparison results demonstrate the superiority and effectiveness of the proposed method.

Ensuring safe driving and real-time execution is a crucial requirement in the motion planning process for autonomous vehicles. Hence, there is a compelling demand for advanced motion planning algorithms that exhibit effective management of inequality constraints and exceptional computational performance. This paper investigates a diffeomorphism-transformed iterative linear quadratic regulator (DTiLQR) algorithm for addressing constrained motion planning problems in autonomous vehicles with nonlinear dynamics and multiple inequality constraints. With regard to the state and input constraints, a novel state-and-input diffeomorphism is proposed to transform the constrained state/input space into an unconstrained one. Subsequently, these inequality constraints are systematically incorporated into the vehicle dynamics, thereby leading to the newly constructed system in this context. Then, we reformulate and incorporate the obstacle avoidance constraint into the objective function using state diffeomorphism and logarithmic barrier function. With this, the original optimization problem is converted to the unconstrained counterpart, adhering only to the constructed system dynamics. In this sense, featuring a streamlined single-loop architecture (which is essentially different from the dual-loop algorithmic design of existing constrained iLQR algorithms), DTiLQR is used to solve the optimization problem effectively while maintaining motion performance and constraint satisfaction for the resulting optimal trajectory. Ultimately, case studies across various driving situations showcase the effectiveness and exceptional computational efficiency of the proposed DTiLQR algorithm.

A confined space is an area in an industrial facility that has limited access and allows only restricted movement due to physical constraints. Confined spaces often require special safety precautions and may be subject to specific regulations to ensure the well-being of workers. Flexible manufacturing cells typically work in confined spaces in order to increase efficiency and decrease cost. The operation can be further complicated if uncertainty is involved. We propose an adaptive robust controller for uncertain mechanical systems in a confined space to enhance system performance while ensuring system safety. The design procedure consists of five phases: constraint-following formulation, fuzzy uncertainty prescription, diffeomorphism transformation, adaptive robust control design, and Nash game-based optimization. The effectiveness of the control scheme is demonstrated by numerical simulation experiments for a humanoid robot arm.

This work investigates a game-theoretic optimization approach towards robust control of uncertain dynamical systems with state and input constraints. The uncertainty involved is possibly rapidly time-varying but bounded within a prescribed fuzzy set. For this, the associated fuzzy dynamical system is appropriately established and constructed based on fuzzy set theory. To cope with the bounded state and input constraints, a novel state-and-input diffeomorphism technique is proposed, where a transformed system is formulated such that the prescribed inequality constraints are innovatively merged into the stabilization and trajectory tracking problems. Furthermore, a diffeomorphism-based robust control (DBRC) strategy is developed to ensure the uniform boundedness (UB) and uniform ultimate boundedness (UUB) of the transformed system. Under this proposed control architecture, the constraint satisfaction of the original system is thus always analytically ensured based on the rigorous properties of the diffeomorphism technique. The resulting control parameter optimization problem then has to take into account the multiple considerations (and compromise) amongst the factors of the steady-state performance; the finite convergence time; and the control effort. For this, a two-player Nash game is formulated and solved in an effective manner. The Nash equilibrium is obtained and the existence of the solution is also proved theoretically. With this methodology, and with the resulting attainment of the desired Nash equilibrium, the attendant outcome of superior system performance is achieved. Finally, numerical simulations on a steer-by-wire (SBW) system demonstrate the effectiveness of the proposed approach.

A Stackelberg game-based design for an adaptive robust control for the fuzzy uncertain underactuated mechanical systems (UMSs) is proposed. The emphasis is on fuzzy-based uncertainty and inequality constraint. The uncertainty is time varying and bounded within a prescribed fuzzy set. For the inequality constraint, we creatively have it merge into constraint-following performance by a diffeomorphism technique. An adaptive robust control strategy is then proposed. Deterministic performance is guaranteed provided the control design parameters are within feasible regions. To further enhance the performance, we introduce a two-player Stackelberg game setting. The optimal choice of design parameters can be solved. The feasibility of this design is demonstrated on an autonomous wheeled mobile robot (AWMR), which is confined in a bounded space.

This article develops a diffeomorphism-based robust bounded control (DRBC) for the permanent magnet linear synchronous motor system subject to inequality constraints (i.e., bounded input and position constraints) and uncertainties. The uncertainties, including parameter uncertainties and external disturbances, are potentially nonlinear and fast time varying. The bound of the uncertainty is described by a fuzzy set. To overcome the bounded input constraint, a robust bounded control is proposed based on a novel input diffeomorphism scheme, which is in a deterministic form and not if–then rule based. Furthermore, to overcome the bounded position constraint, a transformed system is formulated by a state diffeomorphism scheme, which transforms the bounded state-constrained system to an unconstrained one. Thus, the output of the controlled system can be restricted to a prescribed range. The DRBC guarantees both the uniform boundedness and the uniform ultimate boundedness of the transformed system. A fuzzy performance index, which combines the steady-state performance (the average fuzzy performance) and control effort, is then established based on the fuzzy description of uncertainty. As a result, the design parameter optimization can be solved by minimizing the performance index. Experimental results demonstrate that the DRBC is of superior tracking performance and robustness without violating the prescribed inequality constraints.

The inequality constraints, system nonlinearities, parameter uncertainties, and external disturbances are always unavoidable in practical mechanical systems. This article proposes an adaptive robust control (ARC) algorithm from the view of servo constraint following to tackle the control problem of mechanical systems subject to the above factors. For the inequality constraints, a creative diffeomorphism which could convert the two-sided bounded state variables to the unbounded ones is explored, which could render the transformed nonlinear system free from inequality constraints. For the system uncertainties, a leakage-type ARC algorithm is developed, which could render the system the practical stability. The permanent magnet linear motor (PMLM) system is utilized as a typical application to verify the proposed state transformation and ARC approach. Numerical simulations show that the displacement of the PMLM system could well track the desired trajectory without violating the given bound line.

There has been growing concern about digital well-being, especially given the emerging adverse impact of technology overuse. While prior studies have developed a variety of stand-alone techniques to combat technology overuse, little work has been done to build interventions directly into technologies to regulate usage. In this study, we designed three interventions that remind players to take a break in a casual mobile game. We explored players’ experiences with these interventions through a 4-day deployment study and follow-up interviews (N=16). Findings suggest that while some players had a positive experience with the game that had built-in interventions, others experienced unintended outcomes such as disrupted immersion or longer play sessions. We also found that interventions seemed to be more likely to succeed when players experienced a sense of accomplishment or negative emotions. We discuss the implications of the study and provide preliminary suggestions for designing built-in interventions.