arXiv subjects

Jingshu Li

Publications and source records attributed to Jingshu Li.

15 recordsLinked to original sources

arXiv2026

Understanding Older Adults' Experiences of Support, Concerns, and Risks from Kinship-Role AI-Generated Influencers

AI-generated influencers are rapidly gaining popularity on Chinese short-video platforms, often adopting kinship-based roles such as AI grandchildren to attract older adults. Although this trend has raised public concern, little is known about the design strategies behind these influencers, how older adults experience them, and the benefits and risks involved. In this study, we combined social media analysis with interviews to unpack the above questions. Our findings show that influencers use both visual and conversational cues to enact kinship roles, prompting audiences to engage in kinship-based role-play. Interviews further show that these cues arouse emotional resonance, help fulfill older adults' informational and emotional needs, while also raising concerns about emotional displacement and unequal emotional investment. We highlight the complex relationship between virtual avatars and real family ties, shaped by broader sociocultural norms, and discuss how AI might strengthen social support for older adults while mitigating risks within cultural contexts.

cs.HC↗

arXiv2026

AI-exhibited Personality Traits Can Shape Human Self-concept through Conversations

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.

cs.HC↗

arXiv2025

Understanding the Effects of Miscalibrated AI Confidence on User Trust, Reliance, and Decision Efficacy

Providing well-calibrated AI confidence can help promote users' appropriate trust in and reliance on AI, which are essential for AI-assisted decision-making. However, calibrating AI confidence -- providing confidence score that accurately reflects the true likelihood of AI being correct -- is known to be challenging. To understand the effects of AI confidence miscalibration, we conducted our first experiment. The results indicate that miscalibrated AI confidence impairs users' appropriate reliance and reduces AI-assisted decision-making efficacy, and AI miscalibration is difficult for users to detect. Then, in our second experiment, we examined whether communicating AI confidence calibration levels could mitigate the above issues. We find that it helps users to detect AI miscalibration. Nevertheless, since such communication decreases users' trust in uncalibrated AI, leading to high under-reliance, it does not improve the decision efficacy. We discuss design implications based on these findings and future directions to address risks and ethical concerns associated with AI miscalibration.

cs.AI↗

arXiv2025

Exploring the Effects of Chatbot Anthropomorphism and Human Empathy on Human Prosocial Behavior Toward Chatbots

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 further 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.

cs.HC↗

arXiv2025

AI Literacy Education for Older Adults: Motivations, Challenges and Preferences

As Artificial Intelligence (AI) becomes increasingly integrated into older adults' daily lives, equipping them with the knowledge and skills to understand and use AI is crucial. However, most research on AI literacy education has focused on students and children, leaving a gap in understanding the unique needs of older adults when learning about AI. To address this, we surveyed 103 older adults aged 50 and above (Mean = 64, SD = 7). Results revealed that they found it important and were motivated to learn about AI because they wish to harness the benefits and avoid the dangers of AI, seeing it as necessary to cope in the future. However, they expressed learning challenges such as difficulties in understanding and not knowing how to start learning AI. Particularly, a strong preference for hands-on learning was indicated. We discussed design opportunities to support AI literacy education for older adults.

cs.HC↗

arXiv2025

Timing Matters: How Using LLMs at Different Timings Influences Writers' Perceptions and Ideation Outcomes in AI-Assisted Ideation

Large Language Models (LLMs) have been widely used to support ideation in the writing process. However, whether generating ideas with the help of LLMs leads to idea fixation or idea expansion is unclear. This study examines how different timings of LLM usage - either at the beginning or after independent ideation - affect people's perceptions and ideation outcomes in a writing task. In a controlled experiment with 60 participants, we found that using LLMs from the beginning reduced the number of original ideas and lowered creative self-efficacy and self-credit, mediated by changes in autonomy and ownership. We discuss the challenges and opportunities associated with using LLMs to assist in idea generation. We propose delaying the use of LLMs to support ideation while considering users' self-efficacy, autonomy, and ownership of the ideation outcomes.

cs.HC↗

arXiv2025

Mining Evidence about Your Symptoms: Mitigating Availability Bias in Online Self-Diagnosis

People frequently exposed to health information on social media tend to overestimate their symptoms during online self-diagnosis due to availability bias. This may lead to incorrect self-medication and place additional burdens on healthcare providers to correct patients' misconceptions. In this work, we conducted two mixed-method studies to identify design goals for mitigating availability bias in online self-diagnosis. We investigated factors that distort self-assessment of symptoms after exposure to social media. We found that availability bias is pronounced when social media content resonated with individuals, making them disregard their own evidences. To address this, we developed and evaluated three chatbot-based symptom checkers designed to foster evidence-based self-reflection for bias mitigation given their potential to encourage thoughtful responses. Results showed that chatbot-based symptom checkers with cognitive intervention strategies mitigated the impact of availability bias in online self-diagnosis.

cs.HC↗

arXiv2025

As Confidence Aligns: Exploring the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making

Complementary collaboration between humans and AI is essential for human-AI decision making. One feasible approach to achieving it involves accounting for the calibrated confidence levels of both AI and users. However, this process would likely be made more difficult by the fact that AI confidence may influence users' self-confidence and its calibration. To explore these dynamics, we conducted a randomized behavioral experiment. Our results indicate that in human-AI decision-making, users' self-confidence aligns with AI confidence and such alignment can persist even after AI ceases to be involved. This alignment then affects users' self-confidence calibration. We also found the presence of real-time correctness feedback of decisions reduced the degree of alignment. These findings suggest that users' self-confidence is not independent of AI confidence, which practitioners aiming to achieve better human-AI collaboration need to be aware of. We call for research focusing on the alignment of human cognition and behavior with AI.

cs.HC↗

arXiv2024

Wild Narratives: Exploring the Effects of Animal Chatbots on Empathy and Positive Attitudes toward Animals

Rises in the number of animal abuse cases are reported around the world. While chatbots have been effective in influencing their users' perceptions and behaviors, little if any research has hitherto explored the design of chatbots that embody animal identities for the purpose of eliciting empathy toward animals. We therefore conducted a mixed-methods experiment to investigate how specific design cues in such chatbots can shape their users' perceptions of both the chatbots' identities and the type of animal they represent. Our findings indicate that such chatbots can significantly increase empathy, improve attitudes, and promote prosocial behavioral intentions toward animals, particularly when they incorporate emotional verbal expressions and authentic details of such animals' lives. These results expand our understanding of chatbots with non-human identities and highlight their potential for use in conservation initiatives, suggesting a promising avenue whereby technology could foster a more informed and empathetic society.

cs.HC↗

arXiv2024

Study of charged Lepton Flavor Violation in electron muon interactions

With the improvement of muon acceleration technology, it has received great interest to exploit high-energy muon beams for collision or target experiments. We investigate possible charged Lepton Flavor Violation (cLFV) processes mediated by an extra massive neutral gauge boson Zprime in electron muon interactions, either at a proposed electron muon collider or in a fixed target experiment with high-energy muon beam hitting electrons in the target. Based on Monte Carlo calculations and fast detector simulations, we study in detail our signal and possible backgrounds, giving the sensitivity results of cLFV signals at the 90% confidence level. Compared with current and prospective limits set by other experiments, electron muon interactions demonstrate significant advantages in the cLFV coupling strength sensitivity with tau in the final states. In addition, a special cLFV coupling combination, lambda_emu * lambda_mumu, can also be probed in our proposal.

hep-ex↗

arXiv2024

Search for R-Parity-Violation-Induced Charged Lepton Flavor Violation at Future Lepton Colliders

Interest in searches for Charged Lepton Flavor Violation (CLFV) has continued in the past few decades since the observation of CLFV will indicate new physics beyond the Standard Model (BSM). As several future lepton colliders with high luminosity have been proposed, the search for CLFV will reach an unprecedented level of precision. Many BSM models allow CLFV processes at the tree level, such as the R-parity-violating (RPV) Minimal Supersymmetric Standard Model (MSSM), which is a good choice for benchmark. In this paper, we perform a detailed fast Monte Carlo simulation study on RPV-induced CLFV processes at future lepton colliders, including a 240 GeV circular electron positron collider (CEPC) and a 6 or 14 TeV muon collider. As a result, we found that the upper limits on the $τ$ related RPV couplings will be significantly improved, while several new limits on RPV couplings can be set, which are inaccessible by low-energy experiments.

hep-ph↗

arXiv2023

Search for Charged Lepton Flavor Violation in $J/ψ$ decays at BESIII

The observation of any charged lepton flavor violation (CLFV) process would be a clear signal of new physics beyond the Standard Model. Various decay modes, including lepton ($μ, τ$) decays, pseudoscalar meson ($K,π$) decays, vector meson ($ϕ, J/ψ, Υ$) decays, and Higgs decays, have been explored to detect CLFV. Focusing on the search for CLFV at the $τ$-charm region, the results of the search for $J/ψ\to eτ/ eμ$ using the 10 billion $J/ψ$ events collected by the BESIII experiment are presented. The upper limits (ULs) at the 90$\%$ confidence level are $\mathcal{B}(J/ψ\to e^\pmτ^\mp)<7.5\times10^{-8}$ and $\mathcal{B}(J/ψ\to e^\pmμ^\mp)<4.5\times10^{-9}$, respectively. Improving the previously published limits by two orders of magnitudes, the results are the most stringent CLFV searches in heavy quarkonium system.

hep-ex↗

arXiv2023

Searching for Majorana Neutrinos at a Same-Sign Muon Collider

Majorana properties of neutrinos have long been a focus in the pursuit of possible new physics beyond the standard model, which has motivated lots of dedicated theoretical and experimental studies. A future same-sign muon collider is an ideal platform to search for Majorana neutrinos through the Lepton Number Violation process. Specifically, this t-channel kind of process is less kinematically suppressed and has a good advantage in probing Majorana neutrinos at high mass regions up to 10 TeV. In this paper, we perform a detailed fast Monte Carlo simulation study through examining three different final states: 1) pure-leptonic state with electrons or muons, 2) semi-leptonic state, and 3) pure-hadronic state in the resolved or merged categories. Furthermore, we perform a full simulation study on the pure-leptonic final state to validate our fast simulation results.

hep-ph↗

arXiv2023

A Comparative Study of Z$^{\prime}$ mediated Charged Lepton Flavor Violation at future lepton colliders

Charged lepton flavor violation (CLFV) represents a transition between charged leptons of different generations that violates lepton flavor conservation, which is a clear signature of possible new physics beyond the standard model. By exploiting a typical example model of extra Z$^{\prime}$ gauge boson, we perform a detailed comparative study on CLFV searches at several future lepton colliders, including a 240 GeV electron-positron collider and a TeV scale muon collider. Based on detailed signal and background Monte-Carlo studies with fast detector simulations, we derive the potentials in searching for Z$^{\prime}$ mediated CLFV couplings with $eμ$, $eτ$ and $μτ$ of different future colliders. The results are compared with the current limits set by either low-energy experiments or the high-energy LHC experiments. We find that the sensitivity of the $τ$ related CLFV coupling strength at future lepton colliders will be significantly improved comparing with the current best constraints.

hep-ph↗

arXiv2016

Optimization-based Alignment for Strapdown Inertial Navigation System Comparison and Extension

In this paper, the optimization-based alignment (OBA) methods are investigated with main focus on the vector observations construction procedures for the strapdown inertial navigation system (SINS). The contributions of this study are twofold. First the OBA method is extended to be able to estimate the gyroscopes biases coupled with the attitude based on the construction process of the existing OBA methods. This extension transforms the initial alignment into an attitude estimation problem which can be solved using the nonlinear filtering algorithms. The second contribution is the comprehensive evaluation of the OBA methods and their extensions with different vector observations construction procedures in terms of convergent speed and steady-state estimate using field test data collected from different grades of SINS. This study is expected to facilitate the selection of appropriate OBA methods for different grade SINS.

cs.RO↗