Traditional intercultural communication training often lacks safe spaces for open practice, leading to self-censorship and limited skill development. The ICC Tutor, an AI-powered conversational system, addresses this by offering a private, nonjudgmental environment for reflection and dialog. Using retrieval-augmented generation (RAG), the system grounds its prompts and feedback in course materials. We conducted a mixed-methods study (N = 25) with Beginner/Intermediate and expert learners. Preliminary findings suggest that the tutor helped reduce feelings of nervousness. While many beginners reported increased confidence in intercultural communication, expert learners’ confidence temporarily decreased, suggesting the AI’s role in fostering deeper self-reflection rather than just boosting perceived competence. These findings underscore the potential of AI tutors in supporting communication education and highlight the need for experience-adaptive designs to support nuanced learning trajectories.

The growing use of generative AI raises ethical concerns about authorship attribution and plagiarism. This study examines how people judge the reuse of AI-generated content, focusing on moral patiency and ownership perceptions. In an experiment, participants evaluated two substantively similar manuscripts in which the original source was described as authored by a human, an AI system, or an AI agent with a human-like name. Results showed that copying AI-generated work was judged less unethical, less plagiaristic, and less guilt-inducing than copying human-authored work. Mediation analyses revealed that this leniency stemmed from lower perceptions of AI's capacity to suffer harm (moral patiency) and greater ownership attributed to the human writer reusing AI-generated content. Anthropomorphic cues shaped moral evaluations indirectly by reducing perceived ownership. These findings shed light on how people morally disengage when using AI-generated work and highlight differences in how ethical judgments are applied to human versus AI-created content.

The growing integration of artificial intelligence (AI) into decision-making processes has raised important concerns about fairness and trust. This research investigates how the presence of AI, human collaboration, and outcome favorability shape perceptions of fairness and trust in decision-making in a college-admission scenario. Across two experimental studies, we examine decision-making scenarios involving AI-only, human-AI collaboration, and human-only agents, under decision outcomes that were either favorable or unfavorable. Study 1 demonstrates that human involvement, either alone or in collaboration with AI, enhances fairness and trust relative to AI-only decisions, with fairness partially explaining trust differences. Study 2 extends these findings by showing that outcome favorability strongly influences fairness and trust, often outweighing the effects of who made the decisions. Notably, while human-only decisions elicited the highest trust under favorable outcomes, trust differences across agents diminished when outcomes were unfavorable. These findings highlight the complex interplay between decision-making agents and outcome favorability, underscoring the importance of integrating human involvement and managing outcome expectations to foster fairness and trust in AI-driven decision-making systems.

This study investigates people's perceptions of AI decision-making as compared to human decision-making within the job application context.It takes into account both favorable and unfavorable outcomes, employing a 2 � 2 experimental design (decision-making agent: AI algorithm vs. human; outcome: favorable vs. unfavorable).Upon evaluating a job seeker's suitability for a position, participants viewed algorithmic decisions as fairer, more competent, more trustworthy, and more useful than those made by humans.Interestingly, when a candidate was deemed unsuitable for hiring, people reacted more negatively to the verdict given by a human than to the same judgment offered by AI.Moreover, participants credited algorithmic decisions with greater sensitivity to both quantitative and qualitative qualifications, thus indicating algorithmic appreciation.Our findings shed light on the psychological basis of perceptions surrounding Algorithmic Decision-Making (ADM) and the responses to the decisions rendered by ADM systems.

Well-being apps promise to improve people's lives. Yet evidence shows that the data-hungry app culture that contextualizes well-being apps normalizes the user experience of affective discomfort. This apparent contradiction raises a difficult question: Is it responsible to ask people to improve their well-being by engaging further with an app culture that normalizes affective discomfort? We approached this question by deploying an online, scenario-based survey (n=688) about a fictional, but realistic well-being app called "Thalia." Thalia represents a novel class of well-being apps that are envisioned to: (i) utilize AI-driven facial recognition and analysis; and (ii) collect data for use in medical contexts. We found that people perceived Thalia to be affectively discomfiting even as they judged Thalia to be beneficial. Such findings imply a trade-off between 'better living through technology' and the negative affective implications of surveillance capitalistic app culture. Such a trade-off necessitates high-level analysis of just what "well-being" means in the context of contemporary app culture. Through analysis and discussion, we explore a troubling interplay between novel well-being apps and affective discomfort that requires careful attention from HCI researchers if human-centered well-being -- flourishing -- is truly what our products are intended to foster.

As AI-enhanced technologies become common in a variety of domains, there is an increasing need to define and examine the trust that users have in such technologies. Given the progress in the development of AI, a correspondingly sophisticated understanding of trust in the technology is required. This paper addresses this need by explaining the role of trust in the intention to use AI technologies. Study 1 examined the role of trust in the use of AI voice assistants based on survey responses from college students. A path analysis confirmed that trust had a significant effect the on intention to use AI, which operated through perceived usefulness and participants’ attitude toward voice assistants. In Study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined using exploratory factor analysis, which yielded two dimensions: human-like trust and functionality trust. The results of the path analyses from Study 1 were replicated in Study 2, confirming the indirect effect of trust and the effects of perceived usefulness, ease of use, and attitude on intention to use. Further, both dimensions of trust shared a similar pattern of effects within the model, with functionality-related trust exhibiting a greater total impact on usage intention than human-like trust. Overall, the role of trust in the acceptance of AI technologies was significant across both studies. This research contributes to the advancement and application of the TAM in AI-related applications and offers a multidimensional measure of trust that can be utilized in the future study of trustworthy AI.