Associating Attitudes towards AI and Ambiguity: The Distinction of Acceptance and Fear of AI

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

Since the emergence of ChatGPT, Artificial Intelligence (AI) has become increasingly integrated into society, making it essential to understand how individuals perceive and interact with it. Given AI’s inherent ambiguity uncertainty, this study examines relationship between attitudes towards AI ambiguity. A survey 554 Japanese participants was conducted using questionnaires, including Attitude Toward scale (ATAI) scale, which assesses two key dimensions: acceptance fear, along Multidimensional Attitudes Ambiguity Scale. Findings indicate that version ATAI developed for first time in study, demonstrated strong internal consistency, test-retest reliability, validity. Psychometric properties were supported by usage, willingness use AI, expected correlations personality traits, aligning prior literature. Text-based predictions natural language processing reinforced finding, showing significant associations scores. This is examine relate ambiguity, revealing Need complexity Novelty, Discomfort predict fear. Absolutism positively correlates both. These results are inherently complex multidimensional, offering insights effective sustainable engagement as an ambiguous agent.

Language: Английский

---

AI feedback and workplace social support in enhancing occupational self-efficacy: a randomized controlled trial in Japan

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 2, 2025

Language: Английский

---

The fearful mind of artificial intelligence: fear and perceived existential threat of artificial intelligence as a function of its cognitive and emotional capabilities

The Journal of Social Psychology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: May 9, 2025

The purpose of this research was to examine people's fear and perception threat toward artificial intelligence (AI) as a function various psychological features attributed it. To investigate this, participants (Exp. 1, N = 206) read descriptions AI with high or low cognitive emotional capabilities. They were most (least) averse described having the strongest (weakest) these capabilities 1). Similarly, in Experiment 2, representative U.S. sample (N 686) more afraid threatened by equally strong than weaker (weak cognition, emotion), but that pattern reversed when faculties pharmacologically altered humans. These findings provide evidence for competing predictions about configuration evoke negateve responses. Furthermore, they novel test applied AI.

Language: Английский

---

Impact of Large Language Model–Based AI Tools on Physician–Patient Communication: A Systematic Review and Meta-Analysis (Preprint)

Published: May 11, 2025

Recent advances in large language models (LLMs) such as GPT-3/4 have spurred development of AI chatbots and advisory tools medicine. These systems are posited to assist or augment physician–patient communication, potentially improving empathy, clarity, responsiveness. However, their actual impact on communication outcomes remains uncertain. To systematically review meta-analyze peer-reviewed studies (2020–2025) evaluating how LLM-based interventions affect including trust, patient understanding. Following PRISMA 2020 guidelines, we searched PubMed/MEDLINE for published from 2025 examining LLM chatbot applications clinical contexts. Eligible designs included randomized, observational, cross-sectional, qualitative studies. Two reviewers independently screened titles/abstracts, assessed full texts, extracted data study design, population, type, measures, outcomes. We conducted a synthesis random-effects meta-analysis, reporting pooled standardized mean differences (SMD) odds ratios (OR) with 95% confidence intervals (CI). From 312 records, 10 (N=10) were included, all quantitative predominantly cross-sectional. Populations ranged patients chronic conditions healthcare professionals laypersons. Outcomes empathy (7 studies), clarity/information quality (6), satisfaction usefulness (4), trust perceptions (2). In six direct comparisons AI- versus physician-generated responses, LLMs rated significantly higher five One found replies judged empathetic 45.1% cases 4.6% physician (OR ~9.8, P<.001). Similarly, ChatGPT-4 answers scored 5-point scale than human-written responses (mean 4.18 vs 2.70, neurology showed scores (CARE +1.38, P<.01) ChatGPT answers. Only one no significant difference. content was also longer more information-rich, patient-perceived clarity On the other hand, GPT-4 simplified pathology reports, increasing comprehension (7.98 5.23/10, P<.001) reducing consultation time by 70%. sometimes less concise readable low-literacy patients. analyses (4 studies, n=2,604), positive effect (SMD +1.05, CI 0.45–1.65) improved understanding +0.82, 0.30–1.34). Patient results mixed. No directly long-term trust. Current evidence suggests can enhance producing empathetic, detailed, understandable responses. improvements may positively influence experience engagement. generate overly lengthy occasionally inaccurate advice, emphasizing need oversight. While meta-analytic findings promising, robust controlled trials needed confirm benefits, assess outcomes, define optimal integration strategies.

Language: Английский

---

Effort paradox redux: Rethinking how effort shapes social behavior

Advances in experimental social psychology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

---

Incorporating AI Into Military Behavioral Health: A Narrative Review

Military Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: May 6, 2025

ABSTRACT Introduction Concerns regarding suicide rates and declining mental health among service members highlight the need for impactful approaches to address behavioral needs of U.S. military populations improve force readiness. Research in civilian has revealed that artificial intelligence machine learning (AI/ML) have promise advance care following 6 domains: Education Training, Screening Assessment, Diagnosis, Treatment, Prognosis, Clinical Documentation Administrative Tasks. Materials Methods We conducted a narrative review research populations, published between 2019 2024, involved AI/ML health. Studies were extracted from Embase, PubMed, PsycInfo, Defense Technical Information Center. Nine studies considered appropriate review. Results Compared there been much less use The selected using ML shown screening assessment, such as predicting negative outcomes populations. also applied diagnosis well prognosis, with initial positive results. More is needed validate results reviewed. Conclusions There potential be more extensively health, including education/training, treatment, clinical documentation/administrative tasks. article describes challenges further integration AI into considering perspectives members, providers, system-level infrastructure.

Language: Английский

---

The Impact of Artificial Intelligence on Neuroscience and Mental Health: A Perspective Review

AI in neuroscience., Journal Year: 2025, Volume and Issue: unknown

Published: May 26, 2025

Language: Английский

---