AHFE Open AccessGlobal Issues Challenge: Challenges in AI at the Human Level
Editors: Tareq Z. Ahram, Jay Kalra, Waldemar Karwowski
Topics: Artificial Intelligence & Computing
ISBN: 979-8-950676-10-9
DOI: 10.54941/ahfe1007253
Table of Contents
Artificial intelligence uses and loneliness: Examining the relationship between artificial intelligence usage patterns, need to belong and loneliness
Investigating the relationships between motivations for using AI tools and socialization dynamics is gaining increasing importance in AI-human interaction research. The potential opportunities and benefits that artificial intelligence tools can provide in relation to social needs and socialization dynamics attract the attention of researchers and practitioners in various behavioral fields. This study aimed to examine the relationship between motivations for using artificial intelligence tools and social characteristics. For this purpose, motivations for using AI tools, general attitudes towards AI, need for belonging, and loneliness levels were measured in a questionnaire form including 249 participants. The relationships between social interaction, identity, conformity, productivity and information motivations for using AI tools and the need to belong, loneliness and attitudes towards artificial intelligence were examined and the results showed that these motivations are positively predicted by positive attitudes towards artificial intelligence and the need to belong. The findings are discussed within the framework of the relevant literature.
Alp Giray Kaya, Hale Dost
Open Access
Article
Conference Proceedings
Trust in AI in commercial aviation maintenance: Gaining efficiencies while enhancing safety
The commercial aviation industry is currently integrating AI throughout its infrastructure. While business applications of AI can quickly improve relations with customers and efficiently help increase profit, the higher risk operational areas of the industry related to flight safety, like the flight deck, air traffic control, and maintenance, require important human factors trust between the AI being implemented and the user. In the case of pilots and air traffic controllers, this trust is paramount to safe flight. How important is this trust in AI to the aviation maintainer, given that AI is being integrated into the current maintenance workforce as a rapid solution to address the shortage of Aviation Maintenance Technicians (AMT)? With the AMT shortage forecasted to continue over the next 20 years, these opportunities to make AI-aided maintenance decisions bring efficiency and safety gains to maintenance operations and have quickly become a reality. The current AI aviation maintenance technologies that are having the most significant impact in the aviation maintenance arena include diagnostics for engine health, predictive maintenance, automated visual inspections, and data-driven work management to predict and inform better maintenance decisions. The researchers developed an AXTENI framework for AI team decision-making in aviation. They introduced it for maintenance use to demonstrate the importance of trust in AI for ethical maintenance decision-making (DM) to occur. The research survey, ‘Fostering Trust: Maintainers and Artificial Intelligence in Aviation Maintenance”, is introduced to determine where aviation maintainers currently stand in their trust in their newly adopted AI decision-making tools. An analysis of the final survey data is presented.
Mark Miller, Leila Halawi, Sam Holley, Bettina Mrusek, Mark Kanitz
Open Access
Article
Conference Proceedings
From Outcomes to Experience: Designing AI to Support Agency, Collaboration, and Calibrated Trust in Creative Work
AI is rapidly becoming embedded in design practice, from individual ideation to team collaboration and collective decision-making, yet evaluation still privileges short-term output metrics. We argue that outcome-centered paradigms are inadequate for Human-Centered AI because they under-measure the experiential conditions that sustain design innovation over time, including agency, creative self-efficacy, ownership, and calibrated trust. Drawing on HCI and creativity psychology, we conceptualize design innovation as a dynamic, situated process shaped by autonomy and competence, and by interactional work of synthesis, trade-offs, and attribution. As LLMs fluently generate concepts, proposals, and summaries, designers may shift from originators to selectors; even when outputs appear strong, this shift can weaken perceived ownership and diminish creative self-efficacy. At the team level, AI mediation can redistribute conversational authority, obscure provenance, and compress divergence, intensifying convergence pressures and participation inequities. For small-societies, legitimacy becomes central: participants must understand and contest how inputs are represented in the evolving collective record. We propose a design approach that treats LLM-based and multi-agent systems as configurable socio-technical mediators, articulated through seven principles: role-differentiated mediation, legibility of influence, provenance, contestability, divergence before convergence, reflection scaffolds, and trust calibration. We conclude with an evaluation agenda that complements output metrics with longitudinal measures of agency, innovation self-efficacy, ownership, participation equity, and procedural legitimacy.
Yuan-Chi Tseng
Open Access
Article
Conference Proceedings
Human–AI Collaboration in Automated X-Ray Screening: Effects of Alarm Types and Reliability Levels on Operator Performance in Subway Security
Public-transportation X-ray checkpoints increasingly integrate Automated Diagnostic Aid Systems (ADAS) to support threat detection, yet system-level success remains contingent on human operators’ vigilance, decision strategies, and calibrated trust in automation. To promote the joint performance of human–AI collaboration, this study examines how alarm design (i.e., the way AI provides diagnostic advice) should vary with automation reliability. We experimentally compared three alarm modalities—binary alarm (“danger/safe”), likelihood alarm (four-level graded advice: “danger/warning/possible-safe/safe”), and automated decision (the system hides “safe” images and forwards only “danger” cases for human review)—across three system reliability levels (70%, 80%, 90%). Twenty-one participants completed X-ray baggage search tasks with target prevalence set as 30%; after quality control, 18 datasets (n=6 per reliability) were analyzed. Primary objective measures were d′ sensitivity (Signal Detection Theory) and response time (RT) for target-present and target-absent decisions; subjective measures captured multi-dimensional trust.As automated decision triages images and alters the decision space, SDT analyses focused on binary vs. likelihood conditions, with alarm type as a within-group variable and reliability as a between-group variable. A two-way mixed ANOVA revealed a significant main effect of alarm type and a significant alarm-type × reliability interaction (Alarm Type: F=10.88, p<.05; Interaction: F=11.63, p<.05). For binary alarms, operator d′ increased monotonically with ADAS reliability (from 70% to 90%), indicating that categorical cues benefit from high classifier accuracy. For likelihood alarms, d′ improved from 70% to 80% but declined at 90%, suggesting that when the AI is highly accurate, graded, ambiguous messages can impose avoidable decisional complexity and cognitive load, degrading sensitivity relative to simpler cues. RT analyses did not yield reliable omnibus effects, though patterns were consistent with the interpretation that richer advice requires additional decisional processing, especially for target-absent judgments.Subjective results complemented the objective pattern. At 70% reliability, participants preferred likelihood alarms, rating them higher on perceived competence/faith/reliability, consistent with the idea that greater transparency and nuance are valuable when automation is imperfect. At 90% reliability, participants expressed the highest trust and willingness to rely on automated decision, reflecting comfort with delegating routine “safe” triage to a highly reliable AI and reserving human involvement for flagged “danger” cases. Across conditions, trust calibration tracked reliability, but critically depended on the alarm form through which the AI conveyed its assessment.Contributions. (1) We provide an empirical mapping between alarm granularity and automation reliability, demonstrating that the optimal alarm type depends on the AI’s operating performance. (2) We show that graded likelihood cues can enhance sensitivity at lower automation reliability by supporting informed human override, but they can reduce performance at high reliability by adding decisional friction. (3) We integrate SDT-based sensitivity with multi-dimensional trust to articulate actionable design guidance for human–AI teaming in safety-critical screening.Implications for design. To maximize human–AI system performance, alarm transparency should be matched to system reliability. Likelihood-based alarms are preferable when reliability is modest, as they support human verification and facilitate appropriate criterion setting. When reliability is high, binary or automated-decision modes are recommended to minimize cognitive load and enable efficient triage. Practically, an adaptive alarm policy that switches alarm type as real-time reliability estimates change may best sustain calibrated trust, operator efficiency, and system-level sensitivity in high-throughput subway screening.
Xin Zhou
Open Access
Article
Conference Proceedings
Quality of Life in Contemporary Society: Social Dimensions in the Context of Digitalization and Artificial Intelligence
The main objectives of the article are to trace different approaches to the study of quality of life and to analyze its essential objective and subjective characteristics. Conceptually, quality of life is defined as a set of conditions and opportunities provided by the environment and institutions for the material, social, and mental well-being of individuals and communities. Emphasis is on living standards and material well-being, with digitalization and AI contributing significantly to improvements in the material environment and access to public resources. Artificial intelligence and digital technologies can also enhance feelings of autonomy, control, and satisfaction, as well as facilitate access to information
Albena Nakova, Valentina Milenkova, Emilia Chengelova, Karamfil Manolov
Open Access
Article
Conference Proceedings
Skill Development, Maintenance, Erosion, and Revaluation: How Knowledge Workers Experience Generative AI
Generative AI (GenAI) is rapidly embedding itself in knowledge work, supporting tasks such as writing, analysis, coding, and information synthesis. Although widely promoted as enhancing productivity and learning, concerns persist regarding overreliance, deskilling, and erosion of professional expertise. Current debates typically frame GenAI’s impact on skills in binary terms—upskilling versus deskilling—yet empirical evidence on how workers themselves experience these changes in everyday practice remains limited. This study examines how knowledge workers perceive the impact of GenAI on their professional skills. Semi-structured interviews were conducted with 38 professionals in the Netherlands, including academics (e.g., lecturers and professors) and non-academic professionals (e.g., consultants, analysts, engineers, legal professionals, and public sector employees) with varying levels of experience. Data were analyzed using inductive thematic analysis to identify recurring patterns in participants’ accounts of skill-related change. Four perceived skill outcomes emerged: skill development, skill maintenance, skill erosion, and skill revaluation. Skill development involved acquiring or strengthening competencies through learning from GenAI outputs, expanded information access, and offloading routine tasks to focus on higher-level work. Skill maintenance described situations where participants perceived little or no change, often linked to selective and critical use. Skill erosion referred to diminished ability to perform tasks independently without GenAI support. Skill revaluation captured shifts in perceived skill importance as certain tasks became delegable while others gained prominence. Overall, findings indicate that GenAI’s impact on professional skills is heterogeneous and practice-dependent. The proposed four-outcome framework offers a nuanced account of how workers interpret skill change in everyday GenAI use.
Oscar Oviedo-Trespalacios, Felicia Laksanadjaja, Helma Torkamaan
Open Access
Article
Conference Proceedings
AI-empowered Design of Museum Cultural and Creative Products: Consumer Perception of Creativity and Its Impact on Consumption Decision-making
The integration of Artificial Intelligence (AI) in the design process is transforming the cultural and creative industries, especially in the design of Museum Cultural and Creative Products (MCCPs). This study investigates consumer perceptions of creativity in AI-generated MCCPs and applies the Theory of Consumption Values (TCV) to assess how different dimensions of consumer value：quality value, social value, innovation value, and experiential value affect purchase intentions toward AI-designed MCCPs. Additionally, it explores how perceptions of creativity influence purchase decisions.The study is divided into two parts: study1 with a sample of 546 participants, examines how consumers perceive the creativity of MCCPs designed by AI versus human designers. Study2 based on 412 completed surveys, explores consumer purchase intentions for AI-designed products and how perceptions of creativity in AI-designed products influence the relationship between consumption values and purchase intentions. The findings show that consumers are more likely to accept AI-designed products when they perceive them as emotionally appealing and culturally valuable. The study also finds that creativity perception enhances the positive impact of quality, social, innovation, and experiential values on consumers' intension to purchase AI-generated MCCPs.This study helps deepen the understanding of the intersection between AI, creativity, and consumer behavior. The results provide valuable insights for cultural institutions, designers, and marketers, helping them leverage AI to enhance product design while maintaining cultural authenticity.
Chengcheng Ma, Mengkun Bi, Min Hua
Open Access
Article
Conference Proceedings
From Result Imitation to Cultural Translation: An Intelligent Generation Approach for Dong Brocade Patterns Based on Patternology
Generative artificial intelligence (AI) has become a significant frontier in pattern design, but generating culturally informed patterns remains challenging. Mainstream training data biases limit AI's understanding of minority cultures, leading to superficial feature replication and cultural appropriation in AIGC. This study uses Dong brocade as a case, shifting from "Lamarckian copy-the-product" to "Weismannian copy-the-instruction.", it adopts a Weismannian concept of cultural inheritance, focusing on transmitting intrinsic generative logic as 'instructional information'. Through fieldwork and Patternological analysis, a systematized Dong brocade knowledge “instructional information set” was constructed and implemented in a customized generation system. Comparative experiments demonstrate that this method significantly outperforms general models in cultural authenticity. This approach reshapes AI from a general imitator into a culturally informed translator, providing a replicable and culturally sustainable pathway for intelligent innovation in minority pattern designs.
Jia Qin Li, Luo Wang
Open Access
Article
Conference Proceedings
National Systematization for Voluntary Local Reports (VLR) of the 2030 Agenda; Municipalities of Mexico
This paper presents the National Systematization of Voluntary Local Reports (VLR) of the 2030 Agenda for Sustainable Development in Mexican municipalities, developed through the collaboration of the Social and Solidarity Economy Research Network (RIESS) under the Research Networks modality. The project builds on the experience of the Municipal Observatory of Competencies for the Sustainable Development Goals (SDGs) and the preparation of the first Voluntary Local Report of Tijuana, Baja California, Mexico.The study applies a glocal and systemic approach based on the Quintuple Helix Systemic (QHS-VLR) methodology, which integrates government, academia, companies, associations, and consultants. This methodology promotes multisectoral participation and enables the articulation of local initiatives with the global framework of the 2030 Agenda. The project fostered social awareness and active engagement across sectors through the NODESS Tijuana program and the RIESS Network, generating social service activities, instructor training involving undergraduate and graduate students, and the development of applied research projects aligned with each SDG.The first VLR of Tijuana (2024) was developed under the leadership of the Technological Institute of Tijuana and the RIESS TecNM Research Network, in coordination with the Municipal Institute of Citizen Participation and with the support of the Executive Council of the 2030 Agenda of the Ministry of Economy. This report has been incorporated into United Nations databases, positioning Tijuana as the first city in Northern Mexico to present a VLR internationally.The results provide a methodological foundation for strengthening regional collaboration and advancing the national systematization of VLRs in Mexico, contributing from academia to evidence-based governance, regulatory strengthening, and systemic cooperation among societal sectors.
Rodolfo Martinez Gutierrez, Diana Rubi Oropeza-tosca, Sonia Moreno Cabral, Beatriz Chavez Ceja, Blanca Esthela Zazueta Villavicencio, Mayra Karina Galvez-Diaz, Karina Lopez Valle, Carmen Adolfo Castillo Rivera, Jazmin Balderrabano Briones, Omar Jimenez-marquez
Open Access
Article
Conference Proceedings
AIGC as the Third Space for Cultural Innovation Design
Design in the digital-intelligent era is undergoing a profound “cultural turn”, shifting from symbolic representation to algorithmic logic and from aesthetic experience to power structures. Culture has evolved into an active agent for reconstructing technological paradigms and negotiating power relations. Drawing on Bhabha’s concept of the “third space”, this study explores how AIGC and its creative practices function as a field of cultural hybridity and meaning negotiation. It argues that the hybrid interplay between culture and technology challenges the traditional understanding of algorithmic systems, fostering new paradigms of technological perception and application. Simultaneously, at the level of power dynamics, design practice becomes an arena where diverse cultural subjects contend for discursive authority. Through theoretical interpretation and case analysis, this paper reveals how design evolves from formal creation to cultural dialogue, offering a critical perspective on the new mechanisms of cultural innovation and design practice in this changing age.
Zixi Wang, Tie Ji
Open Access
Article
Conference Proceedings