Behaviour & Information Technology

Human-AI Interaction in Intelligent Workplaces: Behaviour, Trust, Experience, and Human-Centered Design

Rationale / Motivation

Artificial intelligence is increasingly becoming an interactive workplace technology through which employees communicate, collaborate, make decisions, and perform everyday work tasks (He et al., 2024; Zuboff, Longhofer, & Winchester, 2023). From generative AI assistants and algorithmic decision-support systems to intelligent monitoring tools and conversational agents, workers are engaging with AI systems on a continuous basis. Consequently, understanding how employees interact with AI technologies, experience AI-mediated work processes, develop trust in intelligent systems, and adapt their behaviours in response to AI-generated outputs has emerged as a central challenge for Behaviour & Information Technology research.

While existing research has extensively examined technological adoption and performance outcomes, comparatively less attention has been devoted to the behavioral, cognitive, relational, and psychological implications of intelligent systems in workplace settings. Emerging technologies are redefining human-computer interaction in organizational contexts and raising important questions regarding trust in AI, algorithmic transparency, digital autonomy, technostress, cognitive overload, human-AI collaboration, and behavioral adaptation (Magliocca, Canestrino, Carayannis, & Gagliardi, 2025).

While different special issues have recently focused on artificial intelligence in organisations, information systems and the future of work, little has been done to understand workplace AI from the perspective of human–computer interaction and human-centered design. Existing special issues have primarily addressed topics such as technological adoption, organizational outcomes, digital transformation, and AI implementation. In contrast, this Special Issue focuses on the human-intelligent system interaction, covering employee experience, trust calibration, behavioral adaptation, user engagement, explainability, cognitive processes, and the design of psychologically sustainable AI-enabled workplaces.

The Special Issue proposed is well aligned with the mission of Behaviour & Information Technology and brings together research on human behaviour, user experience, interaction design, intelligent systems and digital work. The special issue aims to contribute to a better understanding of not only what AI does in organizations, but also how employees experience, interpret, interact with, and adapt to intelligent technologies in everyday work settings.

In addition to empirical and theoretical contributions, the Special Issue strongly encourages design-oriented and design science research that develops, evaluates, and validates human-centred AI systems, intelligent interfaces, explainability mechanisms, trust-enhancing design features, and interaction designs that promote positive employee experiences and responsible AI use.

Scope

This special issue focuses on the intersection of intelligent technologies, workplace behavior, and information systems. We invite theoretical, empirical, methodological, design-oriented, and interdisciplinary contributions examining how AI and digital systems influence individual, team, and organizational behavior in technology-mediated work environments.

The issue is especially interested in studies investigating behavioral and cognitive responses to intelligent systems, human-AI interaction dynamics, digital work experiences, and the broader organizational implications of AI-enabled technologies. Contributions employing quantitative, qualitative, mixed-method, longitudinal, experimental, computational, ethnographic, and design-science approaches are highly encouraged.

Aims & Objectives

1. Examine how AI-enabled systems influence employee cognition, behavior, attitudes, and workplace experiences.
2. Advance theoretical understanding of human–AI interaction in organizational and digital work environments.
3. Investigate the behavioral and psychological implications of intelligent systems, including technostress, digital fatigue, trust, autonomy, and cognitive dependence.
4. Explore how organizations can design human-centered, transparent, and behaviorally sustainable intelligent systems.
5. Encourage interdisciplinary dialogue between information systems, organizational behavior, psychology, human-computer interaction, and technology management research.
6. Promote methodological innovation for studying behavioral outcomes in AI-mediated work settings.

Illustrative Research Questions

The Special Issue particularly welcomes research addressing questions such as:

1.      How do employees calibrate trust and reliance in AI decision-support systems?

2.      How do algorithmic management systems influence perceptions of autonomy, agency, and control?

3.      What design features enhance transparency, explainability, and trustworthiness in workplace AI?

4.      How do employees experience and respond to AI-generated recommendations and feedback?

5.      What factors shape employee acceptance, resistance, or overreliance on AI technologies?

6.      How does generative AI influence cognitive workload, attention, and decision quality?

7.      How can intelligent workplace technologies be designed to support psychological wellbeing and sustainable digital work?

8.      What interaction patterns emerge in human–AI collaboration and hybrid decision-making environments?

9.      How do employees negotiate responsibility, accountability, and agency when working alongside AI systems?

10.  What role does user experience design play in shaping employee trust, engagement, and adoption of AI technologies?

Possible Themes & Topics (Illustrative, Not Exhaustive)

Human-AI Interaction and Behavioral Adaptation

• Employee interaction with AI-enabled systems
• Human reliance, overreliance, and resistance toward AI
• Behavioral adaptation to intelligent technologies
• Human-AI collaboration and hybrid decision-making

Trust, Transparency, and Explainability

• Trust formation in intelligent systems
• Algorithmic transparency and explainable AI
• Employee perceptions of AI fairness and accountability
• Behavioral responses to opaque decision systems

Cognitive and Psychological Implications

• Technostress, digital exhaustion, and AI fatigue
• Cognitive overload and information processing under AI systems
• AI dependency and employee wellbeing
• Digital distraction and attention fragmentation

AI, Work Design, and Digital Autonomy

• AI-supported autonomy and employee empowerment
• Algorithmic management and behavioral control
• Digital surveillance and monitoring technologies
• Behavioral consequences of automated performance systems

Human-Centered Design and Ethical Technologies

• Ethical design of workplace AI systems
• Human-centered intelligent systems
• Responsible AI implementation in organizations
• Designing psychologically sustainable digital work environments

Learning, Skill Development, and Adaptability

• AI-enabled learning systems and digital upskilling
• Employee agility and adaptability in AI-enabled workplaces
• Behavioral implications of reskilling and task transformation
• Human learning behavior in technology-mediated contexts

Team, Collaboration, and Communication Dynamics

• AI-supported teamwork and collaboration
• Virtual collaboration and intelligent communication systems
• Social interaction patterns in AI-mediated environments
• Team trust and coordination under intelligent systems

Contextual and Sectoral Perspectives

• AI in healthcare, hospitality, education, finance, and public services
• Gig work, platform labor, and digital employment systems
• Cross-cultural and cross-national comparisons
• Remote and hybrid work environments

 

Human-Centered AI, User Experience (UX), and Interaction Design

·        User experience (UX) of workplace AI systems

·         Human-centered design of intelligent workplace technologies

·        Conversational AI and intelligent assistants at work

·        Adaptive and personalized workplace interfaces

·        AI usability and user acceptance in organizational settings

·        Interaction design for human-AI collaboration

·        Human oversight and human-in-the-loop systems

·        Trust calibration in intelligent systems

·        Employee engagement with AI-enabled technologies

·        Interface design for responsible and ethical AI use

·        User perceptions of agency, control, and autonomy in AI-mediated work environments

Expected Contributions

This special issue seeks contributions that:

• Advance understanding of behavioral and cognitive implications of AI-enabled technologies at work.
• Develop new theoretical insights into human–AI interaction within organizational settings.
• Provide empirical evidence regarding employee experiences in technology-mediated workplaces.
• Inform the design of human-centered and behaviorally sustainable intelligent systems.
• Contribute to responsible and ethical implementation of AI technologies in organizations.
• Bridge research across information systems, behavioral science, organizational studies, and human-computer interaction.

The special issue aims to contribute to ongoing scholarly discussions surrounding the future of work, digital transformation, intelligent systems, and the evolving relationship between humans and technology in organizational life.

He, G., Zheng, X., Li, W., Tan, L., Chen, S., & He, Y. (2024). The Mixed Blessing of Leaders’ Artificial Intelligence (AI)-oriented Change Behavior: Implications for Employee Job Performance and Unethical Behavior: He et al. Applied Research in Quality of Life, 19(2), 469-497.

Magliocca, P., Canestrino, R., Carayannis, E. G., & Gagliardi, A. R. (2025). Understanding human–technology interaction: evolving boundaries. European Journal of Innovation Management, 28(5), 2006-2028.

Zuboff, S., Longhofer, W., & Winchester, D. (2023). Social theory re-wired Social Theory re-wired (pp. 203-213): Routledge.