Collaboration Systems and Technologies Track

This minitrack invites research that examines how technologies are reshaping teaching, learning, and collaboration across diverse educational and organizational contexts. We are particularly interested in papers that critically engage with both the possibilities and the practical challenges of emerging learning technologies, as well as their interaction with more traditional learning technologies and infrastructures. Topics of interest include, but are not limited to:

• Learning Theories and Pedagogies: How can contemporary and emerging learning theories inform the design and use of technology-enhanced learning experiences? What new pedagogical and learning design approaches are needed to meaningfully integrate advanced learning technologies, such as immersive environments, intelligent systems, and data-rich learning tools, into educational practice?
• Learning Technologies and Infrastructures: We welcome research on innovative learning platforms, experimental tools, and supporting infrastructures (including LMS and learning analytics systems) that enable new forms of teaching and learning, including assessment and collaboration.
• User Experience and Feedback: We encourage submissions that investigate learner and educator interactions with educational technologies, including usability studies, feedback mechanisms, and analyses of how technologies influence motivation, engagement, and learning outcomes. How do teachers and learners experience the integration of AI in learning designs and value the human-AI interaction as part of the educational process?
• Hybrid Intelligence and the Future of Learning: We are particularly interested in work that explores evolving forms of collaboration between humans and intelligent systems in learning contexts. How can we design systems that support learner agency, trust, meaningful feedback, and effective interventions in complex learning environments? What ethical, equity, and governance considerations arise as educational technologies become more capable and pervasive? How can technology foster collaboration, creativity, and problem-solving in these settings? What new forms of symbolic hybrid intelligence systems are likely to emerge, and what competencies will be required from both humans and AI to effectively realise and sustain these systems?

This minitrack emphasizes the crucial connection between learning and collaboration systems and technologies. Submissions should offer theoretical, empirical, methodological, or design innovations that advance our understanding of learning in technology-enhanced environments. We seek to bridge disciplines and research communities between system sciences, AI, computer, and learning sciences, so within this scope, a broad range of research questions, learning settings, and theoretical and methodological traditions will be considered.

Papers accepted for presentation in this minitrack at HICSS will be selected and invited to submit extended manuscripts to Behaviour & Information Technology or Policy Futures in Education.

Minitrack Co-Chairs:

Olga Scrivner (Primary Contact)
Rose-Hulman Institute of Technology, Indiana University, and Scrivner Solutions Inc
obscrivn@iu.edu

Andy Nguyen
University of Oulu
andy.nguyen@oulu.fi

Maarten de Laat
Adelaide University
maarten.delaat@adelaide.edu.au

James Scrivner
Butler University and Scrivner Solutions Inc
jscrivner@butler.edu

AI-powered tools such as Claude, ChatGPT, Gemini, their embedded image models, and other media compositions tools for music, film, social media and have moved beyond the laboratory and into the global marketplace. From professional design studios to indie game development and corporate marketing departments, AI is no longer just a tool, it is employed as co-creator reshaping professional workflows and industrial outputs. How are processes, workflows, creative practices and even economies being shaped by these changes?

This minitrack invites multidisciplinary research that explores the applied role of AI in creativity, arts, and innovation. We specifically encourage submissions that provide empirical evidence, case studies, and practical frameworks from diverse fields including computer science, business, game design, and the arts. Work within or across disciplinary boundaries is welcome. Topics of Interest include:

• AI as Co-Creator: Workflow & Applied Design
  1. Case Studies in Co-Creation: Documented workflows of how professional artists, writers, or designers integrate Generative AI into high-level production pipelines.
  2. Prototyping & Iteration: How AI accelerates the fail quickly methodology of iterative design solutions like game design and UI/UX.
  3. Collaborative Dynamics: Measuring the shift in creative agency when using AI for brainstorming vs. final asset generation.
• AI-Driven Innovation: Industry & Market Application
  1. Generative Marketing & Personalization: Practical applications of AI in creating hyper-targeted advertising and dynamic content at scale.
  2. Disruptive Business Models: How startups are leveraging AI to bypass traditional production costs in fashion, film, and digital media.
  3. Industry-Specific AI Tools: Research on “vertical” AI tools designed for specific sectors (e.g., AI for architectural blueprinting, procedural landscape generation in AAA games, or AI-assisted fashion trend forecasting).
• Ethical, Societal & Legal Realities
  1. Labor Market Shifts: Empirical data on how AI integration is changing job descriptions and required skill sets in the creative economy and technical employment patterns.
  2. IP & Ownership in Practice: Real-world legal challenges and solutions for companies using grey-area AI assets in commercial products.
  3. Bias Mitigation in Creative Output: Practical methods for identifying and correcting algorithmic bias in cultural production (e.g., film casting, storytelling, or fashion design).

Minitrack Co-Chairs:

Lindsay Grace (Primary Contact)
University of Miami
lgrace@miami.edu

Hartmut Koenitz
Södertörn University, University of Amsterdam, and Trinity College Dublin
hartmut.koenitz@sh.se

Peter Jamieson
Miami University
jamiespa@miamioh.edu

This minitrack focuses on the impact of AI on the various aspects of the workplace as it exists currently as well as how it may evolve in the future. We seek papers that address the social, technical, behavioral, attitudinal, emotional, or managerial aspects of AI in the workplace. The unit of analysis can be individuals, teams, or organizations. All kinds of research are welcome including but not limited to quantitative, qualitative, conceptual, or design-oriented research.

The focus of these papers can range from the impact of AI on work and its related aspects to the design considerations of AI in the workplace. In short, this minitrack seeks to highlight research that may influence the future of work and act as a springboard for new ideas and innovations in AI that will be disruptive to the workplace.

The “AI and the Future of Work” minitrack is especially interested in the following topics:

• Power shifts between humans and AI
• AI and employees’ mental and physical wellbeing
• Shift in social/role identities with the introduction of AI
• Required skill set for human employees in an era of AI
• AI and the changing face of leadership
• Social relationships and AI at the workplace
• Integration of AI in work practices (knowledge sharing, decision making, etc.)
• Responsible and Explainable AI
• Ethical considerations of AI at the workplace
• Financial and economic implications of AI implementation in the workplace
• The changing meaning of work or work-life balance in an era of AI
• AI task appropriateness
• Designing AI for the workplace
• AI and changes in work settings
• Workplace Analytics and AI
• AI and creativity in the workplace
• Collaboration with AI
• AI mimicking human labor (ChatGPT, Next Rembrandt etc.)

Minitrack Co-Chairs:

Triparna de Vreede (Primary Contact)
University of South Florida
tdevreede@usf.edu

Dominik Siemon
LUT University
Dominik.Siemon@lut.fi

Xusen Cheng
Renmin University of China
xusen.cheng@ruc.edu.cn

Vivek Kumar Singh
University of Missouri–St. Louis
vsingh@umsl.edu

The social and behavioral sciences face persistent methodological challenges. Recruiting human subjects is costly and slow, experimental designs are constrained by limited sample availability and demographic reach, studies are rarely replicated for sound scientific practice , and the cognitive processes underlying human behavior remain difficult to observe. These challenges are compounded by the norms of peer-reviewed journals, which tend to emphasize novel and surprising findings, so that researchers are unable to efficiently share, replicate, and extend a vast majority of each other’s work.

Artificial intelligence is now opening transformative possibilities for how behavioral research is conducted and shared. Large language models, generative agents, and agent-based simulations allow researchers to create synthetic participants that can pilot experiments, approximate cognitive and social processes, and stress-test hypotheses before committing scarce resources to human trials. Equally important, AI-powered tools can make experimental paradigms more portable and reproducible. This is key to enabling researchers across disciplines, institutions, and geographic boundaries to collaboratively test, extend, and replicate past studies at a fraction of traditional costs.

This minitrack invites researchers from psychology, cognitive science, economics, organizational behavior, information systems, and software development to examine how AI can accelerate, democratize, and reshape the collaborative study of human behavior. We seek work that not only demonstrates new AI-powered methods but also critically interrogates their validity, limitations, and ethical implications. Topics of interest include, but not limited to:

• AI-Simulated Participants, Personas, and Cognitive Process Simulation
  1. Using LLMs and generative agents as synthetic participants to pilot surveys, experiments, and behavioral tasks, and the validity and boundary conditions of such approaches
  2. Design and study of AI personas and characters: how synthetic identities with defined psychological profiles, cultural backgrounds, and behavioral traits can serve as proxies for diverse human populations in experimental settings
  3. Agent-based simulations of decision-making, reasoning, bias, trust, and social interaction using AI-driven cognitive architectures, including the modeling of economic behavior, strategic interaction, and emotional and cultural dimensions of behavior
• LLMs as a Judge or Evaluator of Text-Based Responses
  1. Assessing the accuracy, quality, effectiveness, or consistency of LLMs when analyzing text, including comparisons to human raters
  2. Understanding how the quality of judgements or evaluations are affected by prompts, system prompts, or other systematic changes to the request
  3. Conducting analyses on the consistency, reliability, or validity of LLMs when judging or evaluating text
• Collaborative, Reproducible, and AI-Augmented Research Workflows
  1. AI tools that enable researchers to share, replicate, and extend experimental paradigms across teams and institutions, lowering the cost of replication by reconstructing and re-running prior experimental designs
  2. AI-assisted hypothesis generation, literature synthesis, experimental design, and data analysis platforms, frameworks, and case studies demonstrating how multidisciplinary teams use AI to study human behavior at new scales and in novel ways\
• Validity, Ethics, and Epistemology of AI in Behavioral Research
  1. Epistemological status of AI-generated behavioral data: What counts as evidence about human behavior when derived from synthetic participants or AI personas?
  2. Ethical considerations in supplementing or replacing human participants with AI simulations, including risks of bias propagation, cultural blind spots, and over-reliance on AI-simulated cognition
  3. Governance frameworks and reporting standards for AI involvement in behavioral research

Minitrack Co-Chairs:

Mana Azarm (Primary Contact)
University of San Francisco
mazarm@usfca.edu

Johnathan Cromwell
University of San Francisco
jcromwell@usfca.edu

The advent of the Internet, social media, distributed databases, and mobile technologies has led to an exponential growth in data, both structured and unstructured. This diverse data holds substantial business value, encompassing customer information and interactions, superstore and online transactions, competitive intelligence, labor market insights, and development trends across various industries to name a few. Real-time data from sources like Twitter, Reddit, and Facebook adds an additional layer of complexity, requiring instant processing using Artificial Intelligence (AI), Big Data (BD), machine learning, data streaming technologies, and visual analytics.

Despite the potential benefits, many organizations either lack the necessary tools or fail to grasp the full value of their available data. This minitrack seeks to address these challenges by offering a platform for theoretical, conceptual, and applied discussions on the integration of AI and BD. This minitrack will attempt to gain insights into utilizing data to increase sales, identify opportunities, outperform competitors, enhance products and services, recruit talent, improve operations, and make informed forecasts. The main objective of this minitrack is to provide organizations with a comprehensive understanding of leveraging AI and BD for innovative, collaborative, and sustainable development, ultimately facilitating effective decision-making. As the digital landscape evolves rapidly, the minitrack aims to bridge the gap between the wealth of available data and its practical utilization within organizations. This minitrack is designed for researchers, professionals, and practitioners interested in maximizing the potential of AI and BD for organizational growth. It attempts to gain valuable insights into theoretical frameworks, practical applications, and collaborative approaches, equipping them to make informed decisions in a dynamic, data-driven environment. The minitrack provides a platform for in-depth discussions on the transformative power of AI and BD in shaping the future of organizations.

This minitrack invites original research, case studies, and practical implementations focused on, but not limited to:

• Theoretical foundations of AI and BD for organizational growth, collaboration, and sustainability
• AI-driven decision-making, predictive analytics, and business intelligence in operations
• Practical applications of AI and BD for enhancing products, services, collaboration, and workforce management
• Real-time data processing with AI, machine learning, deep learning, and visual analytics
• Challenges and opportunities of AI and BD for innovative, collaborative, and sustainable development of organizations
• AI and BD tools, methods, and technologies for data-driven decision support and business operations
• AI and BD solutions for innovative, collaborative, and sustainable development of organizations
• Ethical and governance challenges in AI-driven decision making
• Technological and human requirements for effective and efficient AI and BD adoption in organizations
• Supporting organizational creativity, collaboration, innovation and decision-making using AI and BD

Minitrack Co-Chairs:

Celina Olszak (Primary Contact)
University of Economics in Katowice
celina.olszak@ue.katowice.pl

Jozef Zurada
University of Louisville
jozef.zurada@louisville.edu

Jan Kozak
University of Economics in Katowice
jan.kozak@ue.katowice.pl

Zahra Hatami
University of Louisville
zahra.hatami@louisville.edu

Online communities consist of individuals who share a common interest and who use the internet to communicate with each other and work together in pursuit of shared interests. Individuals seek out information online for both utilitarian and hedonic reasons. Online forums are one example of a pervasive platform where individuals can submit and receive answers to questions as well as browse the experiences of others. Individuals with questions often turn to these forums, either directly or indirectly (through search engine results), to find answers to problems they face. While research has begun to address utilitarian and hedonic seeking and consumption of information, there is still much left unknown. This mini-track focuses on research related to understanding information processing and decision making in the context of online communities. The following is a list of sample topics (non-exhaustive) that would be a good fit for this mini-track:

• How individuals search for, filter, or adopt online information
• Online decision-making processes
• Cognitive processing related to consumption of online information
• Validation of online content
• Community based cues
• Evaluation of different cue types (e.g., upvotes, star ratings)
• Design elements of tools to support online communities
• Crowdsourced knowledge
• Approaches to increase contributions/engagement
• Novel approaches to support online communities
• Use of AI as a knowledge source

Minitrack Co-Chairs:

Kelly Fadel (Primary Contact)
Utah State University
kelly.fadel@usu.edu

Thomas Meservy
Brigham Young University
tmeservy@byu.edu

Matthew Jensen
University of Oklahoma
mjensen@ou.edu

This minitrack discusses the phenomenon of autonomous intelligent agents and how this next evolution of human-machine collaboration impacts individual, team and crowd dynamics. Decision-makers at all levels of organizations interact with information systems that are designed to enable better, faster, and more effective decisions. The problem is that information has reached critical mass. The sheer volume of data and data sources make it impossible for a human being to process and filter all available and relevant data, facts, figures, etc. Thus, the need for collaborative, human-machine decision-making is increasing. However, it remains unclear how to enable these new forms of effective human-machine decision-making and provide organizations with leverage.

Many intelligent agents (e.g., chatbots, social robots, virtual assistants, code assistants, advice-giving systems leveraging AI) are being incorporated into teams, organizations and daily life. These varied types of AI use text, imagery, audio, or other environmental sensors to retrieve and process information, and respond appropriately to users. Historically, these agents have helped individuals find directions, assist in ordering goods or services on a website, or recommend relevant sources in an otherwise unmanageable pool of information. With the technological progress of AI, agents are becoming more capable and autonomous. Humans are increasingly using intelligent agents for creative and collaborative tasks (e.g., creating royalty-free music with beatoven.ai, improving programming code with ChatGPT, creating summaries of interaction logs with recommendations with Google Gemini, etc.). While more autonomous intelligent agents present a potential solution for many information-processing and decision-making problems, it is not fully understood how humans will interact, utilize, and are impacted by them in ways different from traditional human-to-human collaboration. As intelligent agents advance and are adopted by users, social norms and team dynamics will emerge that will offer diverse user groups various benefits, however, this might also lead to unintended (negative) consequences. Hence, we need to explore new dimensions of these new forms of human-machine collaboration.

This minitrack will examine the emergence of this new type of collaborative, intelligent, autonomous agents and their implications for individuals, teams, organizations, and crowds. We seek papers that address the social, technical, behavioral, attitudinal, emotional, or managerial aspects of intelligent agents, particularly in collaboration settings. The unit of analysis can be individuals, teams, organizations, or crowds. All kinds of research is welcome, including but not limited to quantitative, qualitative, conceptual, or design-oriented research. Topics of interest include:

• Human collaboration with intelligent agents and systems in teams, crowds, and with individuals
• Effects of artificially intelligent technologies on human productivity, collaboration, teams, and decision-making
• Studies on phenomena of interest, such as trust, reliance, autonomy, control, deskilling, persuasiveness, satisfaction, or performance in human-AI teams
• Studies on task delegation and/or knowledge augmentation when collaborating with intelligent systems
• Effects of false information provision by machine teammates and the effectiveness of mitigation approaches
• Individual differences that impact collaboration with and acceptance of artificial intelligence
• Agent-based support for groups including innovative facilitation methods, techniques, and procedures to improve (a)synchronous collaboration between co-located and/or distributed teams
• Studies of team dynamics and team processes when an artificial teammate is on the team
• Design and evaluation of effective intelligent technology as team members including agent-based support (e.g., robots, chatbots) for decision makers
• Design features and principles for the development of a responsible, sustainable, and fair machine teammate
• Neurophysiological approaches to assess interactions with intelligent systems including eye-tracking (e.g. pupillometry), galvanic skin response (GSR), and electroencephalogram (EEG)
• Collaborating with machines and data sovereignty

We particularly invite research that addresses the following provocation statements:

• Teams are dead – all I need are AI collaborators
• Smart AI assistants make teams dumber
• AI agents disrupt and challenge human team members’ core competencies
• AI agents fundamentally destabilize conventional team configurations and the very ontology of what constitutes a ‘team

Minitrack Co-Chairs:

Isabella Seeber (Primary Contact)
Grenoble Ecole de Management
isabella.seeber@grenoble-em.com

Joel Elson
University of Nebraska at Omaha
jselson@unomaha.edu

Stefan Thalmann
Universität Graz
Stefan.thalmann@uni-graz.at

Generative AI (GAI) and large language models (LLMs) have fundamentally reshaped the traditional interaction patterns between humans and AI. In the GAI era, human–AI collaboration has evolved substantially, giving rise to new research questions requiring specific design, evaluation and behavioural studies. Integrating GAI into collaborative systems redefines teamwork in academic, industrial and organisational contexts, significantly improving operational efficiency, adaptive capacity, and the quality of interpersonal interactions. Collaborative designs involving single AI, multiple AI and AI agents is transforming the architecture of collaborative systems and driving technological innovation. GAI is also empowering team collaboration within metaverse ecosystems and online platforms. However, AI collaboration also poses risks: over-reliance on AI can diminish human initiative and critical thinking, while AI hallucinations and misinformation can compromise collaboration outcomes. While AI tools and traditional collaborative platforms have advanced significantly, the synergy between AI and human collaboration remains insufficient. This research gap highlights the importance of combining two key perspectives: design science research and behavioral/empirical studies. It is crucial to clarify how to construct effective AI-enhanced collaborative systems and to establish rigorous methods to assess their real-world impact. The aim of this minitrack is to promote innovative research that will advance the comprehensive development and validation of AI-enhanced collaborative systems and platforms.

We invite authors to submit research exploring AI-enhanced collaborative systems and platforms, drawing on design science, behavioral science and empirical perspectives. Research employing various theoretical frameworks and methodological approaches could focus on system development, evaluation strategies and practical applications of AI-enhanced collaboration in real-world contexts. The minitrack will cover a wide range of topics, including, but not limited to:

• User interface frameworks for shared AI decision-making
• Design principles for explainable collaborative AI
• Interaction design for AI-mediated asynchronous/synchronous collaboration
• Frameworks for evaluating human-AI team effectiveness
• Field study and Longitudinal evaluation for collaborative AI in organizations
• Assessment of AI contribution to team creativity and trust
• Design science approaches to collaborative AI development
• Case studies of collaborative AI system development and applications
• Development methodologies for collaborative AI systems
• AI collaborative systems in metaverse setting

Minitrack Co-Chairs:

Xusen Cheng (Primary Contact)
Renmin University of China
xusen.cheng@ruc.edu.cn

Xiangbin Yan
Guangdong University of Foreign Studies
xbyan@ustb.edu.cn

Weiguo (Patrick) Fan
University of Iowa
weiguo-fan@uiowa.edu

Human-AI collaborations represent transformative frontiers in today’s technology, where AI systems are designed to work alongside humans and enhance human capabilities. Ranging from computer vision algorithms that can identify anomalies in X-rays, to chatbots that provide customer support to generative AI that can draft meeting minutes and emails, human-AI collaborations have permeated almost every sector of our society, paving the way for more efficient, innovative, and personalized solutions. However, the synergy between humans and AI is also raising important ethical considerations on job replacement, trust, privacy, and security. For instance, introducing AI agents to knowledge contribution platforms may reduce the demand for human experts, holding AI accountable for medical misdiagnosis can be challenging, and chatbots might become toxic when users’ reliance on them passes a certain threshold.

As the field of human-AI collaborations rapidly evolves, it is crucial to identify and address these ethical issues to better leverage the strengths of both humans and AI. This minitrack is organized to draw attention to a wide variety of ethical issues relevant to human-AI collaborations and to encourage more intensive research on this emergent topic. It welcomes theoretical, methodological, and empirical research addressing a variety of technical, social, and ethical issues relevant to complex and multifaceted challenges of AI systems in interaction with human stakeholders (e.g., users, developers, and competitors). Key Topics of Interest include, but are not limited to:

• Human-AI Synergy in Online Platforms
• Human-AI Collaboration in Organization Settings
• Trust in AI Systems
• Multi-Agent Systems for Human-AI Collaboration
• Transparency and Explainability
• Bias and Fairness
• Autonomy
• Privacy and Data Protection
• Security and Vulnerabilities
• Copyright and Intellectual Property
• Weaponization
• Generative AI and Large Language Models
• Multimodal Human-AI Collaboration
• Natural Language Processing and Text Analytics
• Job Displacement and Workforce Change
• AI for Vulnerable Populations
• Unintended Consequences of Human-AI Collaboration

Minitrack Co-Chairs:

Dan J. Kim (Primary Contact)
University of North Texas
dan.kim@unt.edu

Victoria Yoon
Virginia Commonwealth University
vyyoon@vcu.edu

Xunyu Chen
Virginia Commonwealth University
chenx@vcu.edu

Abraham Abby Sen
West Texas A&M University
aabbysen@wtamu.edu

The newest generation of generative AI can produce text, speech, images, video and immersive three‑dimensional environments that are almost indistinguishable from material created by humans. This unprecedented realism creates a set of acute dangers. Deep‑fake videos, synthetic audio and fabricated news articles can be weaponized to spread misinformation, impersonate individuals, and manipulate public opinion. Because the artifacts look authentic, they are especially hazardous for groups that have lower digital‑literacy or that are less accustomed to questioning online content; older adults, non‑technical professionals, and audiences in low‑resource settings are all at risk. Detecting these threats therefore depends on the development and deployment of provenance metadata, real‑time verification tools and interface cues that constantly remind users when a piece of media has been generated by an algorithm. Research that empirically maps how people recognize or fail to recognize AI‑generated misinformation, that evaluates the effectiveness of digital‑literacy interventions, and that proposes technical safeguards such as deep‑fake detection pipelines directly addresses this societal need.

Beyond the dangers, this minitrack turns to the considerable opportunities that arise when generative AI is harnessed in a collaborative manner. When AI is treated as a co‑author rather than a black‑box authority, its capacity to create realistic scenarios, visualizations and conversational agents can become a powerful aid in many domains. Mixed‑initiative authoring tools can let a user define high‑level goals, style constraints or factual boundaries while the system drafts content that the user can accept, edit or reject on the spot. AI‑augmented simulation environments can generate virtual characters, scenes, or data sets that evolve in response to a user’s actions, providing immediate, explainable feedback that supports skill development in education, corporate training, emergency‑response drills or creative production. Compassionate or affective agents can model empathy, cultural sensitivity and ethical restraint, yet remain under human supervision so that a user can pause, question or override the system at any moment. Collaborative brainstorming platforms can allow a generative model to supply novel ideas, visual mock‑ups or data visualizations that teams can refine together, amplifying creative output while preserving shared agency.

A central design challenge in all of these possibilities is the maintenance of trust and control. Transparency layers such as visual provenance ribbons, confidence scores or “generated‑by‑AI” badges, explainable‑AI explanations of how a piece of content was produced, and continuous learning loops in which user corrections improve future generations are essential ingredients of responsible collaboration. Equally important are the digital competencies and skills that users must possess to engage safely with generative media. The ability to critically evaluate source credibility, to interpret confidence indicators, to understand the limits of AI‑generated knowledge, and to exercise ethical judgment when co‑creating content are all part of a broader digital‑literacy framework. This minitrack therefore welcomes work that investigates how these skills and competencies can be taught, how they influence the detection of synthetic artifacts, and how system design can scaffold the development of such skills.

The minitrack also invites contributions that explore multimodal interaction (voice, gesture, haptic feedback) that synchronizes human and machine actions, governance structures that allocate responsibility when synthetic artifacts influence decisions, and inclusive design practices that accommodate users with diverse levels of digital competence.

In sum, the minitrack seeks empirical, theoretical, design‑science and methodological contributions that:

• Illuminate the dangers of ever‑more realistic generative AI and how they can be observed and mitigated;
• Explore how human‑AI collaboration, supported by appropriate digital skills, can turn those same capabilities into trustworthy, ethical and socially beneficial tools across any domain of societal importance; and
• Examine the skills and competences we need in the future to handle and manage the development and role of generative AI in human-technology interaction.

Authors of accepted papers will be offered an opportunity to submit an extended version of the paper to a special issue of AIS Transactions on Human‑Computer Interaction (THCI) titled “Human‑AI Co‑Creation of Realistic Media: Risk Mitigation, Opportunity Realization, and Digital Skills for Safe Interaction” The fast‑track submission provides an accelerated peer‑review process and a shortened revision cycle, enabling rapid publication after the conference.

Minitrack Co-Chairs:

Sofia Schöbel (Primary Contact)
University of Osnabrück
sofia.schoebel@uni-osnabrueck.de

Martin Semmann
University of Hamburg
martin.semmann@uni-hamburg.de

Fiona Fui-Hoon Nah
Singapore Management University
fionanah@smu.edu.sg

Triparna De Vreede
University of South Florida
tdevreede@usf.edu

Humans are inherently social beings who communicate through a range of multi-modal means, including audio, visual and physical forms. This social nature heavily impacts how people work together in teams of equals collaborating towards a shared goal. Additionally it also directly impacts the effectiveness of these combined efforts. Robots designed for collaborative work with humans often embody physical systems that share a space with their human counterparts. It is no surprise then that the nature of how humans work collaboratively with other humans, has a heavy influence on how humans collaborate with robots. For example, humans often use similar forms of multi-modal (audio, visual and physical) communication with robots as they do with other humans. Therefore, the design of multi-modal human-machine interfaces is critical to the successful design of collaborative robots.

Effectively designing multi-modal human-machine interfaces is critical for human-robot collaborations as without this capacity robots are less likely to be accepted by humans and treated as equal members of a mixed human–robot team. This prevents the myriad of benefits gained through such work arrangements. To address this challenge, this minitrack seeks to explore the cutting edge of Human–Robot Interaction
(HRI) and the evolution towards seamless Human–Robot Collaboration (HRC). This will make it possible to delve into the forefront of research, where experts unveil the latest findings, methodologies, and technological advancements shaping the dynamic relationship between humans and robots.

This minitrack aims at offering a comprehensive journey through the intricacies of HRI, examining how multi-modal communication, including audio, visual, and physical interactions, plays a pivotal role in fostering meaningful connections. The goal is to provide insights into the innovative design of collaborative robots that coexist harmoniously with humans, sharing spaces and objectives. This minitrack may function as a gateway to the forefront of research, providing a platform for collaboration, knowledge exchange, and inspiration as we navigate the exciting frontier of human-robot collaboration. Topics of interest include, but are not limited to, the following:

• Promoting cooperative and collaborative interaction with robots
• Examining uncooperative and adversarial human interactions with robots
• The role of adoption and appropriation in human–robot interactions
• Empirical studies examining the cognitive, psychological, emotional, and social aspects of human–robot interactions
• The impact of haptic feedback and touch on human–robot interaction
• The role of robot attractiveness on human–robot interaction
• Ethics on human–robot interactions
• Social-emotional models of human–robot interaction
• Theoretical frameworks for human–robot interaction
• Case studies of human–robot interaction
• Design implications for robot interactions at home, work and public spaces Human-oriented practices that promote human–robot interactions
• New methodological approaches to studying human–robot interactions
• The role of individual differences (robot and/or human) in human–robot interactions

Minitrack Co-Chairs:

Sangseok You (Primary Contact)
Sungkyunkwan University
sangyou@skku.edu

Filippo Sanfilippo
University of Agder
Filippo.Sanfilippo@uia.no

Lionel Robert
University of Michigan
lprobert@umich.edu

Connor Esterwood 
Wayne State University
cte@wayne.edu