Collaboration Systems and Technologies

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:

1. Power shifts between humans and AI
2. AI and employees’ mental and physical wellbeing
3. Shift in social/role identities with the introduction of AI
4. Required skill set for human employees in an era of AI
5. AI and the changing face of leadership
6. Social relationships and AI at the workplace
7. Integration of AI in work practices (knowledge sharing, decision making, etc.)
8. Responsible and Explainable AI
9. Ethical considerations of AI at the workplace
10. Financial and economic implications of AI implementation in the workplace
11. The changing meaning of work or work-life balance in an era of AI
12. AI task appropriateness
13. Designing AI for the workplace
14. AI and changes in work settings
15. Workplace Analytics and AI
16. AI and creativity in the workplace
17. Collaboration with AI
18. 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

Augmented, Virtual, and Mixed Reality (VR/AR/MR/XR) technologies change many of the traditional assumptions about computing while enabling unprecedented capabilities in immersion and collaboration. As these technologies gain in adoption, understanding how their differences and unique capabilities interact with human users and collaborative process has the potential to shed light on the unique capabilities, and provide direction for future advances in these technologies. Topics of interest include:

1. Human-Computer Interaction (HCI) concerns including cognitive load, attention and decision-making processes in collaborative environments
2. Understanding how utilizing XR-based digital workspaces to enable collaboration differs from traditional video conferencing and other existing collaborative tools
3. Exploring and addressing considerations related to ethics, privacy and security of XR devices in the context of user tracking and interactions in collaborative environments
4. Investigating how AI enabled adaptive interfaces can provide personalized experiences utilizing behavioral patterns and real time feedback
5. Addressing and remediating barriers related to accessibility and inclusion with the goal of providing equitable participation for users of differing abilities.
6. Utilization of multisensory feedback (tactile, auditory) and haptics to enhance interaction and engagement
7. Integration of digital twins for representation in XR to enhance real time decision making in industries such as healthcare and manufacturing
8. Using representations that provide social presence and embodiment such as avatar realism, spatial audio and body tracking and the effects on user engagement and effectiveness.
9. Interdisciplinary applications of XR in the context of education and training effectives and retention
10. Future directions for XR and emerging trends such as brain-computer interfacing

Minitrack Co-Chairs:

Chris Kreider (Primary Contact)
Christopher Newport University
chris.kreider@cnu.edu

Omar El-Gayar
Dakota State University
omar.el-gayar@dsu.edu

Computer systems that support collaborative idea generation and creative problem solving have a long-standing tradition in the Information Systems (IS) field, with foundational work in the 1980s and 1990s introducing group decision support systems and their variations (e.g., electronic brainstorming systems). During that era, technology was primarily focused on streamlining processes, enhancing gains, and minimizing losses in user-driven activities of idea generation and evaluation.

Today, the advent of generative AI (GenAI) and Retrieval-Augmented Generation (RAG) technologies suggest a potential shift in how technology might engage in creative problem-solving. These advanced systems could play a more proactive role in both generating and evaluating ideas. While the potential advantages of these technologies are clear, questions arise about the extent to which GenAI can truly contribute to innovative and discovery-driven activities. These AI systems are inherently tied to the data within their corpus, allowing them to generate variations of existing business ideas rather than producing genuinely new-to-the-world innovations.

This tension between an amplified role due to technology abilities and the apparent limitations of GenAI’s due to pattern-based reasoning, calls for an exploration of both the opportunities and constraints of human-computer collaboration in creative problem-solving and business innovation. Consequently, the mini-track seeks empirical as well as theoretical studies that explore these opportunities and limitations and provide guidance for collaborative intelligence that drives innovation and creativity in modern business. This minitrack is open to all types of research, conceptual, theoretical, and/or empirical. Examples of possible topics of interest include (but are not limited to):

1. Hybrid Ideation Platforms for Business Innovation: Investigating how human participants and GenAI systems can collaboratively develop, refine, and scale ideas in business settings. Identifying platform features that enhance synergy, streamline workflows, and drive actionable outcomes for organizations.
2. AI-Driven Originality in Business Solutions: Evaluating whether GenAI can produce genuinely original and commercially viable ideas. Comparing AI-generated outputs with human-generated ideas in terms of creativity, innovation, and applicability to real-world business challenges.
3. Human Judgment in AI-Supported Brainstorming: Analyzing how human expertise, intuition, and contextual understanding can refine AI-generated ideas to address algorithmic limitations, ensure relevance, and align with business goals. Exploring the role of human oversight in enhancing the quality and practicality of AI-supported ideation.
4. Organizational Dynamics in AI-Augmented Problem Solving: Examining how team structures, roles, and decision-making processes evolve when AI systems are integrated into group ideation and problem-solving workflows. Assessing the impact on collaboration, leadership, and innovation culture within organizations.
5. Measuring Creative Performance in Human-Computer Collaboration: Developing and validating metrics to assess the quality, feasibility, and originality of ideas generated through human-AI collaboration. Exploring how these metrics can inform decision-making, resource allocation, and innovation strategies in business contexts.
6. Ethical and Intellectual Property Implications of AI-Generated Outputs: Investigating questions of ownership, authorship, and accountability when AI plays a central role in generating creative and innovative outputs. Addressing the legal and ethical challenges businesses face in leveraging AI for ideation and problem-solving.
7. Mitigating Risks of AI-Enabled Malicious Innovation: Exploring how AI systems can be exploited for harmful purposes, such as generating illegal content, evading safety measures, or enabling malicious innovation. Developing strategies for businesses to safeguard against misuse while fostering ethical AI-driven creativity.

High-quality and relevant papers from this mini-track will be selected for fast-tracked development towards Internet Research. Selected papers must expand in content and length in line with the requirements for standard research articles published in the journal. Although the mini-track co-chairs are committed to guiding the selected papers towards final publication, further reviews may be needed before a final publication decision can be made. Internet Research (IntR) is an international and refereed journal that is indexed and abstracted in major databases (e.g., SSCI, SCI, ABI/INFORM Global), with an impact factor 5.90 in 2023.

Minitrack Co-Chairs:

Christy M.K. Cheung (Primary Contact)
Hong Kong Baptist University
ccheung@hkbu.edu.hk

Marten Risius
University of Applied Sciences Neu-Ulm
marten.risius@hnu.de

Christian Wagner
City University of Hong Kong
c.wagner@cityu.edu.hk

Matthew K.O. Lee
City University of Hong Kong
cbmatlee@cityu.edu.hk

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 minitrack:

1. How individuals search for, filter, or adopt online information
2. Online decision-making processes
3. Cognitive processing related to consumption of online information
4. Validation of online content
5. Community based cues
6. Evaluation of different cue types (e.g., upvotes, star ratings)
7. Design elements of tools to support online communities
8. Crowdsourced knowledge
9. Approaches to increase contributions/engagement
10. Novel approaches to support online communities
11. 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

Cybersecurity and Artificial Intelligence (AI) are key domains whose intersection gives great promises and poses significant threats. The nature of AI and Cybersecurity encompasses many domains. While some perspectives are narrowly focused (e.g., point solutions inside an organization identifying threats in a network stream), many are very sweeping and are either collaborative or tackle collaborative domains (e.g., identifying intentional or unintentional cybersecurity threats propagating across collaboration platforms).

Implementing AI and Cybersecurity can also be internal to an organization or broadly collaborative (e.g., organizations working and competing together in adversarial AI research). Conversely, cybersecurity for AI has point solutions internal to organizations and broadly collaborative domains (e.g., collaboratively protecting from adversarial examples in shared data sets or shared models with multi-organizational transfer learning). However, the range and scope of how AI could be used for cybersecurity and how to improve the cybersecurity of AI remain relatively understudied yet critically important areas.

This minitrack focuses on AI and Cybersecurity that works in broader domains, collaborative inter-organizational realms, shared collaborative domains, or with collaborative technologies. The threats being addressed with and/or to AI are intended to be sweeping in nature and of significant societal impact. Broadly, the topics and research areas include, but are not limited to:

1. Novel applications of Artificial Intelligence, Machine Learning, and Deep Learning in Cybersecurity as it pertains to multi-user/multi-organizational collaborative domains and/or systems
2. Adversarial AI/Machine Learning Applications in Cybersecurity that collaboratively span organizations or apply to collaborative systems (i.e., malware, phishing, or any applicable threat/identification domain)
3. Protecting AI that is used collaboratively (i.e., shared data sets, shared models, shared applications) or spans collaborative domains from cybersecurity threats (i.e., adversarial examples, trojans, model inversion
4. Using AI to protect AI in any appropriate wide-reaching setting
5. Novel Collaboration approaches to leveraging and protecting AI in the cybersecurity domain
6. Sharing/disseminating tools, techniques, and applications of AI in Cybersecurity and Cybersecurity for AI that applies to the overarching theme of this minitrack

Minitrack Co-Chairs:

Mark Patton (Primary Contact)
University of Arizona
mpatton@email.arizona.edu

Sagar Samtani
Indiana University
ssamtani@iu.edu

Hongyi Zhu
University of Texas at San Antonio
hongyi.zhu@utsa.edu

Hsinchun Chen
University of Arizona
hsinchun@email.arizona.edu

In today’s rapidly evolving work environments, collaboration technologies are driving digital transformation across industries. The emergence of collaboration technologies coupled with the power of generative AI is opening new avenues for organizations across different contexts, enabling teams to innovate faster and more efficiently. These technologies are not only streamlining work practices but are also democratizing many processes—empowering both technical and non-technical users to contribute to project success.

This minitrack invites researchers and practitioners to explore sustainable, interdisciplinary approaches for enhancing collaborative workflows. We are particularly interested in contributions that address the following topics:

1. Integration of Generative AI and Low-Code Platforms: Investigate how these technologies can streamline collaboration, boost productivity, and democratize software development by reducing technical barriers.
2. AI-enhanced Collaborative Tools: Design and implement automated virtual agents and other AI-driven tools that support decision-making and facilitate seamless team interactions.
3. Evaluation Methods: Develop and apply robust evaluation frameworks to assess the impact of generative AI and low-code platforms on team dynamics, project outcomes, and innovation.
4. Maintaining Shared Focus and Mental Models: Propose frameworks and strategies to help teams maintain alignment and shared understanding in projects enhanced by AI and low-code technologies.
5. Division of Labor Between Humans and AI: Explore how task design and workflow allocation can be optimized when combining human intelligence with the computational strengths of generative AI.
6. Case Studies and Theoretical Analyses: Empirical research (i.e. quantitative or qualitative) and theoretical insights on the use of social signals and AI to facilitate collaboration within software development teams.

By emphasizing the capabilities of generative AI and low-code platforms, this minitrack aims to chart a course for more efficient, inclusive, and innovative software development practices. We look forward to contributions that not only push the boundaries of current technologies but also provide practical insights into building the collaborative tools of tomorrow.

Minitrack Co-Chairs:

Edona Elshan (Primary Contact)
Vrije Universiteit Amsterdam
e.elshan@vu.nl

Eva Bittner
University of Hamburg
eva.bittner@uni-hamburg.de

Sarah Oeste-Reiß
University of Kassel
oeste-reiss@uni-kassel.de

Philipp Ebel
University of St. Gallen
philipp.ebel@unisg.ch

This Minitrack’s aim is to foster research on IS/IT collaboration. Originally, this included using information systems and information technology (IS/IT) to support cross-organizational collaboration and, conversely, cross-cultural and cross-organizational collaboration to create new IT/IS solutions and digital innovation. More recently, we have extended this to the collaboration among humans and Artificial Intelligence (AI).

AI systems can successfully perform human intelligent works with creativity in the fields of valuable activities such as planning, inferencing, researching, drawing, composing, and even collaborating. AI robots become increasingly similar to Human experts and artists in terms of their products and services. Value co-creation resulting from collaboration through human-AI machines interactions grows very fast and becomes an attractive research field. Cases of successful new business models show up from AI-supported collaborations between firms, between departments, between public and private sectors, and even among individuals. Beyond explainable AI, more collaborative research could come from interpretable, comprehensible, transparent, accountable, and responsible AI with privacy and fairness. Achievements from the advance of AI technologies bring new research issues on developing meta-ethics, normative ethics, and application ethics within humans’ intrinsic value system.

Cross-organizational collaboration makes firms achieve respectful innovation through their business partners beyond countries. Cases on intelligent collaboration using big-data analytics, AI development, ChatGPT, and DeepSeek show potential approaches and methods for the firm’s IS applications development. Digital capabilities and their contributions to firms’ sustainability goals are open to academics and practitioners around the world. Empirical research on digital capabilities with collaboration, and successful case studies on entrepreneurship and new business models are enough for invitation in terms of respectful innovation beyond industries and respectful translation over country boundaries.

Integration of people, processes, and information systems across organizational and national borders enables productive teamwork toward achieving mutual goals. Perception of value, trust, and commitment among participants and stakeholders fosters quality in collaboration. Cross-system integration and intelligent collaboration technologies play crucial roles in IS project success or failure. Additional complexity and dynamics evolve through digital innovation activities in which collaborators, across organizations, countries, and cultures, work on new digital products, services, and business models – changing the way to shape innovation success. Digital Innovation has been increasing by organizations seeking to gain competitive advantage by lowering costs, increasing knowledge, and reaching new customers. Perceived value serves as a stimulus for further growth.

Possible contributions regarding Digital Co-Innovation with AI and cross-organizational IS/IT collaboration may include, but are not limited to the following:

1. Collaboration between firms with AI applications
2. Ethical issues on human and AI machine interaction
3. New business models supported by AI technologies
4. Collaborative digital innovation with big-data analytics, AI applications, and Deep Learning
5. Conceptual frameworks of IS/IT collaboration across organizations and borders
6. Motivating factors for IS/IT collaboration
7. Methodologies for studies of IS/IT collaboration
8. Development and validation of IS/IT collaboration instrument
9. IS/IT collaboration studies at the country, industry, firm, project, team, or individual level
10. Comparative cross-country research on IT/IS collaboration
11. Industry-specific and organization-specific case studies on IT/IS collaboration
12. Managing IS/IT outsourcing and offshoring/nearshoring relationships
13. Out-tasking and crowdsourcing in IT/IS contexts
14. Cross-organizational and international IS/IT project management
15. IT/IS-enabled Open Innovation approaches
16. Cross-organizational innovation approaches and processes
17. Collaboration cases on Big-Data analytics for creating customer value
18. Social collaboration using online social networks with AI technology across countries
19. The role of digital collaboration for impact sourcing and other forms of sustainability-focused outsourcing engagements
20. Sustainability enabled and facilitated through digital collaboration
21. Eco-friendly or socio-friendly IS/IT collaboration for ESG management
22. Cross-organizational collaboration cases for carbon neutrality
23. Processes of international and inter-organizational collaboration in IS/IT projects and operations”

Accepted papers will receive an opportunity of fast-track reviews for the Asia Pacific Journal of Information Systems.

Minitrack Co-Chairs:

Ilsang Ko (Primary Contact)
Chonnam National University
isko@chonnam.ac.kr

Daniel Beimborn
University of Bamberg
daniel.beimborn@uni-bamberg.de

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). The topics relevant to this minitrack include, but are not limited to:

1. Human-AI Synergy in Online Platforms: While many online platforms are actively embracing AI — particularly generative AI technologies — to enhance user experience, the human-AI synergy on these platforms requires careful examination.
2. Trust in AI Systems: As AI becomes increasingly capable of performing a wide range of tasks, both user engagement and perceived reliability of AI systems have seen significant growth, thereby amplifying their influence on human decisions and actions. However, given that AI optimization can progress rapidly and in unforeseen ways, ensuring that AI’s objectives and behaviors are consistent with human values and goals poses a considerable challenge. Consequently, it is crucial to conduct thorough research to ascertain when, where, and to what extent human users should place their trust in AI systems and integrate their outputs into decision-making processes.
3. Multi-Agent Systems for Human-AI Collaboration: Multi-agent systems allow AI agents to coordinate with each other. This collaborative approach enables AI to collaborate with humans in more complex domains.
4. Transparency and Explainability: AI systems, which are commonly characterized as a “”black box””, can be difficult to understand or explain, making it harder to earn human users’ trust.
5. Bias and Fairness: AI systems can perpetuate and amplify biases present in the data and the computation architecture used to train them, which can lead to unfair and discriminatory outcomes. This has led to a growing need for research on methods to increase fairness and reduce bias in AI systems.
6. Autonomy: As AI systems become more advanced, there are concerns about their potential to make decisions without human oversight or control. This has led to a growing need for research on methods to ensure the safety and accountability of autonomous AI systems.
7. Privacy and Data Breaches: The use of AI can raise concerns about the collection, storage, and process of large amounts of personal sensitive data making them a target for data breaches and other forms of cybercrime. Methods and implications for protecting individuals’ privacy and data breaches as a result of misuse of AI systems need to be studied.
8. Security and Vulnerabilities: AI systems can be vulnerable to adversarial attacks (e.g., hacking, malware, and other forms of cyber-attack), which can compromise their security and the security of the systems and networks they are connected to. Attackers also manipulate input data or use other techniques to trick the system into making incorrect decisions.
9. Copyrights and Intellectual Property Rights: AI systems can be used to create and distribute unauthorized copies of copyrighted and trademarked material, making it difficult to enforce and protect such rights.
10. Weaponization: AI systems are increasingly being used in autonomous weapon systems, which raises ethical questions about human-AI collaborations in warfare and the possibility of AI being used to create autonomous weapons.
11. Generative AI and Large Language Models: The last few years have witnessed remarkable signs of progress in Generative AI (GAI) technologies, such as ChatGPT and Stable Diffusion.
12. Multimodal Human-AI Collaboration: The rapid integration of large pre-trained foundation AI models has been equipped with multimodal input and output capabilities. These advancements unlock fresh user experiences and pave the way for more adaptive human-AI collaborations.
13. Natural Language Processing and Text Analytics: A wide variety of ML/DL methods, along with NLP, have been used to analyze voice and text in conversation. Nonetheless, existing approaches suffer from technical limitations, calling for more research to advance the state-of-the art in voice/text analytics.
14. Job Displacement: While AI creates new job opportunities in the IT sector, they have rendered some jobs obsolete, profoundly influencing the skills and competencies required for future employment.
15. AI for Vulnerable Population: (mental disorder sufferers, disabled, minor, etc.): While affording universal accessibility, AI’s interaction with vulnerable groups, such as those suffering from mental disorders and disabilities, may raise concerns due to inadequate design or data contamination.
16. Unintended Consequences of Human-AI Collaborations: Unintended consequences can emerge from the complex interplay between human users and AI. Recognizing these unintended consequences is crucial in guiding the development of AI systems toward more equitable, responsible, and beneficial outcomes for society.

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

Babak Abedin
Macquarie University
babak.abedin@mq.edu.au

The advancement of Artificial Intelligence (AI) has evolved beyond its initial role of augmenting human cognitive functions. In particular, recent developments in digital technology have transformed AI algorithms into social entities that not only replicate human intelligence but also simulate human appearance and interactions. These AI-powered virtual beings, commonly referred to as “digital humans,” are designed to engage in human-like communication and behaviors, enabling more immersive and interactive AI applications. This technological progression has facilitated the deeper integration of AI into daily life, fostering more complex and dynamic collaborations between humans and AI systems.

AI-powered digital humans, designed to closely replicate human-like appearance (form realism) and intelligence (behavioral realism), are equipped with interactive capabilities that enhance the quality of human-AI interactions. As AI technologies continue to evolve, they are becoming increasingly integrated into diverse domains, including customer service, healthcare, education, entertainment, marketing, and media. Despite these advancements, both academia and industry have yet to fully establish which technical, social, and individual factors play a critical role in shaping specific outcomes, as well as how, why, and at what level (e.g., individual, service, firm, and industry) these outcomes occur. To address these gaps, this mini-track seeks to explore the utilization, effects, and challenges associated with digital humans as facilitators of human-AI engagement and collaboration in performing various tasks. In particular, as AI transitions from being a mere computational tool to an active social agent, this mini-track highlights the “human-AI collaboration” perspective. It examines the benefits, risks, ethical considerations, technological advancements, and user experiences that influence the processes and outcomes of AI-human interaction.

The current mini-track solicits interdisciplinary discussions that provide deeper insights into how AI-powered digital humans impact the social, economic, and psychological dimensions of human-AI interactions. We invite researchers to contribute their perspectives on this evolving field and participate in discussions that will drive the next wave of AI-human collaboration. This mini-track welcomes empirical, conceptual, and theoretical contributions on (but not limited to) the following topics:

1. Applications of digital humans in various industries (e.g., healthcare, education, retail, entertainment, marketing, and media).
2. The role of digital humans in enhancing human-AI interaction and engagement.
3. Psychological and behavioral impacts of interacting with digital humans on task performance.
4. Emotional and social responses to digital humans.
5. Ethical and legal issues related to the creation and use of digital human representations.
6. Challenges in developing realistic and effective digital humans.
7. The future of human-AI collaboration through digital humans.
8. Application of advanced AI algorithms, such as deep learning, reinforcement learning, and natural language processing, in modeling and simulating realistic human behaviors in digital humans.
9. The potential of humans existing as digital inhabitants in virtual worlds and immersive environments, along with their virtual collaborations.
10. Technological advancements and challenges surrounding interactions with AI-driven digital humans.
11. Exploring societal and organizational level changes and impact in collaborating with digital humans.
12. The dark side of implementing a digital human in collaboration process.”

Minitrack Co-Chairs:

Joonghee Lee (Primary Contact)
Appalachian State University
leej12@appstate.edu

One-Ki Daniel Lee
Virginia Commonwealth University
leeo@vcu.edu

Soo Il Shin
Kennesaw State University
sshin12@kennesaw.edu

Jin Sik Kim
University of Tennessee at Chattanooga
jinsik-kim@utc.edu

Diffusion (adoption, implementation, and utilization) of collaboration technologies has been investigated in many countries and regions around the globe. The diffusion of collaboration technologies is on the rise as globalization drives inter and intra-country collaboration intensity within and across organizations. In many developing countries, synchronous and asynchronous computer support for team members (co-located or virtual) is being enhanced by handheld digital mobile devices in communities of practice and social media environments. Moreover, the emerging technology trends like Metaverse and Generative Artificial Intelligence (GAI), provide new opportunities to further enhance Information technologies enabled collaboration for development. In the Metaverse, teams can engage in immersive virtual meetings and collaborate in real-time in a virtual workspace. GAI can generate content such as reports and data visualizations for applications, even as a team member, which streamlines the collaboration process. These technologies not only break down geographical barriers but also boost the efficiency and creativity of collaborative work globally.

We would like to invite the authors to submit their research from theoretical, technological, social, psychological, behavioral, and design science perspectives. Research deploying different theoretical lenses could focus on process and system design, methods, modeling, and techniques in addressing various aspects of IT enabled collaboration for development. The minitrack will focus on a wide range of topics including but not limited to:

1. IT enabled collaboration in emerging context (e.g. Metaverse) in developing regions
2. Opportunities and challenges in AI/GAI generated and participated collaboration
3. Metaverse and its influence for digital collaboration
4. Digital collaboration technology diffusion case studies in education, business, government, and healthcare organizations in developing regions
5. Global, virtual, AI, distributed, blended, and face-to-face IT enabled collaboration for development at the team and organizational level
6. New generation of digital technology for cross-cultural collaboration
7. Group decision making, negotiation, facilitation, and communication technologies for development
8. Trust, privacy, security issues in digital technology enabled collaboration
9. Social, behavioral, psychological, economic and technical factors influencing IT enabled collaboration for development
10. Information systems, technologies, theories, processes, methods, or models that could be transferred and applied from developed regions to developing regions

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 Fan
University of Iowa
weiguo-fan@uiowa.edu

Persuasive System Design (PSD) is an approach to design interactive information systems that influence user behavior, attitudes, or decisions without engaging in misleading or deceptive behavior. Persuasive systems use psychological strategies to guide users to achieve specific goals, such as adopting healthier habits, making sustainable choices, or interacting more with an information system. Typically, PSD has been associated with gamification and/or digital nudging.

However, the meaning of PSD has changed with the presence and growth of artificial intelligence. AI can tailor persuasive strategies based on how the user behaves, prefers, and feels. AI chatbots (both voice and text) can employ persuasive communication techniques to build trust and engagement, like Alexas wishing you a good day or GPT inquiring if it can help you.

PSD is also increasingly present in 2D/3D meetings, increasing engagement and participation through gamification, social proof, and adaptive interfaces. In this context, virtual meetings are becoming more interactive and effective as AI-powered nudges and personalization encourage active participation. In 3D virtual worlds, virtual avatars and adaptive learning enhance realistic and meaningful experiences, while AI-powered storytelling, behavioral nudges, and gamification keep users engaged.

While PSD in AI can encourage positive behavior, it also raises ethical concerns. It’s crucial to balance influence and manipulation, ensuring that users maintain their autonomy. Privacy and data transparency must be a priority for the protection of user information. In addition, too much persuasion should be avoided to prevent screen addiction and unhealthy engagement. Ethical design ensures that AI remains a tool for positive change, not exploitation.

This minitrack welcomes research involving PSD in AI contexts and involves topics that are not limited to:

1. Data-driven or real-time adaptive and personalized PSD
2. Human factors, needs and preferences for PSD
3. (Generative) AI for PSD
4. PSD for conversational interfaces and speech or voice-based systems
5. Persuasive design of virtual (AI-generated) avatars in 2D and 3D worlds
6. PSD for the greater good, how can PSD facilitate a sustainable and inclusive future?
7. The dark side PSD, how PSD (e.g., dark patterns) can pose dangers to users
8. Ethical and legal aspects of PSD
9. State-of-the-art methods, tools, or instantiations of PSD”

Minitrack Co-Chairs:

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

Dennis Benner
University of Kassel
benner@uni-kassel.de

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

Kai Lim
Hong Kong Polytechnic University
kai.lim@polyu.edu.hk

The rapid advancement of Artificial Intelligence (AI) has become the driving force for improving the process and outcome of individual and group work. In particular, machine learning and deep learning techniques can not only help businesses extract insights from internal and external structured and unstructured data, but also actively reshape how organizations achieve their strategic decisions. In parallel, the rise of digital collaboration technologies – from intelligent workflows to decentralized knowledge ecosystems – has created a new frontier where AI’s capabilities are paramount. For instance, state-of-the-art deep learning models have the potential to support real-time co-creation, contextual adaptation, and multi-stakeholder decision-making.

Current human-AI interaction paradigms remain in their infancy, often constrained to rudimentary applications like chatbots or single-turn query-response systems. These approaches fall short in scenarios requiring adaptive, context-aware collaboration, where dynamic problem-solving and iterative dialogue are critical. While methods such as Zero-shot and Few-shot Learning have expanded LLM’s versatility, they lack the structural frameworks to facilitate the fusion of human expertise or multi-layered decision-making. To bridge this gap, the field is pivoting toward agentic workflows – dynamic architectures where Generative AI operates as autonomous agents, orchestrating tasks, self-refining outputs, and proactively collaborating with humans or other AI agents. These workflows are bolstered by advanced reasoning models, which equip AI with causal, analogical, and abductive reasoning capabilities, enabling systems to parse ambiguity, infer intent, and adapt responses to shifting contexts. When synergized with techniques like Low-Rank Adaptation (LoRA) and Reinforcement Learning from Human Feedback (RLHF), agentic workflows and reasoning models form the backbone of next-generation generative AI. Such systems will not only interpret nuanced human inputs but also anticipate needs, resolve contradictions, and co-evolve with users – ushering in an era of truly symbiotic human-machine collaboration.

This minitrack focuses on how Generative AI, Large Language Models (LLMs), and other related technologies – are supercharging digital collaboration. It explores advanced technologies for AI to supercharge collaboration – not merely as a tool for efficiency, but as a catalyst for rethinking how humans and machines co-create. As businesses leverage these tools for collaboration and management, the synergy is enhanced by incorporating prompt engineering and explainable AI. By investigating how these paradigms reshape trust, power dynamics, and collective intelligence, we chart a path toward collaboration that amplifies, rather than automates, human agency, fostering hybrid teams where humans and AI together reshape digital collaboration in various settings.

Topics of interest include, but are not limited to:

1. Agentic Workflow Design for Human-AI Collaborations
2. Advancements in AI Reasoning Models
3. Real-Time Collaboration Tools and Creativity Support
4. Generative AI and Natural Language Processing (NLP)
5. Explainable AI for Language Models
6. Human Feedback for Model Improvement
7. Iterative Refinement in Prompt Engineering
8. Human-AI Teaming and AI Explainability Techniques
9. Social and human factors in Prompt Engineering and RLHF
10. Ethical Considerations in human-AI collaboration
11. Privacy and security considerations in Parameter-Efficient Fine Tuning
12. Collaborative frameworks with consideration of LLM
13. Lifecycle and strategies for generative AI and LLM projects

Minitrack Co-Chairs:

Jie Tao (Primary Contact)
Fairfield University
jtao@fairfield.edu

Lina Zhou
University of North Carolina at Charlotte
lzhou8@uncc.edu

Philip Maymin
Fairfield University
pmaymin@fairfield.edu