Knowledge Innovation and Entrepreneurial Systems

In today’s knowledge-intensive world, efficient management and utilization of information are paramount for organizational (and sometimes also personal) success. Artificial Intelligence (AI) has emerged as a powerful tool for knowledge management, offering solutions that streamline processes, enhance decision-making support, and facilitate collaboration. This track focuses on the intersection of AI assistants (including chatbots, text-based or voice-based assistants) and generative AI techniques in the realm of knowledge management.

We invite submissions that explore innovative approaches, design science and design theory, case studies, theoretical insights, and practical applications of AI-driven solutions in knowledge management for both organizational and personal life settings. Topics of interest include, but are not limited to:

• AI-powered knowledge retrieval focusing on techniques and systems for efficient retrieval of relevant information from large knowledge bases using AI assistants, both for personal and organizational-level knowledge (including their issues and limitations).
• Use of generative AI for content creation, including applications of generative AI models such as GPT, BERT, and transformers for generating and summarizing knowledge content (including their issues and limitations).
• Personalized knowledge delivery with AI-driven methods for tailoring knowledge delivery to individual users’ preferences and needs, both in personal and work life
• Collaborative knowledge sharing, including platforms and tools leveraging AI to facilitate collaborative knowledge sharing and collective intelligence within organizations.
• AI-based social knowledge creation and use (e.g., social media system architectures, virtual influencers, deepfakes).
• Diversity and ethical aspects, as well as risks and challenges of designing and appropriating knowledge with AI assistants and other AI systems (e.g., information overload, ‘operator hand-off’ problems, technostress, and protection of information assets).
• Changing organizational cultures and structures by integrating AI assistants, generative AI and other AI systems for knowledge management.
• Design, evaluation, and/or use of knowledge management and AI systems and processes to facilitate knowledge creation and sharing and quick problem solving.
• Technology-in-practice outcomes and processes across both technology-centric and socio-centric approaches to generative AI and AI systems design (as related to, but not limited to, various affordance and agency/agential frameworks, computer-supported cooperative work, etc.).

Minitrack Co-Chairs:

Anika Nissen (Primary Contact)
University of Hagen
anika.nissen@fernuni-hagen.de

Stefan Smolnik
University of Hagen
stefan.smolnik@fernuni-hagen.de

Pierre Hadaya
Université du Québec à Montréal
hadaya.pierre@uqam.ca

W. David Holford
Université du Québec à Montréal
holford.w_david@uqam.ca

Entrepreneurs identify and pursue opportunities to create value under conditions of uncertainty. The era of artificial intelligence (AI) in entrepreneurship has begun, with advances in AI bringing new opportunities for value creation and enabling fewer individuals to accomplish more than ever. From entirely new business opportunities to increased efficiency and automation within business processesThis minitrack centers on understanding how artificial intelligence influences entrepreneurship at the micro, meso, and macro levels.

At the micro level, this could include perceptions of AI and individual differences that influence if and how an organization leverages AI capabilities. Are the skills and resources required to grow an entrepreneurial venture evolving in light of the technology, and if so, how are they developed? How should individuals interact with AI, and how do those interactions influence the individuals? What potential risks are associated with using AI for startups and customers? How will AI influence our decision-making processes?

At the meso level, this includes new businesses built on the technology along with startup efficiencies enabled by AI. Frameworks that understand and guide the various ways entrepreneurs can effectively leverage the technology are needed. The technology enables smaller groups of individuals to expand their impact and grow larger firms, yet the influence on organizational structure, organizational culture, and fundraising remains largely unknown.

At the macro level, what is the entrepreneurial ecosystem’s role, and how can the ecosystem provide the training and infrastructure required for entrepreneurs to adopt the technology successfully? Is there a risk of too much AI adoption, especially in the ecosystem coordination process? Additionally, AI is already being integrated into deal vetting processes. Can AI develop intuition, which is essential in assessing investment opportunities? How AI will shape deal analysis and investment decisions remains unexamined. In a field that has been criticized for a lack of inclusivity, will AI create a more level playing field or perpetuate inequitable allocations of funds?

We encourage the submission of both theoretical and empirical papers, and all types of methods (qualitative or quantitative) are welcome. Topics of interest include, but are not limited to, the following:

• Strategies for Implementing AI in Entrepreneurship
• Technology – Task Fit
• Factors Influencing AI Adoption
• Entrepreneurial Training and AI
• AI Adoption and Equity Outcomes
• Ethics and Risks Associated with Entrepreneurial AI Application
• Employee-AI Collaboration
• AI Influence on Organizations
• AI Influence on Business Models
• Opportunity Identification Through AI
• Market Analysis and Forecasting
• Deal Evaluation Through AI
• AI Influence on Investment & Fundraising
• Entrepreneurial Ecosystems & AI
• AI Infrastructure for Entrepreneurs
• AI and Network Efficiency
• AI Influence on Diversity and Inclusivity in Entrepreneurship

Minitrack Co-Chairs:

John Sebesta (Primary Contact)
University of Denver
john.sebesta@du.edu

Hope Jensen Schau
University of California Irvine
schauh@uci.edu

Melissa Archpru Akaka
University of Denver
melissa.akaka@du.edu

Martin Key
University of Colorado Colorado Springs
tmkey@uccs.edu

Computing education (CE), also known as Computer Science Education, is best known as the field leading the conversation about what, how, and for whom Computer Science should be taught. Computational thinking has been a theme emphasized by educators and researchers in the field, but CE addresses the broader impact of computing in society. In recent years, the teaching of computing has moved from being the exclusive domain of higher and graduate education to becoming a subject of primary education. In some countries it is taught as a specific subject and in others it is taught in an interdisciplinary way.

This minitrack encourages submissions from any disciplinary background reporting different kinds of studies: e.g., empirical studies, case studies, methods and techniques, conceptual frameworks, and literature reviews. Beyond the title of the minitrack, the minitrack covers research and practice framed as related to neighboring concepts such as: computing educators, instructional designers, teacher, school administrators, policymakers, and other actors involved with CE:

• People: e.g., studies on the impact of different technologies (digital or not) on the use of computing, student and teacher experience, behavior, performance, etc.
• Educational science: e.g., educational theories behind CE and their application.
• Pedagogy: pedagogical aspects (e.g., collaborative learning, blended learning, cognitive processes, intellectual skills, edutainment, and others) in CE.
• Learning analytics: e.g., tools for measuring skills behind computing, adaptivity and personalization in CE.
• Teaching strategies: e.g., unplugged computing, robotics, visual languages, innovative didactic materials/techniques, new courses, metacognition, etc.
• Theories/concepts/methods: e.g., contributions to the science of CE.
• Digital world: e.g., ethics, equity, and civil rights and their implications concerning the interaction with digital media, socio-cultural relations related to CE, anthropology, civic potentials of being in the digital world, bodies, gender, identity, poetics, and politics in CE.
• Computational Thinking: e.g., general aspects of computational thinking.
• Curricula: e.g., CE for K2-K12, multidisciplinary, connected, and interdisciplinary approaches involving CE, international curricula.

Authors of accepted papers have the option to fast-track extended versions of their HICSS papers to Smart Learning Environments.

Minitrack Co-Chairs:

Wilk Oliveira (Primary Contact)
Tampere University
wilk.oliveira@tuni.fi

Pasqueline Dantas Scaico
Federal University of Paraíba
pasqueline@dcx.ufpb.br

The purpose of this minitrack is to examine the ever-changing landscape of Artificial Intelligence (AI) implementations in the business sector. As AI continues to advance, it brings both unprecedented opportunities and challenges for organizations. We encourage submissions that explore innovative uses of AI in various business fields and sectors, such as finance, marketing, operations, human resources, and health care. Topics of interest may include machine learning, natural language processing, computer vision, and robotics, highlighting how these technologies contribute to better decision-making, increased efficiency, and the promotion of innovation. This minitrack serves as a platform for researchers and practitioners to share their insights, exchange best practices, and discuss the transformative impact of AI on modern business practices. The topics included in this minitrack will cover a broad spectrum of AI applications in business, offering a comprehensive view of the field and encouraging diverse contributions from researchers and practitioners.

In this minitrack, we welcome submissions of full research papers or research in progress, including theoretical as well as practical articles, literature reviews, use cases and case studies, and design science research papers. Topics of interest include, but are not limited to:

• Startups and entrepreneurship in AI
• Strategic decision-making with AI
• Integrating AI into strategic planning
• AI for business model innovation
• Human-AI collaboration in decision support
• AI in Marketing and Customer Experience
• Chatbots and virtual assistants
• AI-driven marketing analytics
• Automation of manufacturing processes with AI
• Fraud detection and risk management with AI
• AI in Finance and Investment
• AI for talent acquisition and recruitment
• Employee engagement and performance analysis
• AI-driven HR analytics and decision support
• AI and business ecosystem transformation
• AI and Business Innovation
• Organizational readiness for AI adoption
• Best practices and case studies of successful AI integration
• Regulatory frameworks for AI applications
• Data privacy and security in AI-driven businesses
• Ethical guidelines for AI in business

Minitrack Co-Chairs:

Safa’a AbuJarour (Primary Contact)
American University of Sharjah
sabujarour@aus.edu

Mohammed AbuJarour
XU Exponential University of Applied Sciences
m.abujarour@xu-university.de

The field of Knowledge Management (KM) has been undergoing a fundamental transformation. On the one hand, the ever increasing computational capacities and features of digital technologies, such as Artificial Intelligence, are changing how we collaborate, communicate and exchanging knowledge, thereby confronting organizations with increasingly powerful KM systems. On the other hand, new and emerging topics such as Responsible Knowledge Management, Spiritual Knowledge Management or KM in purpose-driven organizations are gaining traction in research and practice.

What do all these changes mean for the way we understand and study knowledge and KM in organizations? This minitrack is intended to bring together novel ideas to explore the future role of KM research and KM practice in a changing and increasingly dynamic digital world. Our overall goal is to define a research agenda for KM of the future and showcase how modern and innovative KM research looks like.

Researchers and practitioners are invited to share and present their ideas and proposals which topics KM research should address in the next decade. We welcome submissions for this minitrack adopting different theoretical lenses and worldviews, using a variety of research methods and conceptual ideas, and exploring the topic with a visionary mindset. Topics covered include (but are not limited to):

The future role of KM in Artificial Intelligence

• KM as the foundation for Artificial Intelligence – or Artificial Intelligence as enabler for KM, or both directions?
• How should we integrate AI-based systems into KM initiatives as they possess increasing processing capabilities and degrees of agency?
• How much knowledge is in digital representations of knowledge, such as knowledge graphs?
• What is the role of human knowledge, competence and expertise in hybrid work systems including AI?
• How does AI enable new forms of learning processes between humans?
• How do training data sets for AI-based systems imply organizational biases and thus influence future learning processes?

The further development of new approaches and ideas currently emerging in KM

• How can new approaches to KM, such as Responsible KM, be realized by means of concrete tools, techniques, and methods?
• KM and Spirituality – what is their link? What role could Spiritual KM play in the future of KM?
• What are epistemological alternatives to the prevailing paradigm of instrumental-calculative rationality, i.e., to the reliance on rational knowledge and thinking?
• How to enable the knowledge flow of non-rational knowledge for individuals and/or in organizations?
• What is the role of practical wisdom (i.e., phronesis) in managing organizations?
• How can the realization of an organization’s purpose and KM be connected?
• How can KM support topics such as Organizational Becoming or Organizational Self-Enactment?
• What is the role of tacit knowledge and how can the use of tacit knowledge be further improved in organizations and at the individual level?
• What is the role of unlearning in envisioning as well as adopting new KM practices?

The future role of KM in society

• In what ways do new digital technologies change how people and organizations communicate and collaborate, and how does this change KM?
• How and to what extent should we expand established KM frameworks to account for new digital technologies?
• How can insights from KM research and practice enable remote work?
• What role should KM play in an increasingly connected world and how to ensure that role is realized?
• How can KM research tackle and contribute to solving the grand challenges of our times?
• What role does KM play in the transformation towards sustainable business?
• Business Ethics and KM – what can KM contribute to doing well by doing good?

This minitrack welcomes all types of contributions, both conceptual and empirical, using a variety of methods to provide new insights into the first steps of a research agenda for KM of the future.

Minitrack Co-Chairs:

Alexander Kaiser (Primary Contact)
Vienna University of Economics and Business
alexander.kaiser@wu.ac.at

Florian Kragulj
Vienna University of Economics and Business
florian.kragulj@wu.ac.at

Thomas Grisold
University of St. Gallen
thomas.grisold@unisg.ch

Game-based learning (GBL) broadly refers to learning and education that involves characteristics of games and play in their design, pedagogy, praxis, culture or teacher and learner experience. Game-based learning is a broad field of research and practice and contains under its umbrella for example the following key concepts: educational games, serious games, gamification of learning, games-with-a-purpose, science games, simulation games, smart toys, and others.

Gamefulness has increasingly become an integral part of any learning and education, and its rise is based on the premise that games are able to invoke beneficial engagement, interest, and motivation as well as cognitive, emotional, and social skill development.

This minitrack encourages submissions from any disciplinary background reporting different kinds of studies: e.g., empirical studies, case studies, methods and techniques, conceptual frameworks, and literature reviews. Beyond the title of the minitrack, the minitrack covers research and practice framed as related to neighboring concepts such as educational games, serious games, gamified learning, single and multiplayer games for learning, simulation, and training games, gameful design, etc. Studies should cover, but are not limited to, the following issues:

• Users: e.g., studies on the effects of GBL on usability, engagement, motivations, user/player types, individual differences, user modeling, etc.
• Educational science: educational effectiveness of GBL, educational theories and their application in GBL, games as assessment tools, games as educational research tools, pedagogical models for GBL, instructional design of GBL, learning mechanisms in GBL, emotions and affective interaction, etc.
• Computing: Computational aspects involving GBL, e.g., tangible/physical computing, artificial intelligence, algorithms, frameworks, architectures, etc.
• Technology: e.g., GBL in mobile games, procedural content generation, etc.
• Arts & Design: e.g., user interface design, emotional design, design of learning mechanics, mapping instructional methods to game mechanics, inclusive design and accessibility issues, narratives, media, and & languages in GBL, localization in GBL, etc.
• Culture: e.g., socio-cultural relations involving GBL, Anthropology, and poetics in GBL, bodies, gender, and politics in GBL, etc.
• Pedagogy: pedagogical aspects of GBL, e.g., personalization, collaborative learning, blended learning, cognitive process, intellectual skills, edutainment, etc.
• Learning analytics: e.g., adaptivity and personalization in GBL, studies involving GBL in the learning analytics process, game-mechanics adaptation based on learning data, etc.
• Health: e.g., GBL for health, effects of GBL in health, health literacy, behavioral change, etc.
• Theories/concepts/methods: i.e., contributions to science around GBL.
• Ethics: i.e., general ethical aspects involving GBL.

The Game-based learning minitrack is part of the Gamification Publication Track aimed at persistent development of gamification research. Authors of accepted papers have the option to fast-track extended versions of their HICSS papers to:

• Smart Learning Environments
• Internet Research
• AIS Transactions on Human-Computer Interaction

Minitrack Co-Chairs:

Wilk Oliveira (Primary Contact)
Tampere University
wilk.oliveira@tuni.fi

Maximilian Altmeyer
German Research Center for Artificial Intelligence
maximilian.altmeyer@dfki.de

Juho Hamari
Tampere University
juho.hamari@tuni.fi

Isabella Aura
Tampere University
isabella.aura@tuni.fi

The higher education sector must constantly evolve to keep up with the latest technological advances. In particular, the rapid deployment of new tools based on Gen AI has reawakened the challenge of adopting new tools. Investigating and understanding the implications of Gen AI in higher education is critical, as well as exploring how to adapt the educational environment to ensure that the next generation of students can benefit from Gen AI while limiting its negative consequences.

This minitrack explores how generative AI (Gen AI) affects higher education at various levels and how it shapes teaching and learning in higher education. It intends to promote a discussion of the experience and consequences of using Gen AI in curriculum and course implementation and its impact on institutions, instructors, and students which hopefully will help create a pathway for standard regulation of disruptive technologies such as Gen AI. Potential topics in our minitrack may include, but are not limited to:

The institutional levels:

• Case studies on Gen AI policy and practice within and across institutions
• Innovations in Gen AI from higher education
• AI literacy and digital skills

The program/curriculum level:

• Gen AI’s effect on assessment and accreditation
• Implementing Gen AI into a college curriculum across disciplines

The course level focus:

• Using Gen AI in classrooms, assignments, and assessments.
• Different disciplines (Art, Mathematics, Computer Science, etc.) and Gen AI
• Learning process and outcomes and Gen AI

The instructor-level focus:

• Integrating Gen AI in the classroom activities and assignments
• Tech skills and Gen AI
• Pedagogy and Gen AI

Student-level focus:

• Use of Gen AI and ethics
• Case studies on student behavior with Gen AI
• The expectations of Gen AI use in college classroom

Multiple stakeholders’ perspectives

• Ethical use of Gen AI in higher education
• Inclusivity and equality in the context of Gen AI in higher education
• Gen AI in shaping learning and teaching
• Aligning interests among multiple stakeholders in the use of Gen AI (Industry/Employers, University Staff, Students)

Minitrack Co-Chairs:

Minna Rollins (Primary Contact)
niversity of West Georgia
mrollins@westga.edu

Marco Carratù
University of Salerno
mcarratu@unisa.it

Irida Shallari
Mid Sweden University
irida.shallari@miun.se

Xin Skye Zhao
University of Sheffield
xin.zhao@sheffield.ac.uk

There are many ways in which knowledge and the systems in which it resides can have a negative impact on others. For example, ill-motivated actors can create fake news or engage in trolling to manipulate and deceive. Governments and corporations can leverage their resources to engage in “surveillance capitalism” to invade privacy. Other nefarious uses of knowledge include cyberbullying and the perpetration of addiction. The recent introduction and widespread of generative AI has exponentially increased all of these undesirable uses of knowledge. Beyond the many ways in which individuals and organizations might exploit knowledge itself, there are also many problems that are caused when people are empowered by knowledge and systems to act in dysfunctional ways.

The aim of this minitrack is to improve understanding of the so far largely neglected dark side of knowledge by encouraging researchers from a variety of fields to share their work. The dark side of knowledge has implications for individuals, communities, organizations and many aspects of society, thus we do not limit the minitrack to:

• Theories, models and classification frameworks that shed light on the dark side of knowledge.
• Methods for studying the dark side of knowledge and its impact on individuals, communities and organizations, and on many aspects of society.
• Understanding how individuals, communities and organizations can minimize, prevent or respond to the dark side of knowledge.
• Understanding what motivates individuals, communities and organizations to deliberately engage in dark side behaviors and practices.
• Examining dark side outcomes, behaviors and practices that accidentally or unintentionally emerge.
• Approaches to lobbying, regulating and controlling dark side behaviors and practices.
• Region, sector and industry-focused studies on the dark side of knowledge.
• Specific phenomena, including but not limited to surveillance capitalism, fake news, online bullying, conspiracy, hashtag hijacks, reputation blackmail, online firestorms, tweet-up disasters, activities on the darknet/dark web/deep web

Minitrack Co-Chairs:

Jan Kietzmann (Primary Contact)
University of Victoria
jkietzma@uvic.ca

Ian McCarthy
Simon Fraser University
imccarth@sfu.ca

David Hannah
Simon Fraser University
drhannah@sfu.ca

Research in this minitrack investigates the current state of research in measuring knowledge systems impact and knowledge-based initiatives. As artificial Intelligence (AI) and associated systems are designed to augment and replace human actors, it is becoming increasingly important to identify and explore how knowledge artifacts contribute to the successful implementations of these systems as well as how such systems impact both knowledge-based initiatives and organizational performance.

This minitrack encourages paper submissions from researchers and practitioners exploring value, performance and success measurement aspects of knowledge and AI systems. Topics of interest include but are not limited to:

• Impact of knowledge management strategy, organization, systems, culture, and other issues on knowledge management success
• Frameworks and models for assessing knowledge management systems
• The role of knowledge artifacts in AI and augmented reality systems
• The role of knowledge applications for digital transformation
• Methodologies and processes for measuring knowledge and AI system success and performance
• Organizational effectiveness/efficiency due to knowledge management/organizational memory/organizational learning, knowledge and organizational memory use
• The development of metrics and key performance indicators for evaluating knowledge systems
• Benchmarking of knowledge-based initiatives
• Case studies of knowledge and AI system success and performance measurements
• Measuring knowledge management performance in global organizations and globally dispersed communities
• Effectiveness and/or efficiency of knowledge and AI systems
• Modeling and measuring the impact of social software on knowledge management performance
• Anecdotes and user stories and their theoretical basis to facilitate the value of knowledge-based initiatives
• Developing grounded theory approaches to valuing knowledge-based initiatives
• Understanding knowledge-based initiatives’ activities and output as service offerings and exploring their productivity
• Usage, adoption and success of knowledge management methods

Minitrack Co-Chairs:

Stefan Smolnik (Primary Contact)
University of Hagen
Stefan.Smolnik@FernUni-Hagen.de

David Croasdell
University of Nevada, Reno
davec@unr.edu

Murray Jennex
West Texas A&M University
mjennex@wtamu.edu

The path from innovation to marketplace is far from simple, and nascent businesses do not easily become success stories. Entrepreneurs today depend on information systems and effective knowledge management to navigate their path towards success.  This mini-track focuses on the theoretical foundations, underlying technologies, and applications of these systems and processes.

This minitrack welcomes research and cases on the role of information systems and knowledge management in innovation and entrepreneurship. We are particularly interested in the impact on innovation and entrepreneurship by advancements in information collection (e.g., sensors, IoT, and tacit knowledge creation), interpretation (e.g., decision systems, deep learning, and generative artificial intelligence), and management (e.g., education, ecosystems, and regulatory policy), and encourage papers that integrate theoretical and empirical aspects.  Submissions on theoretical underpinnings of innovation and entrepreneurship, as well as literature reviews, must tie their findings to such systems. This mini-track also welcomes papers on the education of entrepreneurship students and practitioners in knowledge management and information system technology. Topics relevant to the mini-track include the following:

• Knowledge creation and management in innovation and entrepreneurship
• Information systems and knowledge management in:
  • digital entrepreneurship
  • intrapreneurship (corporate entrepreneurship)
  • research translation (academia)
  • social entrepreneurship
• Knowledge management and ideation, opportunity discovery, and design thinking
• Artificial intelligence in innovation and entrepreneurship: technology and policy
• Knowledge management and innovation in entrepreneurial ecosystems
• Open, collaborative, and visualization systems in entrepreneurship
• Digital entrepreneurship: digital products, services, tools and business models
• Successes and failures: cases and lessons learned
• Innovation and entrepreneurship education in the classroom and the field
• Incubators, accelerators, and maker spaces as hubs for knowledge creation
• Regulating the risks of innovation and entrepreneurship

This minitrack offers fast track opportunities in the Journal of Small Business Management and in the Journal of the International Council for Small Business.

Minitrack Co-Chairs:

Cesar Bandera (Primary Contact)
New Jersey Institute of Technology
cesar.bandera@njit.edu

Katia Passerini
Seton Hall University
Katia.Passerini@shu.edu

Michael Bartolacci
Penn State University – Berks
mrb24@psu.edu

Sadan Kulturel-Konak
Penn State University – Berks
sxk70@psu.edu

This minitrack focuses on examining the nature and role of knowledge flows (e.g., knowledge exchange, transfer and sharing) across people, communities, networks and organizations, as well as across both space and time. Technical, managerial, behavioral, organizational and economic perspectives on knowledge flows will be accepted and presented in this minitrack, and both qualitative and quantitative research methods are welcome. Potential topics that this minitrack will address include:

• Technical, managerial, behavioral, organizational and economic challenges and perspectives on knowledge flows
• The effects on knowledge flows of the consumerization of IT (CoIT); Internet of things (IoT); social media, social computing, social networks and communities, communities of practice (CoPs); information and computer technologies (ICTs); knowledge reuse; organizatios; artificial intelligence (AI), machine learning and robotics, Robotic Process Automation (RPA); neuroscience, brain-computer interfaces, artificial humans and other computer-based entities
• Knowledge system analysis, design, test, evaluation, implementation, maintenance and redesign
• Harnessing, analyzing, visualizing and measuring knowledge flows for creativity, innovation, competitive advantage

Minitrack Co-Chairs:

Paul Shigley (Primary Contact)
Naval Information Warfare Center Pacific
Paul.r.shigley.civ@us.navy.mil

Clare E. Morton
Naval Information Warfare Center Pacific
Clare.e.morton.civ@us.navy.mil

Mark Nissen
Naval Postgraduate School
MNissen@nps.edu

Augmented intelligence is a new perspective on artificial intelligence (AI), social computing, machine learning, big data, data mining, and related areas. It aims to augment human intelligence that includes not only cognitive intelligence but also emotional intelligence, social intelligence, and physical intelligence. In recent years, more and more practitioners and researchers have started to accept this more holistic view of AI and to demonstrate that the ultimate goal of AI is not to replace humans but to augment humans.

This minitrack welcomes studies that investigate augmented intelligence from both technical and social behavioral perspectives. It welcomes papers in all formats, including empirical studies, design, theory, theoretical framework, case studies, etc. Potential topics include, but are not limited to:

• Large language models
• Generative transformer architecture
• Generative models
• Generative design
• Reinforcement learning
• AI Constitution
• Anthropic AI Ethics
• Algorithmic accountability
• Responsibility for AI
• Ethical AI development
• AI governance
• AI laws and regulations
• AI and social computing
• AI, big data and business analytics
• AI and knowledge management
• AI, digital inclusion and social inclusion
• AI and the future of work
• AI in digital economy
• AI in healthcare
• BDA-based systems and AI-based systems
• Human–AI hybrids
• Human and AI complementarity
• Trust towards AI
• Users’ fear of AI
• Use of robots and the impacts on workplace
• Biases in machine learning models and best practices
• Biases in robotics and best practices
• Business applications of AI systems
• Value-Sensitive Design and AI system design
• Ethical issues in AI development and use
• Organizational adoption and use of AI
• Regulations and public policies related to AI

Minitrack Co-Chairs:

Yibai Li (Primary Contact)
University of Scranton
yibai.li@scranton.edu

Zhenghui Sha
University of Texas at Austin
zsha@austin.utexas.edu

Xuefei Nancy Deng
California State University, Dominguez Hills
ndeng@csudh.edu