Symposia, Workshops, and Tutorials

This tutorial will provide participants with a solid introduction to the opportunities and challenges of text mining, using open-source the open-source programming languages R and Python. Participants will also be introduced to the role techniques demonstrating during the tutorial play in understanding issues in ethical AI, AI safety, cybersecurity, and inclusion. At the end of this tutorial, participants will be able to:

• discuss the role of text mining in big data analytics;
• understand the main opportunities and challenges in text analysis;
• articulate several conceptual approaches to text analytics;
• explain how to automate the process of collecting textual data;
• describe and use several techniques to analyze large scale textual data;
• describe the role of open-source languages R and Python in text mining;
• apply generative AI and large language models (LLMs) to text analytics tasks, including prompt engineering and RAG-based workflows.

The tutorial will first present a variety of exploratory (inductive) and confirmatory (deductive) text mining techniques, demonstrating how traditional approaches inform and contrast with modern embedding and LLM-based pipelines. It will be structured into four streams. While there are clear overlaps between these streams, each represents a different set of conceptual and technical approaches to text mining and analytics. After a brief introduction to the open-source languages and tools used in the tutorial, we will present hands on navigation through the four text analytics streams (with demonstrations using our new dataset), structured into four sessions:

Stream 1 Traditional “Bag-of-Words, Count and Rule-Based Approaches”: is for researchers interested primarily in statistical “bag of words” and count-based approaches to text mining, including exploratory (inductive) keyword and phrase frequency analysis, term-frequency by inverted document frequency (tf*idf), correspondence analysis, and confirmatory (deductive) and rule-based approaches such as the development and application of dictionaries, including sentiment analysis.

Stream 2 Syntactic Parsing and Unsupervised Machine Learning: will present an overview of syntactic parsing and NLP-based approaches to text mining, including Named Entity Recognition (NER); and unsupervised Machine Learning approaches, such as: topic modeling and k-means clustering.

Stream 3 Supervised Machine Learning and Deep Learning: will present an overview of supervised Machine Learning (ML) including: predictive regression and classification models using naïve Bayes, Support Vector Machines (SVM), and random forests; and Deep Learning (DL) approaches including: Dense Neural Networks (DNN), Long Short-term Memory (LSTM) networks, Convolutional Neural Networks (CNNs).

Stream 4 Word Embeddings, Large Language Models, and Transformers: will present an overview of word embeddings, large language models, and transformer models, including Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-Trained Transformers (GPT), and Text-to-Text Transfer Transformers (T5) models. These techniques all underly the generative AI systems like ChatGPT. We will include a discussion of prompt engineering for generative AI by demonstrating the capabilities of GUI-based RAG-AI applications like NotebookLM.

All tutorial participants will be asked to prepare for the tutorial in advance by downloading Anaconda Navigator, as their Python environment and package manager, and R and Positron (the new data science IDE from Posit (formerly RStudio). Participants will be encouraged to use Positron for their Integrated Development Environment (IDE) for both R and Python. Anaconda Navigator will also provide access to Jupyter Notebooks for use as an IDE.

A private GitHub repository with the data, scripts, and other materials for use during the tutorial will be available for the participants prior to the conference. Data for the HICSS-60 tutorial will include textual datasets focused on cybersecurity and/or AI (e.g. AI governance documents, RFI comments on NIST AI RMF, CERT incident reports, and CVEs).

SWT Leaders

Derrick Cogburn (Primary Contact)
Ameican University
dcogburn@american.edu

Tahir Ekin
Texas State University
tahirekin@txstate.edu

Haiman Wong
Purdue University
wong424@purdue.edu

This workshop brings together researchers, data scientists, domain experts, and AI practitioners to examine the critical, often underappreciated challenges of preparing diverse and volatile scientific data for use in increasingly AI-augmented systems. The agenda will be structured around three core themes:

1. State-of-the-Art Data Preprocessing Methods for Generative AI
Solicit presentations for in-progress work related to pipelines for cleaning, transforming, and representing heterogeneous data types for use in AI systems. Presentations will highlight advances in data normalization, missing data imputation, multimodal embedding strategies, and metadata standardization that enable downstream AI ingestion.

2. Current Limitations and Challenges of Applying AI to Scientific Datasets
Panel discussions and lightning talks will candidly address where AI data pipelines fall short when confronted with real-world scientific data. Topics will include: the brittleness of AI models when encountering domain-specific or low-volume datasets; challenges of data provenance and reproducibility; limitations in handling variable quality, resolution, and schema; biases introduced during preprocessing; and the lack of shared standards across disciplines that hinders interoperability and model generalizability.

3. Emerging Research Directions and a Community Research Plan
The workshop will culminate in a structured collaborative session where participants identify priority research gaps and co-author a living research agenda. This plan will propose concrete directions to advance preprocessing methodologies, community data standards, and AI architectures capable of meaningfully ingesting data across types, formats, and quality levels.

Participants will leave the workshop with:

• A working understanding of the current landscape of data preprocessing techniques and their applicability to AI workflows across scientific domains
• Concrete awareness of the technical, methodological, and standards-related barriers that limit AI’s utility on heterogeneous scientific data
• Familiarity with emerging frameworks, ontologies, and tools being developed to address these barriers
• A collaborative perspective on where the field most urgently needs investment in research, tooling, and community coordination
• Co-authorship opportunity with a collaborative and interdisciplinary team of researchers for future publications.

SWT Leaders

Jeren Browning (Primary Contact)
Idaho National Laboratory
jeren.browning@inl.gov

Victor Walker
Idaho National Laboratory
victor.walker@inl.gov

Ross Kunz
Idaho National Laboratory
Matthew.Kunz@inl.gov

Modern social science is facing a paradigm shift due to the development of computer and Internet technologies. Human behavior and social phenomena are possible to be quantified by big data digitally tracing online activities and mobility records at a granular level. In some cases, big data can be analyzed using technologies evolving in the natural sciences, such as physics, chemistry, and biology. Experimental data and multiple results from theoretical and computational simulations complement them. Both theoretically and analytically grounded insights may open new doors of computational social sciences.

The scope of this mini-symposium includes, but is not limited to, big data applications, big data collection and use, an integrated framework for theory, simulation, statistics, and experiments.

SWT Leaders:

Isamu Okada (Primary Contact)
Soka University
okada@soka.ac.jp

Fujio Toriumi
University of Tokyo
tori@sys.t.u-tokyo.ac.jp

Dongwoo Lim
Kansai University
imuu@kansai-u.ac.jp

A social robot or smart toy is a physical robot or toy component that connects to one or more cloud services through networking and sensory technologies to enhance a traditional robot or toy’s functionality. It can capture the user’s physical activity (e.g., walking, standing, running, etc.) and store personalized information (e.g., location, activity pattern, etc.) using a camera, a microphone, and sensors.

This symposium aims to cover research issues of robotic and toy computing from technical and non-technical perspectives, such as security, privacy, ethics, and data protection.

SWT Leaders:

Patrick C. K. Hung (Primary Contact)
Ontario Tech University
patrick.hung@ontariotechu.ca

Marcelo Fantinato
University of São Paulo
m.fantinato@usp.br

Farkhund Iqbal
Zayed University
farkhund.iqbal@zu.ac.ae

Because HICSS‑60’s community explicitly spans information systems, AI, and applications, it is an ideal venue for tracing AI’s decade‑by‑decade connections to the co‑evolution of hardware and networking, algorithms, and institutional practices—connections that more narrowly focused AI conferences typically bracket or treat in isolation. From this vantage point, an “AI Gestalt” view of its history becomes essential: only by seeing these strands together can we understand how AI has actually developed across multiple epochs. In this sense, the AI Gestalt has always been greater than the sum of its parts, not only because of the technologies themselves but also because of the unanticipated brilliance and insight of the researchers and engineers who shaped them.

Since the August 31, 1955 Dartmouth proposal by John McCarthy and his colleagues that launched AI as a field, each successive decade has seen this AI Gestalt grow more explosive, as advances in hardware, networking, algorithms, and institutions have interacted in ways no one could have anticipated at the outset. This historical view shows that AI was never siloed but instead evolved through interconnected architectures and addressed concrete scientific and engineering problems across a variety of domains.

The period from approximately 1970 to 1994 was a particularly vibrant era for AI research and applications. During these years, rule‑based and expert‑system technologies gained prominence in commercial contexts, marking a shift from experimental prototypes to real‑world deployment. This maturation of the field was recognized with the 1994 Turing Award granted jointly to Dr. Edward Feigenbaum and Raj Reddy for their foundational contributions to large‑scale expert and knowledge‑based systems that demonstrated AI’s practical and economic potential. This perspective also reframes the years from 1990 to 2005 not as a lull or “AI winter,” but as a period of consolidation in which methods, tools, and infrastructure quietly evolved to support the emergence of today’s large‑scale intelligent systems.

From 2005 to the present, this history highlights the central role of neural networks, deep learning, and Large Language Models (LLMs). In this era, AI applications can be understood along a spectrum defined by the number of parameters, the size and nature of training data, and the targeted user functionality. At the low end are Small Language Models (SLMs) and their applications—for example, domain‑specialized models that assist scientists in protein design, materials discovery, or analysis of legal and medical documents within tightly constrained corpora. At the other end are broadly capable LLMs such as ChatGPT‑4, which support open‑ended dialogue, code generation, multilingual translation, and creative writing, and are increasingly being embedded in productivity tools, educational platforms, and enterprise workflows.

The most important things participants will learn is the co-evolution since 1955 of AI research and applications; across the board computer hardware; global introduction of computer science and engineering in universities and colleges; the increasing collaboration among scientists, industries and commercial interests in AI with the arrival of early networks like the ARPANET, TYMNET, TELENET, and finally the Internet. And most importanty, the fact that AI was never siloed and has always progressed synchronously with a dependence on multiple technologies.

Taking hardware an example: The participants will learn that the transistor was invented in 1949, and that the early computers’ hardware was manufactured using small tubes, andor transistors. The integrated circuit arrived in early 1960s, and that in about 1975–1978, new general‑purpose computers were, in practice, finally fully chip‑based (IC logic plus semiconductor RAM, while legacy systems were still being used. These descriptions will include memory sizes, storage capacity, and processor speed as a function of Moore’s Law. Thus pointing out that in spite of the limitations imposed by hardware generation-by-generation, AI research, applications and commercial use accelerated in such a way as to encourage hardware manufacturers to innovate with a guaranteed substantial return on research and investment. There will be similar takeaways in the other areas mentioned above.

In sum it is hoped that at the end of this SWT the participants will understand and appreciate the it is this long history that is gifting to them the opportunities the current AI manifests. And that 50 years from now what AI will have become will be like comparing today’s computers to those manufactured from tubes and transistors.

SWT Leader:

William Yeager
Stanford University, retired
byeager@fastmail.fm

This workshop ​is designed to unpack the case for healthcare digital twins, how we can develop suitable digital twins,​ and identification of significant challenges and areas of concern such as human-computer interface design, personalization, transparency, explainability, validation, patient safety, privacy, ethical, legal, and regulatory obligations. It will go behind the curtain of digital twins for healthcare and look at the good , bad and ugly.

SWT Leaders:

Nilmini Wickramasinghe (Primary Contact)
La Trobe University
n.wickramasinghe@latrobe.edu.au

Elliot Sloane
Villanova University
ebsloane@gmail.com

Freimut Bodendorf
Freidrich Alexander University
freimut.bodendorf@fau.de

Demographic change is inevitable. The United Nations Department of Economic and Social Affairs estimates that by 2050, one in six people worldwide will be age 65 years or over. The working-age population is shrinking rapidly, with significant labor shortages predicted in healthcare and elderly care. For example, Finland may be in a population crisis that requires foreign workers. The country’s population is projected to outpace its working-age population in the decade of 2030, due to an aging population and a declining birth rate, as reported by a recruiting and human resources platform. While studies show that older adults are becoming more technology-savvy, the rapid changes in AI across all facets of life sometimes make older adults feel like they are coming out of retirement to learn a new job.

The objective of the workshop is to bring critical thinking to bear on advancing artificial intelligence (AI), drawing on a pool of scholars who have experienced its effects on aging in the present and will experience them in the future. We aim to present and discuss fundamental efforts at the intersection of aging and technology.

The workshop leaders view the participants’ learning goals as encapsulated in a great appreciation of the expanding aging population as well as technological advancements in supporting older adults in areas such as healthcare administration, healthcare procedures, medical devices, anonymous transportation, companionship and cognition (i.e., reading, games, current events), helping hands (i.e., sensors, robots), and AI curriculum adapted for older adults

SWT Leaders:

Cindy Riemenschneider (Primary Contact)
Baylor University
c_riemenschneider@baylor.edu

Michael Milovich
Rowan University
milovich@rowan.edu

Mobile health (mHealth) technologies represent one of the fastest-growing areas of digital health innovation, supporting scalable interventions, clinical decision support systems, and behavior change tools. Despite widespread adoption, relatively little attention has been devoted to the processes through which these systems are designed, built, and implemented through interdisciplinary collaboration between clinicians, behavioral scientists, and software engineers. This symposium addresses this gap by examining the socio-technical, organizational, and design challenges involved in developing mHealth applications through academic–industry partnerships, with a focus on development processes rather than clinical outcomes.

This hybrid symposium-panel brings together clinical health researchers from Harvard Medical School/Mass General Brigham, an academic medical center, who have collaborated with Zco Corporation, a software development company, to design and build mobile health applications addressing diverse clinical needs. Presenters will discuss how their applications were conceptualized, designed, and implemented, including stakeholder engagement, workflow integration, technical constraints, and lessons learned from interdisciplinary collaboration. The session emphasizes digital health development as a socio-technical process involving coordination across clinical, organizational, and engineering domains. Presentations will feature digital health applications developed across multiple clinical departments within a large academic health system, illustrating diverse use cases and cross-departmental innovation in mobile health:

• Psychiatry: a patient-facing HIV prevention and substance use intervention designed to deliver behavioral health support in emergency care settings.
• Emergency Medicine: a patient-facing music-based intervention for chronic pain management
• Anesthesiology: a provider-facing clinical decision support application designed to assist clinicians in determining safe medication concentrations and programming intravenous infusion pumps
• Oncology: a patient-facing virtual reality application to improve the experience of adults with sickle cell disease
• Technology Partner Perspective: Zco Corporation will describe the engineering, design, and collaboration processes required to translate clinical requirements into functional digital health systems

Together, these examples illustrate how digital health innovation emerges across diverse clinical contexts, workflows, and stakeholder needs within healthcare systems, highlighting the role of cross-departmental collaboration in shaping design and implementation decisions. The symposium contributes to system sciences research on human-centered technology design, interdisciplinary innovation, and implementation of digital health systems. It will explore key questions including:

• Development and Design Process
  1. How do clinical researchers and engineers translate clinical needs into technical specifications for mobile health applications?
  2. What processes guide the design, development, and iteration of digital health tools across clinical contexts?
  3. How do development decisions evolve through collaboration between healthcare providers and software developers?
• Interdisciplinary Collaboration
  1. What challenges arise in collaboration between clinicians, behavioral scientists, and engineers during app development?
  2. What communication strategies and workflows support effective academic–industry partnerships?
• Implementation and Real-World Integration
  1. What barriers arise when moving from prototype to deployable digital health tools?
  2. What technical, regulatory, or operational constraints shape implementation decisions?
• Adoption and User Experience
  1. How are usability and user needs incorporated throughout the development lifecycle?

A central contribution of this symposium is its dual perspective: academic clinical researchers will discuss the design and development processes underlying their mobile health applications, while industry developers will describe technical translation, engineering constraints, and cross-sector collaboration. This combined perspective provides insight into how clinical requirements are transformed into deployable digital health systems.

The session is particularly relevant to researchers and practitioners working in information technology in healthcare, design science, implementation research, digital innovation, and human-computer interaction. By focusing on development processes rather than outcomes, the symposium advances understanding of how complex health technologies emerge through interdisciplinary collaboration and socio-technical integration. The session aims to foster dialogue between health researchers and technology developers and identify best practices for building scalable, human-centered digital health solutions.

SWT Leader:

Tiffany Glynn
Harvard Medical School/Mass General Brigham
trglynn@mgh.harvard.edu

In this symposium, we explore the current state of the art and emerging future pathways of digital health ecosystems through the lens of strategic and collaborative foresight. We approach future digital health ecosystems as cyber-physical socio-technical systems that merge cyberspace – accelerating and connecting real-time data streams such as patient-centric health data, health data governance models and emerging health information technologies and their applications leveraging, for example, artificial intelligence, virtual reality and human digital twins – and physical space including people, smart healthcare facilities, medical devices, robotics and patient centric life science manufacturing (Pharma 5.0). Together, these elements datafy and interconnect the full value chain between individuals, providers, manufacturers, service organizations and regulators, shaping new possibilities for addressing current and future health needs and challenges.

As data driven technologies continue to reshape healthcare services, medical diagnostics, and pharmaceutical R&D and the operational environment is characterized by uncertainty, organizations face growing pressures to enhance agility, flexibility, and resilience while maintaining patient safety, data security and regulatory compliance.

Exploring different pathways for future developments, identifying opportunities and risks, navigating uncertainties, realizing possibilities and developing future capabilities requires collaboration and foresight across diverse actors within and around digital health ecosystems. The symposium therefore focuses on how strategic and collaborative foresight and socio-technical perspectives can help stakeholders collectively explore emerging and long-term developments, understand systemic interdependencies and consider how digital health ecosystems may evolve.

The symposium will highlight themes such as:

• Current developments and conceptualizations of cyber-physical socio-technical digital health ecosystems
• Emerging data-driven and intelligent technologies influencing future digital health and healthcare landscapes
• Governance, data and organizational considerations shaping ecosystem trajectories
• Collaborative foresight approaches for exploring alternative futures and guiding ecosystem-level thinking
• Systemic perspectives that connect technological possibilities with human and societal needs and constraints

Participants will gain:

• Insights into the evolving landscape of digital health ecosystems and the forces shaping their development
• Exposure to collaborative foresight perspectives that support exploration of future pathways under uncertainty
• A deeper understanding of how ecosystem-level thinking can inform long-term strategic considerations in health and wellbeing contexts

SWT Leaders:

Tero Villman (Primary Contact)
Finland Futures Research Centre, University of Turku
tero.villman@utu.fi

Toni Ahlqvist
Finland Futures Research Centre, University of Turku
toni.ahlqvist@utu.fi

Tolga Karayel
Finland Futures Research Centre, University of Turku
tolkar@utu.fi

A myriad of interconnected systems touching mental health are failing people and society, linked with terrible outcomes ranging from increasing rates of depression and social isolation leading to tragedies at personal, familial, community, and societal levels. The overarching goal of this interactive workshop will be to consider barriers to transformation engender by these interconnected systems, coupled with identifying potential solutions underpinned by the disruptive power of (learning) system sciences. The envisioned interactive workshop will proceed in four phases:

Phase 1.) The interactive workshop will commence with the SWT Leader presenting a brief overview of LHSs and highlighting past work at HICSS conferences vis-à-vis synergistically fusing LHSs and system sciences to drive system change, especially in complex adaptive systems. He will also give a preview of the problems he aspires for participants to work to address collaboratively.

Phase 2.) The SWT Leader (and potentially additional presenters) will present brief case studies on topics including:

• The overall mental health crisis being faced in the United States and around the world
• The opioid epidemic and its implications for mental health
• Social media and mental health
• Failures of social services and public health systems in the context of mental health
• Violence, civil unrest, and mental health
• The medicalization of mental health and corresponding ensuing challenges
• Personal examples of problems with mental health systems

Phase 3.) An open discussion among participants will be facilitated by the SWT Leader (and possibly by additional presenters and/or volunteers from amongst the workshop participants). This open discussion will engender an unstructured opportunity for participants to identify connections between these systems, as well as to openly share relevant professional and personal experiences.

Phase 4.) In a structured way, participants will identify, from a system sciences perspective (and utilizing frameworks emanating from learning cycles and Core Values of LHSs), both barriers to achieving positive disruptive change and potential solutions enabling surmounting such barriers.

SWT Leader:

Joshua Rubin
University of Michigan Medical School
josh@joshcrubin.com

Predictive healthcare aims to transition clinical practice from reactive intervention to proactive, data-driven, and preventive decision-making. Recent advances in artificial intelligence (AI) and network science, along with the increasing availability of biomedical data, have created a methodological foundation for building intelligent predictive systems. Continuous mobility data collected from wearable devices offers high-resolution, longitudinal signals that can be leveraged for early detection and risk stratification of various medical conditions.

In this tutorial, we explore how mobility data—such as walking patterns, balance, and daily activity levels, movement variability—can be analyzed using modern AI tools and network models to detect early warning signs or biomarkers of medical conditions. By examining movement patterns over time, we can identify subtle changes that may indicate emerging health issues long before traditional symptoms are visible. We introduce practical approaches that combine machine learning tools with network science algorithms to uncover meaningful health-related signals from multimodal datasets. These methods allow researchers and clinicians to move beyond simple activity tracking and toward predictive systems that support better decision-making.

Two important application areas will be highlighted: healthy aging and developmental disorders. As populations age worldwide, early detection of mobility decline is essential for maintaining independence, high quality of life. Similarly, identifying atypical movement patterns in children can support earlier screening and intervention for developmental and neurobehavioral conditions. Participants will gain a practical understanding of how to construct mobility networks, extract potential digital biomarkers, evaluate predictive models, and consider ethical and privacy implications in wearable-driven healthcare. This approach has the potential to transform biomedical research and significantly improve healthcare outcomes for diverse populations.

SWT Leader:

Hesham Ali
University of Nebraska Omaha
hali@unomaha.edu

Wearables are now used in healthcare and as health support tools for remote monitoring, recovery tracking, chronic disease management, clinical research and more. Programs often show promise in pilots, then stall when integrated into routine care. Common failure points include unstable field data, inconsistent adherence, workflow friction, unclear escalation and ownership (who acts on what data, when), interoperability barriers, and late-stage governance concerns (privacy, consent, equity, liability). This workshop is built around real deployments across hospitals, home monitoring programs, and clinical research trials. Through case discussions and structured breakouts, participants will surface repeatable failure modes, share operational fixes, and define a research and deployment checklists to support better implementation and adoptions in the real world.

In healthcare, wearables succeed or fail as part of a sociotechnical system. Device performance matters, but outcomes depend on how data are captured, interpreted, and acted on inside real clinical operations and patient or technology user’s lives. Field deployments routinely confront data missingness, artifacts, drift, alert fatigue, and inequitable access, alongside practical questions of staffing, triage, and integration with enterprise systems. This workshop focuses on what it takes to make wearable-enabled care clinically actionable, operationally feasible, and trustworthy at scale. It intends to:

• Define themes that occur commonly in real-world deployment of wearables in clinical and research settings.
• Identify common in-the-wild failure modes (patient dropouts, technology failures or errors, real-world experiences of testing assumptions vs. actual, software challenges, connectivity barriers) and practical mitigation strategies.
• Compare implementation models and frameworks to help support wearables in the “wild” deployment.
• Co-produce a short checklist, field evaluation template, and a focused research agenda aimed at scaling safe, equitable, sustainable wearables in the wild use and trust about data obtained.

SWT Leaders:

Carmen Quatman (Primary Contact)
Ohio State University
carmen.quatman@osumc.edu

Rachel Davis-Martin
University of Massuchusettes
rachel.davis-martin@umassmed.edu

Charlotte Goldfine
Yale University
charlotte.goldfine@yale.edu

Contemporary supply chains are increasingly characterized by systemic complexity, persistent uncertainty, and competing sustainability objectives. While organizations face growing pressures to improve environmental and social performance, decision-making often remains short-term and siloed, overlooking system-level dynamics such as resilience, ecosystem health, and value distribution. Sustainability in supply chains is therefore not an optimization problem with a single solution, but a continuous process of negotiating trade-offs across cost, efficiency, resilience, environmental impact, and social outcomes.

This symposium positions digital twins as system-level socio-technical capabilities that enable organizations to make sustainability trade-offs visible, governable, and actionable. Rather than treating digital twins as purely technical artefacts, the symposium frames them as boundary-spanning systems that integrate data infrastructures, organizational routines, decision logics, and institutional constraints across supply chain ecosystems.

Drawing on ten years of longitudinal research and the UKRI-funded £1.2 million BeefTwin project, ranked in the top 10% of 900 interdisciplinary submissions, the symposium demonstrates how interdisciplinary research approaches are essential for operationalising digital twins to address sustainability trade-offs, with particular empirical grounding in agri-food supply chains. Rather than offering prescriptive best practices, the symposium will equip participants with system-level thinking, interdisciplinary research logic, and conceptual tools that are transferable across sectors beyond agri-food supply chains.

The symposium covers four interrelated themes:

• Sustainability Trade-offs as System-Level Phenomena: Examining why trade-offs between efficiency, resilience, environmental impact, and social outcomes persist in supply chains, and why sustainability must be understood as an ongoing balancing process rather than a “win–win” outcome.
• Digital Twins as Socio-Technical Systems: Conceptualizing digital twins as system representations that connect physical processes, digital infrastructures, and organizational decision-making, enabling feedback visibility, scenario simulation, and coordinated action.
• Interdisciplinary Research as a System Design: Requirement Unpacking how interdisciplinary research is conducted in practice, including research design choices, collaboration mechanisms, and epistemic tensions across operations management, information systems, environmental science, AI, and organizational theory.
• Longitudinal Evidence from the BeefTwin Project: Demonstrating how interdisciplinary digital twin development unfolded over time and how sustainability trade-offs were quantified, visualized, and mitigated in a complex agri-food supply chain.

Upon completion of the symposium, participants will gain:

• Conceptual understanding of sustainability trade-offs as emergent properties of complex sociotechnical supply chain systems rather than isolated managerial decisions
• Theoretical insight into digital twins as system representations that integrate data, decisions, and governance mechanisms across supply chain ecosystems
• Methodological understanding of how interdisciplinary research is designed, coordinated, and sustained in complex system contexts.
• Empirical insight from a rare longitudinal case illustrating how digital twins evolve, stabilize, and generate system-level outcomes over time
• Research agenda inspiration for System Sciences scholars interested in digital infrastructures, sustainability transitions, and socio-technical system governance

SWT Leaders:

Fatima Gillani (Primary Contact)
Aston University
f.gillani@aston.ac.uk

Xiao Ma
Nottingham Trebt University
xiao.ma@ntu.ac.uk

Jennifer Chandler
California State University, Fullerton
jechandler@Fullerton.edu

This symposium builds on the themes developed in A Modern Guide to Industrial Economics (Book forthcoming in MAy 2026) and addresses the transformation of industrial economics in response to contemporary systemic shocks, including the pandemic, geopolitical tensions, climate change and technological disruption. The session will explore:

• The limits of traditional industrial economics frameworks focused on market structure and firm behaviour
• The renewed centrality of industrial policy in managing structural change and technological transitions
• The evolution from laissez-faire traditions to mission-oriented and geopolitically informed industrial strategies
• The convergence between industrial policy and “New Economic Statecraft”
• The transition from Industry 4.0 to Industry 5.0 and its implications for sustainability and resilience
• The ecosystem perspective as an analytical and policy framework for governing complex industrial systems

Particular attention will be devoted to understanding how industrial economics must evolve to interpret tightly interconnected socio-economic systems exposed to systemic risks and geopolitical fragmentation.

Upon completion, participants will:

• Understand how industrial economics is being reshaped by global instability and structural transformation
• Identify the role of industrial policy in addressing market failures, system failures and capability gaps
• Gain insights into the geopolitical dimensions of industrial strategy, including securonomics and technological sovereignty
• Learn how Industry 5.0 reframes industrial transformation around sustainability, resilience and human-centred development
• Develop an integrated view of industrial ecosystems as policy-relevant governance structures
• Acquire analytical tools to interpret industrial change across local, regional and global scales

SWT Leader:

Francesca Spigarelli
University of Macerata
francesca.spigarelli@unimc.it

This workshop is designed to explore the technical and systemic Habitat Engineering required to maintain sustainable human-AI biomes. It intends to address the urgent risks of Model Autophagy (where models degrade by iteratively consuming their own synthetic output) and Cognitive Atrophy (the erosion of human decisionmaking capacity due to excessive AI outsourcing). By integrating advanced research in AI, this session seeks to move the field from theoretical ethology to applied stewardship.

In this workshop, participants will engage with emerging frameworks for governing agentic autonomy, including:

• The Uncontrollability Index: A mathematical approach to formalizing the threshold where an agent’s behavioral entropy exceeds a human steward’s oversight capacity.
• Relational Governance and 2HP: Strategies to mitigate “Second-Hand Privacy” harms, where one user’s permissions inadvertently leak sensitive data about non-consenting third parties.
• Symbiotic Scaffolding: Designing “Trust-but-Verify” interfaces that prioritize evidence-linked transparency and “Designed Friction” to keep humans in the critical thinking loop.
• Regime-Aligned Agentic Decisions: Methods for ensuring agents align with quantitative “truth reserves” and technical market states rather than hallucinated linguistic narratives.
• Stewardship Continuity: Technical protocols and metadata schemas for the Stewardship Context Dossier, enabling the safe handoff of AI alignment history between human operators.

Upon completion of this workshop, participants will be able to:

• Quantify Governance Thresholds: Calculate behavioral entropy using the Uncontrollability Index to identify when autonomous systems require manual intervention
• Audit Relational Privacy: Implement the 2HP metric to measure and mitigate privacy externalities in multi-channel personal-data ecosystems
• Architect Trust Infrastructures: Design interface affordances based on clinician and expert “information foraging” loops to support calibrated reliance over passive acceptance
• Enforce Safety Benchmarks: Evaluate agentic systems against the Canine Protocol, specifically testing for “Safe Disobedience” and functional independence from AI agents
• Develop Traceability Standards: Contribute to the creation of the Technical Standard for Agentic Traceability (TSAT-2027), a roadmap for long-term sustainable AI stewardship

SWT Leaders:

Keeheon Lee (Primary Contact)
Yonsei University
keeheon@yonsei.ac.kr

Younah Kang
Yonsei University
yakang@yonsei.ac.kr

Jean Young Song
Yonsei University
jeansong@yonsei.ac.kr

This workshop explores two AI-powered innovations to enhance student learning in undergraduate and graduate Information Systems coursework: (1) automated tracking of students who miss formative writing assignments, with AI-generated suggestions for timely interventions, and (2) simulation-based exercises using generative AI to deepen critical thinking and reinforce course concepts. By independently designing and implementing these tools, faculty can reduce manual workloads, provide proactive support for struggling students, and foster authentic engagement with AI.

By the end of the workshop, participants will be able to:

• Identify how AI tools can detect early signs of disengagement and support timely outreach for students
• Design human‑reviewed, AI‑supported interventions that encourage student engagement
• Create generative‑AI‑based simulations that prompt students to critique, analyze, and revise AI‑generated content
• Apply structured prompts and reflection activities that strengthen students’ critical thinking about AI’s role in learning

SWT Leader:

Carol McGuire
Miami University
carol.mcguire@miamioh.edu

This symposium provides a platform for two panel discussions. The first discussion will involve researchers to offer their perspectives, considering recent advances, on exciting ways that augmented intelligence using Gen AI, Agentic AI, and Augmented reality can improve the future of work, with benefits for workers, employees, and/or society. The second discussion focuses on the risks associated with these disruptive technologies and the identification of paths toward understanding and eventually minimizing potential harm while preserving key benefits.

At the symposium, scholars from academia and industry will share their thoughts on question including, but not limited to:

• In what ways can organizations foster effective collaboration between humans, generative AI, and agentic AI systems, and how might this collaboration reshape traditional workflows?
• What principles should guide the development of human-AI interaction models to enhance productivity and innovation?
• How will generative AI, agentic AI, and AR influence the nature of jobs in the future, and what new types of roles or industries might emerge as a result?
• What challenges and opportunities do the evolving job landscape pose for both employers and employees?
• How do generative AI, agentic AI, and AR contribute to or challenge human creativity, and what opportunities does it present for innovation in various industries? Can generative AI be a catalyst for more creative and dynamic work environments?
• How will generative AI and agentic AI influence global workforce dynamics, including outsourcing trends, remote work, and cross-border collaborations? What challenges and opportunities arise in managing a globally distributed workforce in the context of generative AI?
• Are there regulatory frameworks or policies that should be established to govern the ethical and responsible deployment of generative AI in the workplace?
• What are key ethical risks and legal issues that threaten to reduce the benefits of these emerging technologies of human augmentation for workers, organizations, and society?
• How should ownership and intellectual property rights be defined and protected in the context of generative AI systems trained on proprietary datasets?
• What mechanisms can ensure fair compensation for owners of data used to train AI models, especially when the resulting models generate significant value?
• What considerations should organizations consider when negotiating data licensing and usage agreements with data providers, particularly in scenarios where generative AI is involved?

SWT Leaders:

Souren Paul (Primary Contact)
Drexel University
souren.paul@gmail.com

Tung Bui
University of Hawaii at Mānoa
tungb@hawaii.edu

Alex Kass
Accenture
alex.kass@accenture.com

This workshop aims to:

• Foster collaboration to drive impactful AI integration across academic disciplines and industry sectors
• Explore strategies to enhance the transition of AI research and development into practical industry and educational applications
• Explore training data best practices and challenges to help non-AI experts effectively train and deploy AI models
• Leverage faculty experiences across disciplines to create viable mechanisms for cross-disciplinary AI integration
• Generate actionable insights and launch the

Topics that this workshop will address include:

• Industry-Academia Collaboration
  1. Effective partnerships for AI development and workforce readiness
  2. Open-source contributions and shared innovation strategies
  3. Strategies to identify and address industry needs
• AI Research to Real-World Deployment
  1. Challenges in moving AI from theory to practice
  2. Case studies of successful AI integration in industry
• AI in Education: Preparing the Future Workforce
  1. Embedding AI into educational programs and curricula in disciplines other than AI/CS
  2. Aligning educational outcomes with industry needs

This workshop is expected to produce the following outcomes

• A collaborative whitepaper or report summarizing insights and recommendations from the workshop.
• New partnerships initiated during the event among attendees, including industry-academia partnerships.
• Recommendations for AI adoption strategies applicable across different sectors.
• Opportunities for future research and collaboration to enhance AI integration, including collaborative publications and projects.

SWT Leaders:

Costis Toregas (Primary Contact)
George Washington University
toregas1@gwu.edu

Debasis Bhattacharya
University of Hawaii Maui College
debasisb@hawaii.edu

Robots are no longer confined to factory floors. They deliver food, assist surgeons, monitor warehouses, tutor children, and increasingly participate as de facto team members in knowledge work. At the same time, generative AI is giving robots conversational fluency, emotional expressiveness, and autonomous decision-making capabilities that were speculative just two years ago. This convergence raises questions that the information systems and system sciences communities cannot afford to treat as settled — because they are not.

This workshop takes a deliberately provocative approach to human-robot collaboration (HRC). Rather than cataloging incremental advances, we surface the contested, uncomfortable, and genuinely unresolved questions that will define how organizations and societies integrate robots into human life. Should robots be designed to deceive? Is anthropomorphism a design virtue or a manipulation tactic? When a cobot injures a worker, who is accountable — the robot, the designer, the manager, or the algorithm? Are we building trust in machines that do not deserve it?

The session will be structured around structured debates, position provocations, and adversarial collaborations — formats designed to generate friction, challenge assumptions, and produce insights that conventional paper sessions cannot. The goal is not consensus but clarity: mapping where the field agrees, where it is genuinely divided, and where new research is most urgently needed.

The workshop is organized around five provocations: deliberately sharp framings of contested issues in HRC. These provocations are grounded in rapidly evolving trends across IS, HRI, organizational behavior, and AI ethics. They are chosen because the field has not reached agreement — and because the answers will determine whether collaborative robots enhance or erode human agency in organizations.

### Provocation 1: “Robots should be allowed to lie.” Deception, white lies, and emotional display in HRC. When (if ever) should robots misrepresent their internal states, capabilities, or intentions? What are the organizational and ethical consequences of designing robots that manage impressions?

### Provocation 2: “Anthropomorphism is manipulation, not design.” The arms race to make robots more human-like — faces, voices, gendered bodies, “personalities” — and whether this constitutes a form of dark pattern that exploits cognitive biases.

### Provocation 3: “Your next coworker will be a robot — and you won’t get a say.” Organizational deployment of collaborative robots and the power dynamics it creates. Who decides when a robot joins a team? What happens to workers’ autonomy, skill, and identity?

### Provocation 4: “When the robot fails, nobody is responsible.” Accountability gaps in human-robot teaming. Current governance and liability frameworks were not designed for autonomous agents that learn, adapt, and make consequential decisions.

### Provocation 5: “Generative AI gave robots a voice — and we are not ready.” The rapid integration of large language models into embodied robotic systems, and the radically new interaction dynamics this creates. LLM-powered robots are persuasive, fluent, and unpredictable — a combination that existing HRI theory barely addresses.

Upon completion of this workshop, participants will:

• Confront and articulate their own positions on five genuinely contested questions in HRC
• Understand how emerging trends — especially LLM-powered robots, agentic AI, and organizational automation — change the assumptions underlying existing HRI theory
• Identify where current IS and HRI research is insufficient to guide deployment decisions
• Gain exposure to adversarial and debate-based research methods that surface hidden assumptions
• Leave with a prioritized set of “most-needed” research questions and a network of collaborators willing to pursue them across disciplines

SWT Leaders:

Connor Esterwood (Primary Contact)
Wayne State University
cte@wayne.edu

Lionel Robert
University of Michigan
lprobert@umich.edu

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

The objective of this workshop is to provide faculty participants with an interactive opportunity to learn and discuss a broad range of technology delivery strategies, data resources, and ERP based curricula for Information Technology and Business programs. The workshop will present technology and curricula resources (data, systems, and projects) from the University of Arkansas Information Systems Department and Enterprise Systems, housed in the Information System Department, Walton College of Business. Technologies utilized include: 1) SAP S/4HANA, 2) SAP Discovery Center, 3) Acumatica, 4) PowerBI, 5) Tableau, 6) ERP Simulation and many other technologies.

In addition, a panel of both faculty participants and industry specialists will be available to address the support provided by Enterprise Systems in their usage of these technologies as well as exploring current trends related to ERP technology. The workshop will include hands-on access to the Enterprise System platform. Workshop highlights include:

• Enterprise Systems Introduction
  1. Technologies available: SAP S/4HANA, Acumatica, PowerBI, and Tableau
  2. Exercises & Use Cases: SAP, Acumatica, Artificial Intelligence (Joule), and Data Analytics and Visualization Dashboards
• Curricula Development – ERP
  1. Finding academic partners through University Alliance (UA)
  2. Finding industry partners through Americas SAP User Group (ASUG)
  3. Collaboration opportunities between businesses and university
• ERP Technology Infusion into the Classroom – Panel
  1. Nick Knight – 3Value
  2. Ashwin Mundkur – SAP
  3. Nicolette Marasa – ASUG
  4. Susan Bristow – University of Arkansas
  5. EmmaLe Davis – University of Arkansas
  6. Ron Freeze – University of Arkansas
  7. Paul Cronan, Moderator
• “Hands-on” Applications for Curricula
  1. ERP Simulation
  2. Acumatica Walkthrough
• Discussion — Resources, Challenges, Curricular Issues, and Research Implications
  1. Resources for Curriculum
  2. Information Systems Department Masters Programs, Enterprise Systems, University of Arkansas

Participants will leave with practical resources, sample use cases, and insights for integrating ERP technology into their own programs.

NOTE: Preconference material will be made available for attendees on coordinators’ or other websites.

SWT Leaders:

Susan Bristow (Primary Contact)
University of Arkansas
sbristow@walton.uark.edu

Ron Freeze
University of Arkansas
rfreeze@walton.uark.edu

EmmaLe Davis
University of Arkansas
edavis@walton.uark.edu

The objective of this workshop is to provide faculty participants with an interactive forum to discuss a broad range of technology delivery strategies, data resources and curricula for Business Analytics programs. The workshop will present technology and curricula resources (data, systems, and projects) from the University of Arkansas Information Systems Department and Enterprise Systems, housed in the Information System Department, Walton College of Business. Technologies utilized include: 1) Teradata, 2) Microsoft SQL Server, 3) SAP S/4HANA, 4) SAS VIYA, 5) PowerBI, 6) Tableau, 7) Python and many other technologies. Examples of data will also be presented.

In addition, a panel of both faculty participants and industry specialists will be available to address the support provided by Enterprise Systems in their usage of these technologies. The workshop will demonstrate hands-on access to the Enterprise System platform. Workshop highlights include:

• Enterprise Systems Introduction
  1. Technologies available: Teradata, SAP S/4HANA, IBM Z15
  2. Exercises & Use Cases: SAS VIYA, SAS EG, SAS EM, SAP, Teradata
  3. Datasets: Dillard’s, Sam’s Club, Acxiom, BlueCross BlueShield
  4. Platforms: Teradata Studio, HANA Studio, SQL Server Studio
• Curricula Development – Business Analytics
  1. Finding academic partners
  2. Finding industry partners
  3. Collaboration between businesses and university
• Technology Infusion into the Classroom – Panel
  1. Pamela Schmidt – Washburn University
  2. Yenny Yang – Teradata University
  3. EmmaLe Davis – University of Arkansas
  4. Kenneth Grifno – UT Southwestern
  5. Ron Freeze, Moderator
• “Hands-on” Applications for Curricula
  1. Connecting to a large dataset
  2. Connecting to Teradata and large Data Sets
• Discussion — Resources, Challenges, Curricular Issues, and Research Implications
  1. Resources for Curriculum
  2. Information Systems Department Masters Programs, Enterprise Systems, University of Arkansas

 Participants will leave with practical resources, sample use cases, and insights for integrating enterprise analytics into their own programs.

NOTE: Preconference material will be made available for attendees on coordinators’ or other websites.

SWT Leaders:

Susan Bristow (Primary Contact)
University of Arkansas
sbristow@walton.uark.edu

EmmaLe Davis
University of Arkansas
edavis@walton.uark.edu

Ron Freeze 
University of Arkansas
rfreeze@walton.uark.edu

Much IS research on AI has focused on knowledge work and the automation and augmentation of routine tasks. However, this workshop examines an emerging socio-technical shift: AI systems are moving from daily support tools to serving as (multi-agent) co-leaders in decision-making to potentially substituting for the often-romanticized notion of “true human leadership”. As organizations experiment with AI-driven and AI-only leadership (e.g., virtual CEOs like NetDragon), conscious choices must be made about the role of AI in leadership, and both organizations and scholars must rethink how we conceptualize leaders, the impact of positioning AI in such roles, and how it can best support leadership work through appropriate data, platforms, and accountability mechanisms.

This interactive workshop explores these choices from four angles:

• Accountable AI
• Strategic AI
• Responsible AI 
• Technological enablement

The workshop includes lightning talks from leading international scholars and short video reflections from practitioners, followed by a plenary discussion and breakout session featuring an AI-led boardroom simulation and a co-creation phase to develop a research agenda based on the four workshop angles, with respect to early evidence, research gaps, and methodological challenges. The workshop concludes with a plenary synthesis and the development of a joint research agenda co-created by featured speakers and participants.

Join this workshop to learn about the emerging role of AI-driven and AI-only leadership through insights from academia and industry, jointly develop theory- and practice-relevant research questions, exchange ideas on suitable methodological pathways, and meet new collaborators for knowledge exchange and future joint work.

Featured speakers and panelists for this workshop include:

• Prof. Dr. Anne Gferer (IU International University of Applied Sciences)
• Dr. David Holtz (Columbia University) – Dr. Fabrizio Dell’Acqua (Harvard University)
• Dr. Marjan Houshmand (University of Hawaii at Manoa)
• Dr. Paul Schmiedmayer (Stanford University)

SWT Leaders:

Bo Sophia Xiao (Primary Contact)
University of Hawaii at Manoa
boxiao@hawaii.edu

Isabell M. Welpe
Technical University of Munich
welpe@tum.de

Stan Karanasios
University of Queensland
s.karanasios@uq.edu.au

Lisa-Maria Schober
Technical University of Munich
lisa-maria.schober@tum.de

Maximilian Rink
Technical University of Munich
maximilian.rink@tum.de

Deep tech breakthroughs – e.g., mRNA vaccines or quantum computing – promise outsized impact, yet many ventures stall in the “valley of death” between research and scalable commercialization. This interactive workshop explores how incumbents can bridge that gap through corporate venturing (e.g., CVC, incubators, joint development) and other corporate–startup partnerships, increasingly embedded in foundation-model platforms and other digital infrastructures, while managing mismatched timelines, governance, risk, and capability-building. The session includes lightning talks from leading international scholars, followed by interactive break-outs to co-develop a research agenda on the strategic rationale for deep tech collaboration, key barriers, effective collaboration models, platform-mediated collaboration, and the organizational transformations needed to successfully scale deep-tech breakthroughs. Join to learn more about puzzles and trade-offs in deep tech, sharpen research questions, exchange methods, and meet (new) collaborators.

Featured speakers include:

• Bart Clarysse (ETH Zurich)
• Ramana Nanda (Imperial College)
• Jason D. Shaw (NTU)
• Paul Schmiedmayer (Stanford University)
• Martie-Louise Verreynne (University of Queensland)

SWT Leaders:

Isabell M. Welpe (Primary Contact)
Technical University of Munich
welpe@tum.de

Tung Bui
University of Hawaii at Manoa
tungb@hawaii.edu

Olav Sorenson
UCLA Anderson
olav.sorenson@anderson.ucla.edu

As AI technologies mature, the challenge is no longer access or deployment but realizing impact at scale. This interactive workshop explores the potential, challenges and real-life experiences of how AI-field experiments in organizations can create value and drive organizational performance. The ability of AI to increase productivity by double digits is well documented across industries and tasks by numerous correlational and experimental studies. Despite this potential several reports from companies have claimed that their AI pilots either a) failed to show real live impact b) failed to scale or c) showed productivity improvement in single tasks and processes but that these improvements did not show up in the financial numbers at the organizational level. Thus, pressing questions remain for scholars and organizational practitioners alike:

• Why do AI implementations fail to scale from pilots and isolated use cases into sustained, organization-wide value?
• How can firms implement AI in such a way that its productivity-rising potential materializes at the organizational level creating real economic value and impacting organizational performance in a measurable way?

We propose that field experiments in organizations are best suited to not only answer the open research questions but also allow for reliable, causal answers that can guide organizations in their implementation and scaling of AI. The workshop will focus on:

• Exploring how both scholars and practitioners can benefit from AI-field experiments in organizations, how they can and should be designed, what practical challenges arise and how these challenges can be overcome
• Identifying open questions and an agenda for research studies from a research and practice perspective that can be answered with AI-field experiments
• Connecting scholars and practitioners aiming to establish lasting collaborations

This session addresses the gap between AI adoption and organizational performance, with a focus on the following topics:

• Design and Implementation of field experiments in AI adoption: Designing and executing field experiments (or randomized/ quasi randomized treatments) for AI interventions to identify causal business value inside firms
• Measuring business value from AI at scale: How organizations define, collect and interpret key performance indicators (KPIs) for AI value: revenue growth, cost reduction, productivity, innovation metrics, customer/employee satisfaction
• Scaling roadmaps – from individual use cases to organizational change: How companies transition from discrete AI experiments to enterprise‑wide deployment: governance, data/infrastructure readiness, talent, workflow redesign, change management
• Organizational, cultural and governance enablers / barriers: Which organizational capabilities (people, processes, governance) enable or impede capturing value from AI
• Structuring industry-academic collaborations for credible evidence generation: Practical guidelines for facilitating field experiments while drawing lessons from BCG (Boston Consulting Group) + TUM AI Strategy Research Cluster’s research

SWT Leaders:

Nadja Born (Primary Contacy)
Technical University Munich
nadja.born@tum.de

Stan Karanasios
University of Queensland
s.karanasios@business.uq.edu.au

Michal Kosinski
Stanford University
michalk@stanford.edu

This workshop, in its second iteration at the HICSS conference, invites research scholars, practitioners, and policy scholars to engage in forward-looking dialogue on how information systems (IS) and digital technologies mobilize crowds and collective intelligence can influence entrepreneurship and innovation. As crowd-enabled processes rapidly evolve—driven by AI-enabled curation, shifting platform governance, and emerging forms of decentralized collaboration—there is a pressing need to rethink how these systems enable, mediate, and scale collective action.

The workshop is designed as a highly interactive forum where participants collaboratively develop an updated research agenda that reflects these emerging realities. With crowd-driven entrepreneurship now deeply embedded within digital ecosystems, understanding the role of information systems in structuring participation, coordinating contributions, and amplifying innovation outcomes has become increasingly critical.

While prior research has explored specific phenomena such as crowdsourcing, crowdfunding, and digital collaboration, the integrated study of crowd dynamics within information systems remains underdeveloped and ripe for theoretical and empirical advancement. As digital platforms grow more sophisticated—incorporating multimodal AI, blockchain-enabled governance, and advanced analytics— new opportunities arise to examine how these systems influence opportunity recognition, venture creation, innovation diffusion, and scaling at a global level.

This workshop aims to harness the collective expertise of participants to advance an interdisciplinary conversation at the intersection of IS, entrepreneurship, and innovation. Through facilitated discussions, small-group activities, and idea-generation sessions, the workshop seeks to catalyze high-quality, impactful research and foster enduring scholarly collaborations. Topics of Interest (including, but not limited to):

Next-generation crowdsourcing models and digital innovation acceleration

• Evolution of crowdfunding ecosystems and their integration into information systems
• Designing IS architectures for engaging, motivating, and governing digital crowds
• Leveraging AI, generative AI, and machine learning to enhance crowd contributions and decision quality
• Governance, regulatory developments, and ethical frameworks for crowd-enabled digital platforms
• Crowd-driven market discovery, opportunity evaluation, and startup scaling strategies
• Collective intelligence in R&D, product co-creation, and open innovation networks
• Information systems enabling decentralized and distributed innovation
• Diversity, inclusion, and equity in digital crowd participation and platform design
• Metrics, evaluation, and impact assessment of crowdsourced innovation outcomes
• Patterns of digital technology diffusion driven by crowd behaviors
• Business models to support innovation and entrepreneurship through incorporation of IS, digital technologies and crowd participation
• Role of social networks, digital communities, and online engagement in crowdsourcing and crowdfunding
• Economic, legal, and institutional perspectives on crowd participation in digital innovation
• Future trends, challenges, and opportunities in global crowdsourcing ecosystems

SWT Leaders:

Niharika Garud (Primary Contact)
University of Melbourne
niharika.garud@unimelb.edu.au

Rakesh Pati
Deakin University
rkpati7@gmail.com

Viraji Jayaweera
University of Melbourne
v.jayaweera@unimelb.edu.au

Effective management and communication of information is essential to drive innovation and deliver sustainability outcomes across the triple bottom line – economic, environmental, and social performance. Organizations need comprehensive, reliable and timely data to guide decision-making, measure progress, and adapt strategies across these interconnected dimensions. This necessity raises important questions about the scope and dimensions of the information being collected, processed and disseminated, and about the potential for misinformation and disinformation to distort decisions and erode trust. A common example is greenwashing, where an organisation overstates its environmental credentials through deceptive marketing or selective disclosure. Recent regulatory developments, including mandatory ESG, climate risk and GHG disclosures in many jurisdictions, are accelerating requirements for new information capture, verification and information systems, creating both opportunities and new compliance risks.

This symposium will feature presentations of recent and ongoing research on the effective use of information to support innovation and sustainability within organizations and broader communities, and a panel discussion exploring current and emerging challenges in this domain. It aims to:

• Facilitate discussion and sharing of research findings among scholars who are interested in exploring how information can be used effectively to support development of innovations, as well as to enhance economic, environmental and social sustainability at individual, organizational and community levels
• Set up a research agenda that will enable global research collaborations to advance theory and practice in managing information for innovation to enhance sustainability / Environment, Social Governance (ESG) goals

Topics to address include, but are not limited to:

• Information in measurement of leadership, ESG and misconduct in organisations (a new empirical study)
• Information in supporting innovation: tacit versus explicit knowledge management in SMEs orchestrating value chain activity
• Dynamic capabilities to capture and measure information that pertains to sustainability and uncertainty
• Technology advancement for enhancing Environment, Society, and Governance (ESG) performance: new ‘ESGT’ concepts
• The role of enterprise architecture in enhancing sustainability through digital transformation
• Best practices and challenges in managing and sharing emissions data across supply chains

SWT Leaders:

Daniel Samson (Primary Contact)
University of Melbourne
d.samson@unimelb.edu.au

Niharika Garud
University of Melbourne
niharika.garud@unimelb.edu.au

Sherah Kurnia
University of Melbourne
sherahk@unimelb.edu.au

This workshop focuses on design science research (DSR) – a research paradigm to generate design knowledge through the iterative process of developing and evaluating solutions to real-world problems. DSR has proven particularly successful to make research contributions of value by supporting decision makers in business and society in designing a prosperous, inclusive, ethical, and sustainable digital world. As the design of real-world solutions often faces challenges of complexity, a new DSR methodology has been developed: Echeloned DSR (eDSR). eDSR decomposes DSR into smaller self-contained work units – so called echelons – that support adaptation and collaboration as well as concurrent publication of results in DSR. This workshop is designed to introduce participants to DSR in general but specifically to eDSR methodology to understand issues related to research design and its application for a complex DSR project.

Workshop Schedule

Session 1 Introduction to the Workshop – 9:00 to 10:15 am

• Introduction to the workshop, and a step-by-step tutorial of a completed eDSR project with sixteen design echelons
• Tuunanen, T., Winter, R., & vom Brocke, J. (2024). Dealing with Complexity in Design Science Research – A Methodology Using Design Echelons. MIS Quarterly.
• Ghanbari, H., Tuunanen, T., and Kazan, E. (2025). Towards Competition and Collaboration: Design Principles for a Coopetitive News Platform.

Coffee Break 10:15-10:30 am

Session 2 eDSR project development workshop – 10:30 to 12:00 noon

• Participants will be mentored to develop their own eDSR projects in small groups based on the workshop materials.
• Participants present their eDSR project plan slide deck and the design echelons and reflect on the design knowledge development.
• Closing words

Recommended Readings

DSR Foundations:

• vom Brocke, J., Hevner, A., Maedche, A. (2020), Introduction to Design Science Research, in: J. vom Brocke, A. Hevner & A. Maedche (Eds.), Design Science Research Cases (pp. 1-13): Springer
• Gregor, S., & Hevner, A. R. (2013). Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly, 37(2), 337-355.

A Method for DSR:

• Tuunanen, T., Winter, R., & vom Brocke, J. (2024). Dealing with Complexity in Design Science Research – A Methodology Using Design Echelons. MIS Quarterly.

Evaluating the Artifact:

• Venable, J., & Pries-Heje, J. (2016). FEDS: a Framework for Evaluation in Design Science Research. European Journal of Information Systems, 25(1), 77-89.

Theorizing Design Knowledge:

• Gregor, S., Kruse, L. C., & Seidel, S. (2020). Research Perspectives: The Anatomy of a Design Principle. Journal of the Association for Information Systems, 21(6), 1622-1652.
• Mandviwalla, M. (2015). Generating and justifying design theory. Journal of the Association for Information Systems, 16(5), 3.

SWT Leaders:

Tuure Tuunanen (Primary Contact)
University of Jyväskylä
tuure@tuunanen.fi

Robert Winter
St.Gallen University
robert.winter@unisg.ch

Matthias Söllner
University of Kassel
soellner@uni-kassel.de

This workshop provides participants with an overview of current applications of eye tracking data for measuring engagement with visual information. The workshop will be conducted via brief, targeted lectures followed by hands-on exercises and activities.

The targeted outcomes of this workshop include: (1) understanding the types of qualitative (e.g., heat maps) and quantitative (e.g., fixation, saccades) data essential for studying visual information processing and decision behavior, (2) gaining insights into crafting a comprehensive mixed-method eye-tracking study, combining both qualitative and quantitative approaches, (3) learning how to effectively use qualitative and quantitative eye-tracking metrics to explain engagement, effort, and decision behavior.

SWT Leaders:

Jim Ryan (Primary Contact)
Worcester Polytechnic Institute
jryan@wpi.edu

Soussan Djamasbi
Worcester Polytechnic Institute
djamasbi@wpi.edu

Bengisu Tulu
Worcester Polytechnic Institute
bengisu@wpi.edu

This workshop explores future-oriented research and design approaches in Information Systems (IS). As digital infrastructures increasingly shape long-term societal trajectories, IS scholars are encouraged not only to analyze present systems but also to engage with possible, plural, and emerging futures.

The workshop introduces participants to methodological approaches for researching and designing futures in IS, including speculative design, world-building, participatory futuring, and AI-supported scenario development. Particular attention is given to how such approaches generate knowledge, structure inquiry, and inform research design.

Through an interactive World Café format, participants rotate across 4-5 different stations, engaging hands-on with tools such as:

• AI-supported scenario and world-building
• Structured future decision-making exercises
• Critical design fictions
• Participatory and collective imaginaries

The workshop combines conceptual grounding with practical experimentation. The workshop concludes with a plenary discussion synthesizing insights and identifying future research directions.

Upon completion, participants will:

• Understand different methodological approaches to future-oriented IS research
• Gain hands-on experience with futuring tools and techniques
• Be able to critically assess how futures are constructed and operationalized in IS research
• Contribute to shaping an emerging research agenda for futures-oriented inquiry in IS

SWT Leaders:

Dirk Hovorka (Primary Contact)
University of Sydney
dirk.hovorka@sydney.edu.au

Katja Thoring
Technical University of Munich
katja.thoring@tum.de

As AI methods, technology platforms and advanced software systems bypass ISR requirements on model, method and result validation before advice, recommendations and support are offered to users of information systems, a new ISR methodology is needed to make sure that artificial and computational intelligence methods are validated and operational for practical problem-solving and decision making. This new methodology will probably be a synthesis of successful elements of decision analysis, design science, decision support systems and decision analytics with additional elements (both cognitive and digital) from digital platforms .

The symposium aims to explore novel and speculative methodological forms, methods, and elements that can serve as foundational elements of new methodologies for IS research to address the growing cognitive abilities of AI technologies.

SWT Leaders:

Christer Carlsson (Primary Contact)
Institute for Advanced Management Systems Research, Abo Akademi University
christer.carlsson@abo.fi

Matti Rossi
Aalto University
matti.rossi@aalto.fi

This symposium focuses on the practice and applications of AI primarily in the context of Cybersecurity. There are multiple critical components in this area, including appropriate applications of machine learning and deep learning based on type of AI, Innovative applications to AI in cybersecurity, types of data, appropriate data, data acquisition, benchmarking, toolsets, and results analysis. Tools, methodologies, as well as challenges and means of addressing them will be discussed.

SWT Leaders:

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

Hsinchun Chen
University of Arizona
hsinchun@arizona.edu

Sagar Samtani
University of Indiana
ssamtani@iu.edu

Agentic AI refers to AI systems that can plan and execute multi-step actions across tools and communication channels with limited human supervision (e.g., drafting, routing, and sustaining multi-turn interactions). Agentic trust refers to the trust relationship that forms when humans increasingly rely on AI “proxies” (defensive copilots/guardians) to interpret intent, authenticate requests, and recommend actions—often under time pressure and asymmetric information. We use these terms descriptively (capabilities and workflow effects), not to imply human-like intent.

Generative AI (GenAI) and emerging Agentic AI are rapidly reshaping this terrain. Attackers are moving beyond simple phishing to “industrialized persuasion”—hyper-personalized, multilingual, and context-aware interactions sustained by semi-autonomous agentic workflows. Simultaneously, defenders are adopting AI “guardians” and copilots that fundamentally alter how employees verify information, escalate threats, and make decisions.

This symposium convenes scholars and practitioners to build a coherent social + organizational science agenda for cybersecurity under these new conditions. We focus explicitly on the socio-technical layer: human-AI teaming, organizational governance, and measurable interventions in high-velocity collaboration environments.

This symposium is intentionally centered on the “last mile” of security where technical controls often yield to human judgment. It complements (rather than duplicates) research focused on adversarial ML or malware detection by emphasizing:

• Human-AI Teaming: How employees interact with “defensive” AI agents
• Organizational Resilience: Workflow design, norms, and governance
• Persuasion Science: The cognitive mechanisms of AI-generated social engineering

The symposium will cover five tightly connected clusters of topics:

• GenAI-enabled persuasion and “Agentic” social attacks
  1. LM-driven Business Email Compromise (BEC) at scale: persuasion personalization and “synthetic familiarity” 
  2. Multi-modal deepfakes (voice/video) and real-time executive impersonation
  3. Agentic Threats: Autonomous AI agents that can maintain persistent, multi-turn manipulative conversations across platforms (e.g., email → chat → voice)
• Human-AI teaming for verification and defense
  1. AI Copilots as “Security Guardians”: Benefits and failure modes (hallucinations, false confidence, automation bias)
  2. Designing “Verification Rituals”: Workflows that combine human judgment with AI analysis without paralyzing productivity
  3. Trust Calibration: When to trust an AI recommendation vs. a human request in a zero-trust environment.
• Organizational systems that shape security behavior
  1. Culture, leadership signals, and psychological safety: Incentivizing “challenge behavior” in an era of deepfakes
  2. Governance patterns for workforce GenAI use: Balancing productivity with data leakage and shadow AI risks
  3. Socio-technical friction: How workflow design creates cognitive load that attackers exploit
• Measurement, evaluation, and cumulative science
  1. Outcome measures beyond click-rates: “Time-to-verify,” reporting latency, containment quality, and resilience to novel vectors
  2. Simulation methodologies: Using LLM-based agents to simulate social attacks safely in controlled environments
  3. Ethics/IRB governance for deception research and deepfake exposure in the workplace
• Emerging frontiers: Identity in the age of Synthetic Media
  1. Identity assurance in remote/hybrid work: The collapse of “trusted channels”
  2. Security communication: Managing internal misinformation and rumor propagation during cyber crises

Examples of research questions that this symposium will address include:

• How do multi-modal deepfakes alter the cognitive cues employees use to authenticate urgency and authority?
• Does the presence of a “Security Copilot” increase automation bias, leading employees to approve malicious requests if the AI is silent?
• Which organizational “verification rituals” (e.g., out-of-band callbacks) remain robust against realtime voice cloning?
• How can we ethically simulate “agentic” social engineering attacks to measure organizational resilience without harming participants?
• What telemetry best captures “near-miss” events where employees correctly identify but fail to report sophisticated social attacks?

SWT Leaders:

Juliana Schroeder (Primary Contact)
University of California, Berkeley
jschroeder@haas.berkeley.edu

Matthew Schroeder
Wells Fargo
m.james.schroeder@gmail.com

This symposium explores how Artificial Intelligence (AI) is transforming credibility, deception, and trust in modern socio-technical systems. Presentations will cover topics on AI-generated deception such as deepfakes and automated social engineering, AI-assisted interviewing and credibility assessment, human trust and reliance in human–AI teaming, multimodal behavioral analytics and sensor fusion, explainability and transparency in AI systems, organizational and workforce implications, ethical and governance challenges, and real-world applications in cybersecurity, fraud detection, and information integrity. By the end of the session, participants will gain a cross-disciplinary understanding of emerging AI-enabled deception threats, current tools and methods for assessing credibility, and the human, technical, and policy factors that shape trust in AI-supported decision making. Attendees will leave with a clearer understanding of research frontiers, practical applications, ethical considerations, and opportunities for collaboration across technical, behavioral, and organizational communities.

Topics of interest include, but are not limited to:

• AI-generated deception, deepfakes, and synthetic media
• Human trust and reliance in human–AI teaming and decision support
• AI-assisted credibility assessment, interviewing, and screening
• Detection and mitigation of AI-enabled social engineering and phishing
• Multimodal behavioral analytics and sensor fusion for deception detection
• Credibility and deception in AI-mediated communication (chatbots, avatars, virtual agents)
• Explainability, transparency, and trust calibration in AI systems
• Organizational and workforce implications of AI for security and risk assessment
• Ethical, legal, and policy issues in AI-enabled credibility assessment and surveillance
• Applications and case studies in cybersecurity, fraud, border security, and information integrity

SWT Leaders:

Norah Dunbar (Primary Contact)
University of California Santa Barbara
ndunbar@ucsb.edu

Jeff Hancock
Stanford University
hancockj@stanford.edu

David Markowitz
Michigan State University
dmm@msu.edu

As AI-generated content, algorithmic amplification, and platform-based information flows reshape the digital economy, questions of information integrity increasingly affect organizational performance, governance, decision-making, and societal trust. Addressing these challenges requires interdisciplinary collaboration and the development of rigorous analytical approaches capable of keeping pace with rapidly evolving technologies.

This tutorial will present a research-driven framework for examining synthetic media as a critical challenge within contemporary sociotechnical systems. It will also introduce analytical frameworks that support rigorous investigation of information manipulation across digital platforms.

Designed to foster scholarly exchange, support ideas in development, and help shape emerging research agendas, the tutorial brings together conceptual insight, methodological perspectives, and applied analysis to encourage participants to critically examine the mechanisms through which manipulated information propagates and to identify opportunities for future inquiry. More precisely, it intends to advance a sociotechnical perspective on mis/disinformation, emphasizing the interaction between technological infrastructures, human behavior, organizational processes, and institutional contexts.

This tutorial will provide a forum for scholars to explore unresolved questions surrounding platform governance, AImediated communication, risk, and resilience in digital environments. It will connect information systems research with adjacent domains, including communication, data science, public policy, and organizational studies, to promote cross-disciplinary collaboration.

SWT Leaders:

Regina M. Luttrell (Primary Contact)
Syracuse University
rmluttre@syr.edu

Jason Davis
Syracuse University
jdavis72@syr.edu

Carrie Welch
Syracuse University
ctwelch@syr.edu

Help us build the future of cyber defense training! Join the WARDEN workshop, hosted by the Naval Information Warfare Center Pacific, to contribute to a hands-on tool that visualizes the Cyber Kill Chain and empowers our IT fleet.

The “Autonomous Cyber Defense Challenge” is a NISE-funded workforce development effort offered by the Basic and Applied Research Department (BARD) to increase our understanding of the Cyber Kill Chain and how the use of deception can mitigate attacks. This introductory course focuses on autonomous agents, network analysis, and cyber tactics, techniques, and procedures (TTPs). NIWC Pacific Science and Technology (S&T) develops novel research to produce applications that better train organizations and personnel to defend themselves, and to augment our Naval warfighters’ IT capabilities

WARDEN is a game-like application designed to empower engineers and scientists with a comprehensive understanding of Cyber Kill Chain fundamentals. This program aims to enable participants to apply security measures, assess vulnerabilities, conduct data collection and analysis of various network locations, understand attack nomenclature, and comprehend how using deception can offset an attack. These objectives encompass a broad knowledge base, including familiarity with the taxonomy of cyberattack behaviors, reduction in time-from-machine, and the ability to adapt to the continuously evolving landscapes of autonomous technology.

The objective of this workshop is to develop a cross-competency workforce of highly skilled network examiners and analysts who are expertly skilled at identifying attacks, understanding the benefit of creating defensive machine learning agents, and producing defensive mitigations which can be applied to mission-critical systems.

Participants will develop a practical understanding of the Cyber Kill Chain by observing how adversarial actions and defensive responses unfold within a simulated network environment. Through guided interaction with WARDEN, learners will examine autonomous cyber operations, including the behavior of red and blue agents, adversarial tactics, techniques, and procedures (TTPs), and the role of distributed and adaptive defensive measures. Attendees will gain exposure to artificial intelligence and machine learning concepts as applied to autonomous agent algorithms, including how agent parameters influence decisionmaking and system outcomes.

The workshop will enable participants to interpret agent-driven activity, analyze attack progression, and reason about the effectiveness of deception and mitigation strategies. By visualizing NIWC-relevant research concepts that are traditionally text- or console-based, learners will leave with improved intuition, shared terminology, and transferable insight into how autonomous cyber systems operate, how risks emerge, and how defensive strategies can be evaluated and refined.

SWT Leaders:

Clinton Anderson (Primary Contact)
Naval Information Warfare Center Pacific
emcclintanderson@gmail.com

Jonathan Buch
Naval Information Warfare Center Pacific
jonathan.m.buch@us.navy.mil

Andreas Hirschmann
Naval Information Warfare Center Pacific
andreasjhirschmann@gmail.com

Governments globally are increasing investments in digital transformation, GovTech ecosystems, and Artificial Intelligence (AI) to tackle complex societal challenges. Issues such as climate adaptation, demographic change, migration governance, public health preparedness, energy transitions, and urban resilience require coordinated, data-intensive, and adaptive responses from the public sector. In this context, digital innovation and entrepreneurship (Digital I&E) have become essential mechanisms for generating new public value. Despite widespread experimentation, sustained institutional transformation remains limited, with the central challenge identified as scaling.

This workshop advances a shift from AI experimentation to AI execution. Rather than framing AI adoption primarily as a regulatory or strategic debate, it conceptualizes AI readiness as an implementation program grounded in infrastructure investment, ecosystem orchestration, and institutional capacity building.

From an Information Systems (IS) perspective, scaling digital innovation in government requires aligning socio-technical systems, including technologies, organizational routines, governance structures, institutional logics, data infrastructures, and human capabilities. GovTech and AI4Gov initiatives introduce digital artifacts such as platforms, algorithms, data pipelines, and decision-support systems, whose impact depends on their integration with legacy infrastructures, interoperability across agencies, regulatory compliance, procurement models, and trust relationships among stakeholders.

In line with emerging debates on AI readiness and strategic digital capacity, the workshop frames scaling as a coordinated capability-building effort: bundling talent, data, compute, and trusted digital infrastructures into functioning public-sector ecosystems. Scaling AI in government, therefore, depends on investments in compute capacity, shared data spaces, secure cloud infrastructures, testing environments, and interoperable platforms, alongside the development of skilled public-sector professionals.

GovTech ecosystems encompass startups, public agencies, higher education institutions (HEIs), and civil society actors, facilitating the co-creation of digital public services. These ecosystems function as innovation intermediaries, promoting experimentation through events like hackathons, regulatory sandboxes, accelerators, and public-private partnerships. AI4Gov initiatives extend this ecosystem logic by positioning AI as critical public infrastructure. Rather than focusing solely on isolated AI applications, AI readiness requires systemic integration across compute resources, data governance frameworks, professional training, regulatory alignment, and testing capacities.

Despite significant policy momentum, numerous GovTech and AI4Gov initiatives are limited to pilot projects. Prototypes demonstrate technical feasibility and immediate efficiency improvements but often do not achieve full institutionalization. Research on digital transformation and public sector innovation frequently identifies persistent barriers, including fragmented data governance, rigid procurement systems, lack of interoperability, insufficient organizational capabilities, regulatory uncertainty, and inadequate cross-sector coordination.

These challenges reflect not only technological gaps but systemic bottlenecks across talent, data access, compute infrastructure, and trusted execution environments. Scaling AI therefore requires coordinated investment strategies that integrate these elements rather than addressing them in isolation.

The workshop builds on insights from the DIGIMPACT project and AI4GOV-X, focusing specifically on mechanisms that facilitate embedding and scaling beyond experimental phases. DIGIMPACT.EU provides empirical evidence across domains such as migration governance, emergency response, sustainable agriculture, and urban mobility, analyzing how Digital Innovation and Experimentation (I&E) initiatives can generate measurable societal impact. AI4GOV-X contributes a complementary execution-oriented model by strengthening AI capacity in public administrations through interdisciplinary education, modular professional training, shared innovation platforms (AI4Scale), and AI-powered knowledge infrastructures (AI4Engine). These initiatives provide relevant perspectives on ecosystem orchestration, AI governance architecture, and pathways for scaling.

The workshop will explore six interrelated thematic areas:

• Socio-technical embedding of GovTech solutions
• Responsible AI governance and AI4Gov architectures
• Ecosystem orchestration and platform governance
• Organizational capabilities and digital leadership

Across these themes, special attention will be given to metropolitan and regional ecosystems as execution layers where infrastructure, research institutions, public authorities, and private-sector actors intersect. AI readiness is built in places, through coordinated investment and partnership, not solely through national policy strategies. The session frames scaling as institutionalization across levels: from pilot to program, from local to regional, from single agency to ecosystem, and from experimental prototype to operational infrastructure. This framing aligns closely with HICSS’ IS-oriented focus on digital platforms, enterprise architectures, governance mechanisms, and socio-technical change.

By convening researchers, policymakers, practitioners, and ecosystem actors, the workshop provides a structured space to compare implementation trajectories, analyze scaling breakdowns, and identify transferable governance and architectural mechanisms. The expected outcome is a refined research agenda to scale GovTech and AI4Gov and achieve sustained societal impact.

SWT Leaders:

Gabriela Viale Pereira (Primary Contact)
University for Continuing Education
gabriela.viale-pereira@donau-uni.ac.at

Tomasz Janowski
Politechnika Gdańska
tomasz.janowski@pg.edu.pl

Peter Parycek
University for Continuing Education
peter.parycek@donau-uni.ac.at

Cities are increasingly shaped by interdependent networks of people, infrastructure, and digital services, where decisions are dynamic and actions in one domain can cascade across others. In this context, Smart City Digital Twins are evolving from visualization and simulation tools into decision infrastructures that support both operational response and strategic planning under uncertainty.

This workshop explores how recent advances in large language models (LLMs), agentic AI, and physical AI can strengthen decision-centric digital twins for complex cyber-physical-human networks (CPHNs). LLMbased interfaces can improve stakeholder access and interpretation of complex system behavior. Agentic AI can support multi-step planning, tool- and simulator-enabled analysis, and structured scenario exploration. Physical AI, embodied or infrastructure-embedded intelligence (e.g., robots, autonomous vehicles/drones, edge AI, smart controllers), can connect digital twin insights to real-world action in safety- and time-constrained environments.

The workshop brings together researchers and practitioners across decision analytics, service science, cyber-physical systems, AI, and smart cities to address a central question: How can smart city digital twins evolve from descriptive representations into credible systems for decision dynamics? We focus on shared challenges that limit progress across domains, including scalable spatiotemporal data integration, rigorous reasoning under uncertainty, validation and governance for trustworthy deployment, and evaluation practices that demonstrate decision impact rather than model fidelity alone.

Participants will leave with a clearer framing of decision infrastructure for smart city digital twins, a shared vocabulary for integrating agentic and physical AI with human oversight, and a set of community priorities that can guide future research, deployments, and cross-disciplinary collaboration.

SWT Leaders:

John Taylor (Primary Contact)
Georgia Tech
jet@gatech.edu

Neda Mohammadi
University of Sydney
neda.mohammadi@sydney.edu.au

The Computerized Intervention Authoring System version 3.0 (CIAS; www.cias.app) is a National Institutes of Health-funded, open-source, non-commercial platform that enables researchers to easily develop, edit, and share sophisticated interactive content without coding of any kind. Interventions built with CIAS deploy as cross-platform compatible web/mobile web apps of any duration, with easy personalization, integrated one- and two-way tailored SMS, optional animated narrators who speak aloud in over 40 languages, and instant translation. Researchers can either use the hosted platform at Michigan State University to build and deploy their interventions (without worrying about the technicalities of platform maintenance) or deploy the platform on their server. A GDPR-compliant instance of CIAS—which will be available to researchers throughout the EU—is also available through the University Medical Center Göttingen, Germany.

This workshop will proceed in four phases. First, we will highlight the advantages of mobile web apps and no-code Software as a Service (SaaS) platforms. Second, we will introduce attendees to CIAS 3.0, highlighting key features and capabilities, followed by independent practice while the presenters circulate and provide guidance. Third, we will consolidate learning from the prior section by helping attendees develop elements of a simple app. Fourth and finally, the presenters will share their experience in disseminating and implementing CIAS-developed applications in healthcare settings. Workshop attendees will leave with the information and skills needed to start developing their own custom digital interventions using CIAS, including access to templates and training resources. Learning objectives include:

• Understanding the implications of a no-code, open-source digital intervention development platform and how it can accelerate research
• Understanding how to access and use CIAS to develop any kind of digital intervention, in any language, and how to use additional features like tailored SMS, aggregate data visualization, and secure live chat
• Learning how to access ongoing CIAS support and how to collaborate on intervention development with other teams

SWT Leaders:

Jordan Braciszewski (Primary Contact)
Henry Ford Health
jbracis1@hfhs.org

Amy Loree
Henry Ford Health
aloree1@hfhs.org

Steve Ondersma
Michigan State
University onders12@msu.edu

The tutorial intends to define computational consciousness & awareness (C&A) to describe basic principles, popular theories, structures and mechanisms for computation, and a possible software architecture and model apparatus for mechanizing C&A in a computer system. An implementation of software architecture for a platform in which to conduct experimental studies will be described.

SWT Leaders:

Stephen Kaisler (Primary Contact)
SHK & Associates
skaisler1@c0mcast.net

Azadin Adamov
ADA University
adamov@ada.az.edu

Brittany Davidson
University of Bath
Bid123@bath.ac.uk

This workshop examines vibe coding as a sociotechnical phenomenon with far-reaching implications for information systems research and practice. Rather than treating it as a purely technical concern, we position vibe coding at the intersection of software development methodology, organisational change, digital innovation, and human-AI collaboration.

The workshop will also introduce a structured workflow designed specifically for domain experts, that is, those knowledgeable in a field but without developer training. In this workflow, the human and AI co-create specifications through structured interviews, the AI autonomously documents and develops within humanapproved boundaries, and the human retains oversight through explicit approval gates and manual testing. A unique feature of the workflow is the option to restart projects at any time via an “escape hatch” functionality that extracts and distils essential project knowledge into a “lifeboat document” to be used as starting point for a new development attempt.

SWT Leaders:

Alex Reppel (Primary Contact)
Royal Holloway, University of London
alexander.reppel@rhul.ac.uk

Mark Lycett
Royal Holloway, University of London
mark.lycett@rhul.ac.uk

Patrick Kirchhoff
patrick.kirchhoff@googlemail.com