Software Technology Track

We welcome research on agile teams and organizations, from enduring challenges to emerging AI-era practices that shape how software teams plan, build, test, deliver, and govern. We also welcome research on low-code, co-pilot and AI-assisted development platforms. While these technologies have the potential to accelerate software development, they may also introduce new organizational challenges.

These organizational challenges take many forms. For example, agile was initially designed for co-located, on-site teams, but organizations today cope with scaling issues and remote and hybrid work. Low-code platforms and AI-assisted programming approaches (e.g., vibe coding tools) are democratizing software development, enabling non-technical personnel to build applications and solve problems directly. This shift creates opportunities for innovation and agility, while also raising important questions about how organizations can support these citizen developers with appropriate training, guardrails, and collaboration models to ensure sustainable, high-quality outcomes.

In this minitrack, we seek research papers and experience reports that explore practices, tools, and techniques for agile development. We also seek to explore how these concepts can be leveraged in other contexts (such as data science or physical product development). Practitioners interested in submitting an experience report are welcome to reach out to a mini-track co-chair for support and guidance, if desired. Our minitrack seeks to answer questions such as:

• How can emerging technologies like AI and machine learning be seamlessly integrated into existing agile software development practices to enhance efficiency and effectiveness?
• How do AI-enabled agents facilitate team communication and coordination? 
• How do agile practices and principles adapt when AI becomes part of the development process?
• How can organizations best support citizen developers?
• What new collaboration models emerge between professional developers and domain experts?
• How to balance team autonomy and decentralized decision-making with the need for organizational control and alignment in large-scale agile development?
• How should agile teams and organizations measure performance, velocity, and value delivery in AI-augmented environments?
• How to scale agile (how to effectively manage dependencies, teams, stakeholders, processes, technologies, and tools) including comparative results on the use of different agile scaling frameworks? 
• What organizational structures are required to enable shared leadership in self-managed teams? 
• How do agile and lean principles extend to DevOps environments? Is there a difference between agile and lean before and after deployment? How are post-deployment issues and opportunities in software projects impacting planning and development of software development projects?
• What organizational structures and novel tools are required to leverage AI, low-coding, and rapid-prototyping as part of the project management process?
• What are the best practices for maintaining efficiency and effectiveness in remote or hybrid agile teams?
• How can agile teams ensure inclusivity and leverage diversity to enhance team performance and innovation?

Possible additional topics for the minitrack include but are not limited to:

• AI-enabled code development tools 
• AI-enabled team collaboration and communication tools
• New frontiers in agile or lean management – going beyond software development
• Forecasting, planning, testing, measurement, and metrics
• Exploring the fit between agile (or lean) organizations and their environmental context
• Agile and lean requirements engineering, and risk management 
• Agile in hybrid digital/physical contexts
• What cultures, team norms and leadership characteristics lead to sustained agility?
• Empirical studies of agile or lean organizations
• Impact of tool use on agile or lean management
• Education and training –new approaches to teaching and coaching agile
• Global software development and offshoring/multi-shoring
• Project management methods, low-code development

Minitrack Co-Chairs:

Jeffrey Saltz (Primary Contact)
Syracuse University
jsaltz@syr.edu

Viktoria Stray
University of Oslo and SINTEF
stray@ifi.uio.no

Alex Sutherland
Scrum, Inc.
alex.sutherland@scruminc.com

The rapid emergence of large-scale foundation models has fundamentally reshaped the technical, economic, and societal landscape of artificial intelligence. While significant attention has been devoted to model architectures, training algorithms, and downstream applications, far less focus has been placed on the AI infrastructure layer that enables foundation models at scale. This minitrack aims to address this critical gap by bringing together interdisciplinary research on the design, operation, optimization, and governance of AI infrastructure supporting foundation models.

AI infrastructure spans a broad and evolving stack, including compute accelerators (GPUs, TPUs, NPUs), data-center architectures, energy systems, storage, networking, scheduling, and orchestration platforms. At the same time, foundation models introduce unprecedented demands on power, cooling, carbon footprint, data movement, resilience, security, and cost structures. These challenges raise new questions that cut across information systems, computer science, energy systems, economics, and public policy.

This minitrack solicits research that explores how infrastructure constraints shape the performance, accessibility, sustainability, and trustworthiness of foundation models and conversely, how foundation models drive new infrastructure paradigms. We are particularly interested in work that connects technical infrastructure decisions with organizational, economic, environmental, and societal outcomes, a perspective that aligns strongly with the HICSS tradition. Topics of interest include, but are not limited to:

• Architectures for AI data centers and distributed AI infrastructure
• Energy-aware and carbon-aware training and inference of foundation models
• Co-design of AI models and hardware/infrastructure systems
• Scheduling, orchestration, and resource allocation for large-scale AI workloads
• Edge–cloud–data-center tradeoffs for foundation model deployment
• AI infrastructure resilience, reliability, and fault tolerance
• Security, privacy, and trust issues in AI infrastructure stacks
• Economic models, cost structures, and market dynamics of AI infrastructure
• AI infrastructure governance, regulation, and public policy implications
• Sustainability, lifecycle analysis, and environmental impacts of AI systems
• Societal implications of concentration and access to AI infrastructure

The minitrack welcomes theoretical, empirical, systems-oriented, and interdisciplinary contributions, including design studies, simulations, field studies, policy analyses, and case studies from academia and industry. By focusing on the infrastructure foundations of AI, this minitrack aims to foster a deeper understanding of how AI systems are built, scaled, governed, and sustained in practice.

Minitrack Co-Chairs:

Reza Ghorbani (Primary Contact)
University of Hawaii at Manoa
rezag@hawaii.edu

Liuwan Zhu
University of Hawaii at Manoa
liuwan@hawaii.edu

Mohammad Taghi Hajiaghayi
University of Maryland
hajiagha@cs.umd.edu

As artificial intelligence (AI) continues to evolve, cyber threats are becoming more sophisticated, automated, and difficult to detect. Adversaries increasingly leverage AI to enhance attack strategies, including phishing, malware generation, adversarial machine learning, and evasion techniques. In particular, AI-driven malware is rapidly emerging, capable of autonomously adapting to security defenses, modifying attack vectors in real time, and bypassing traditional detection mechanisms. Meanwhile, AIpowered defense mechanisms are being developed to counteract these threats, leveraging machine learning for threat intelligence, anomaly detection, and automated mitigation.

This minitrack focuses on the dual role of AI in cybersecurity—both as a weapon for cyber attackers and as a tool for defenders. We invite research contributions on, but not limited to, the following topics:

• AI-generated phishing, social engineering, and deepfake attacks
• AI-driven malware development, obfuscation, and polymorphism
• Automated malware detection and classification using AI
• AI-enhanced vulnerability discovery and exploitation
• Large Language Models (LLMs) in cyber attack automation
• AI-based threat intelligence and intrusion detection
• Countermeasures against AI-powered cyber threats
• Explainability and robustness of AI-driven cybersecurity solutions

This minitrack aims to bring together researchers and practitioners to discuss emerging AI-powered threats and novel defense strategies.

Minitrack Co-Chairs:

Junggab Son (Primary Contact)
University of Nevada, Las Vegas
junggab.son@unlv.edu

Zuobin Xiong
University of Nevada, Las Vegas
zuobin.xiong@unlv.edu

As artificial intelligence (AI) continues to transform various industries, its profound impact on software engineering cannot be understated. This mini track aims to explore the intersection of AI and software engineering, focusing on the innovative ways in which AI technologies are influencing software development, testing, maintenance, and overall software lifecycle management. The proposed mini track invites researchers and practitioners to delve into the multifaceted implications of AI on software engineering practices, providing a platform for insightful discussions and the exchange of cutting-edge research findings.

The integration of AI into software engineering processes is rapidly reshaping the landscape of how software is conceived, developed, and maintained. Understanding the implications, challenges, and opportunities that arise from this potentially symbiotic relationship is crucial for researchers, practitioners, and educators in the field. This mini track seeks to foster a collaborative environment where participants can engage in meaningful dialogue, share their experiences, and contribute to the evolving discourse on the impact of AI on software engineering. Topics of Interest include, but are not limited to:

• AI-driven Software Development Processes:
  1. Automated code generation and optimization
  2. Impact on Software Design
  3. Intelligent code completion and suggestion systems
  4. AI-assisted requirement analysis and specificatio
•  AI in Software Testing and Quality Assurance:
  1. Automated testing using machine learning algorithms
  2. AI-driven fault prediction and localization
  3. Quality assurance in AI-infused software systems
• AI for Software Maintenance and Evolution:
  1. Predictive maintenance and malfunction detection
  2. Intelligent bug tracking and resolution
  3. Adaptive software evolution with AI assistance
• Reshaping Job Profiles
  1. Shifting skill requirements for software engineers
  2. Emerging and evolving job profiles
  3. AI in recruitment and hiring for software engineering roles
• Ethical and Social Implications of AI in Software Engineering:
  1. Bias and fairness in AI-enhanced software systems
  2. Responsible AI practices in software development
  3. Societal impact of AI-driven software solutions
  4. How are the roles reshaped in software development teams
• Educational Initiatives in AI and Software Engineering:
  1. Integration of AI concepts into software engineering curricula
  2. Training programs for AI-aware software engineers
  3. Challenges and opportunities in AI education for software developers

Minitrack Co-Chairs:

Stefan Wittek (Primary Contact)
Clausthal University of Technology0
stefan.wittek@tu-clausthal.de

Sandra Gesing
San Diego Supercomputer Center
sgesing@ucsd.edu

Ryan Karl
Carnegie Mellon University
rmkarl@sei.cmu.edu

Peter Salhofer
FH JOANNEUM – University of Applied Sciences
peter.salhofer@fh-joanneum.at

The expanding capabilities of Generative AI are forging new paths in software development, an area ripe with opportunities for innovation and transformation. This minitrack aims to provide a forum to examine this rapidly evolving landscape, highlighting both the technological advancements and the broader implications of these emergent technologies.

In recent years, the field has shifted from AI as autocomplete toward agentic, tool-using systems that can plan, execute and verify multi-step work across repositories, tests, and CI/CD pipelines; often producing reviewable diffs and pull requests rather than isolated code snippets. This shift reframes software development as human–agent collaboration, where engineers increasingly specify intent, constraints and acceptance criteria while AI systems implement, test and iterate.

We seek to attract a cadre of research that both delineates and critiques the concepts, methods, frameworks, architectures, functionalities, and broader implications of applying and integrating Generative AI and agentic workflows (including coding agents) in software development. The scope of this minitrack includes, but is not limited to, the following key areas:

• Deployment of synthetic data and synthetic artifacts (e.g., requirements, test inputs, logs, traces) for model training and evaluation within development environments, facilitating advanced machine learning applications while addressing data privacy and governance concerns
• Exploration of the ethical dimensions in the use of Generative AI in software development, focusing on intellectual property rights, provenance and attribution, bias in automated outputs, and accountability in decision-making
• Integration of Generative AI and agentic workflows into software development practices, improving code quality and development speed, while considering issues of code originality, review burden, human oversight and skill development/atrophy
• Utilization of Generative AI for creating comprehensive test datasets and test suites (unit, integration, property-based, fuzzing-inspired, and scenario tests), enhancing software testing effectiveness, and uncovering critical edge cases
• Intersection with agile and DevOps methodologies, leveraging Generative AI for continuous integration and adaptive responses to evolving project requirements, including DevSecOps automation (policy checks, dependency risk triage, secret scanning workflows, secure-by-default patterns) in agentic pipelines
• Implications of Generative AI in legacy system sustainment, addressing the challenges and opportunities in modernizing and maintaining older software infrastructures, including large-scale refactoring, migration, and documentation of institutional knowledge
• The role and impact of Generative AI and agentic workflows in software innovation, fostering new methods in ideation, brainstorming and co-designing, thus revolutionizing traditional approaches to software development and project conceptualization
• Evaluation and measurement of outcomes in agentic software development: productivity and cycle time, defect density, maintainability, security posture, reliability, and socio-technical impacts; and how these tradeoffs vary by task type, developer expertise, and organizational context
• Bright side and dark side: productivity and democratization on the one hand, and on the other hand security risks, compliance/privacy leakage, automation bias, and shifting accountability when agents can act across tools and environments.

This minitrack aspires to be a platform for rigorous scholarly inquiry into the multifaceted applications of Generative AI in software development, emphasizing both the innovative potential and the consequential ethical, legal and operational challenges. We welcome empirical studies, design-science research, theorybuilding, field experiments and mixed-method work grounded in real development settings.

Minitrack Co-Chairs:

Johnny Chan (Primary Contact)
University of Auckland
jh.chan@auckland.ac.nz

Brice Valentin Kok-Shun
University of Auckland
brice.kok.shun@auckland.ac.nz

David Sundaram
University of Auckland
d.sundaram@auckland.ac.nz

Ghazwan Hassna
Hawaiʻi Pacific University
ghassna@hpu.edu

As AI, including foundation models and generative AI, becomes deeply embedded in critical infrastructures, cyber operations, finance, healthcare, national security, and everyday digital services, new classes of vulnerabilities and threats emerge. These include data poisoning and model manipulation, adversarial and evasion attacks, model extraction and membership inference, prompt injection and jailbreaks targeting large language models, and cascading failures in autonomous and multi agent systems. At the same time, organizations face growing pressure to demonstrate that AI systems are reliable, robust, transparent, and aligned with regulatory, ethical, and safety expectations.

This minitrack explores the multifaceted challenges of securing and assuring AI systems across their entire lifecycle—from data collection and model training to deployment, operation, and retirement. It aims to provide a forum for innovative research that advances the security, safety, and assurance of AI systems. It invites contributions at the intersection of:

• Technical security and robustness (e.g., algorithms, architectures, formal methods, resilient design)
• Operational assurance and governance (e.g., secure MLOps, AI red teaming, evaluation and monitoring)
• Policy, ethics, and regulation (e.g., AI risk management, standards, and compliance)

The Journal of Information Technology, Cybersecurity, and Artificial Intelligence (JITCAI) would be interested in a special issue from the minitrack from the HICSS-60 conference.

Minitrack Chair:

Tyson Brooks (Primary Contact)
National Security Agency and Syracuse University
ttbrooks@syr.edu

Shiu-Kai Chin
Syracuse University
skchin@syr.edu

Erich Devendorf
Air Force Research Laboratory Information Directorate
erich.devendorf.1@us.af.mil

Prakash Sarathy
Northrop Grumman
sriprakash.sarathy@ngc.com

As technology is incorporated into more aspects of daily life, cyber operations, defenses, and digital forensics solutions continue to evolve and diversify. This encourages the development of innovative managerial, technological, and strategic solutions. Hence, a variety of responses are needed to address the resulting concerns. There is a need to research a) technology investigations, b) technical integration and solution impact, c) the abuse of technology through attacks, and d) the effective analysis and evaluation of proposed solutions. Identifying and validating technical solutions to secure data from new and emerging technologies, investigating the impact of these solutions on the industry, and understanding how technologies can be abused are crucial to the viability of commercial, government, and legal communities.

We welcome new, original ideas from participants in academia, industry, government, and law enforcement who are interested in sharing their results, knowledge, and experience. Topics of interest include, but are not limited to:

• Human aspects of cyber security operations and defense
• The impact of AI and Generative AI on cyber security operations and defense
• Research efforts that intersect cyber security, operations, defense, and counterterrorism
• Cybersecurity, operations, and defense research that impacts critical infrastructure sectors
• Applying machine learning tools and techniques in terms of cyber operations, defenses, and forensics
• Case studies surrounding the application of policy in terms of cyber operations, defenses, and forensics
• Approaches related to threat detection and Advanced Persistent Threats (APTs)
• “Big Data” solutions and investigations – collection, analysis, and visualization of “Big Data” related to cyber operations, defenses, and forensics
• Malware analysis and the investigation of targeted attacks
• Digital evidence recovery, storage, preservation, memory analysis, and network forensics, including anti-forensics techniques and solutions
• Forensic investigations of current and emerging domains, including mobile devices, the Internet of Things, industrial control systems, SCADA, etc.
• Research in security incident management, including privacy, situational awareness, and legal implications

The above list is suggestive, and authors are encouraged to contact the mini-track chairs to discuss related topics and their suitability for submission to this minitrack.

Accepted papers will be offered the opportunity to extend their submission by 50% and submit to a special issue of the Association for Computing Machinery (ACM) Digital Threats: Research and Practice (DTRAP) Journal.

Minitrack Co-Chairs:

William Glisson (Primary Contact)
Louisiana Tech University
glisson@latech.edu

Todd McDonald
University of South Alabama
jtmcdonald@southalabama.edu

Gregory Bott
University of Alabama
ggrispos@unomaha.edu

Dependability denotes justified reliance on a system’s delivered service. The foundational taxonomy of Laprie et al. (2004) structures dependability along three dimensions: attributes (availability, reliability, safety, integrity, maintainability), threats (faults, errors, failures), and means (fault prevention, fault tolerance, fault removal, fault forecasting). Security which comprises confidentiality, integrity, and availability, integrates as both attribute and cross-cutting concern.

Cyber-Physical Systems (CPS) and Systems-of-Systems (SoS) now underpin critical societal infrastructure: autonomous vehicles, surgical robotics, smart grids, industrial automation. Their correctness is no longer desirable but mandatory; failures propagate across system boundaries with potentially catastrophic consequences. Managing this complexity requires dependability as a first-class engineering concern throughout the system lifecycle.

AI/ML components amplify this challenge. Learned models introduce fault classes absent from classical software: data distribution shifts, adversarial perturbations, specification incompleteness, opaque decision boundaries. Established verification and validation paradigms require extension; assurance cases must accommodate probabilistic, data-dependent behaviour.

Socio-technical dimensions additionally compound technical complexity. Human operators interact with autonomous systems under uncertainty; organisational processes govern development, deployment, and maintenance; regulatory frameworks (EU AI Act, IEC 61508, ISO 26262) impose compliance constraints. Dependability emerges from the interplay of technical architecture, human factors, and organisational context, not from any single dimension in isolation.

This minitrack addresses dependability across both technical systems (CPS, embedded systems, autonomous systems, AI-enabled components) and socio-technical systems (human-machine teaming, organisational resilience, regulatory compliance). We seek contributions grounded in dependability concepts that advance theory, methods, tools, or empirical understanding by bridging foundational frameworks with the engineering realities of contemporary and emerging system classes. We invite contributions presenting novel methods, tools, frameworks, empirical studies, and industrial experiences. Topics include but are not limited to:

• Dependability Foundations
  1. Specification, modelling, and quantification of dependability attributes (availability, reliability, safety, integrity, maintainability) for CPS and SoS
  2. Fault taxonomies and fault models for AI/ML components: data faults, model drift, adversarial inputs, specification gaps
  3. Trade-offs and interactions among dependability attributes in complex system architectures
• Dependability Means
  1. Formal methods, model-based engineering, and correct-by-construction approaches for dependable systems
  2. Fault tolerance architectures: redundancy, graceful degradation, self-adaptation
  3. Verification and validation of AI/ML components: neural network verification, runtime monitoring, assurance cases
  4. Fault forecasting: predictive maintenance, digital-twin-based prognostics, security and reliability modelling
• Socio-technical Dependability
  1. Human-machine teaming and shared autonomy in safety-critical systems
  2. Organisational factors: security, safety culture, process maturity, and their impact on system dependability
  3. Regulatory compliance engineering (EU AI Act, IEC 61508, ISO 26262)
• Empirical Studies
  1. Industrial case studies, failure analyses, and lessons learned from automotive, aerospace, healthcare, energy, manufacturing, etc…. domains
  2. Comparative evaluation and benchmarking of dependability techniques

This minitrack provides a venue for cross-disciplinary exchange grounded in established dependability concepts while addressing emerging challenges from AI-enabled autonomy and increasing system complexity. It aims to advance the state of research and practice in engineering dependable systems, spanning technical CPS/SoS and socio-technical contexts, by bringing together researchers and practitioners from software engineering, systems engineering, safety engineering, AI/ML, and human factors.We target:

• Researchers developing methods, models, and tools for dependability analysis, assurance, and engineering
• Practitioners addressing dependability challenges in safety-critical and mission-critical domains
• Academics and industry professionals working at the intersection of AI/ML and system dependability

Minitrack Co-Chairs:

Philipp Zech (Primary Contact)
University of Innsbruck
philipp.zech@uibk.ac.at

Irdin Pekaric
University of Liechtenstein
irdin.pekaric@uni.li

Tom Mattson
University of Richmond
tmattson@richmond.edu

Raffaela Groner
Chalmers University of Technology and University of Gothenburg
raffaela.groner@ms.informatik.uni-stuttgart.de

Sustainable Development Goals of United Nations invite action from all levels of the society to help solving the problems of the world together. Toward these goals, games can offer potential solutions with their ability to mimic, contain, or sample real or plausible scenarios and systems in a readily accessible simulation. They are inherently player centric; therefore, player’s perspective and involvement for the intended experience derives the success of a game. From this central role of the player comes the power of games to educate, rehabilitate, recreate and rejoice with entertainment.

This minitrack intends to draw attention to the use of games and game technology for special purposes and positive outcomes where the created experience reaches beyond entertainment. Recognising that games are a powerful vehicle to make emerging technologies accessible to society, this mini track creates a space to explore the many factors that influence the design, development, application, adoption, use, and impact of games and game technology.

The exploration of Games for Impact minitrack falls under the umbrella of recent games research fields such as games for health/rehabilitation/therapy, games for learning, games for empathy, games for social innovation, and citizen science games. Potential subtopics or areas including but not limited to the following are listed below:

• Case study on designing, developing, using, and evaluating games for special purposes
• Best practices and guidelines on game design, study design, interaction design, user experience (UX) and user interface (UI)
• The role and application of games and game technology in creating, disseminating, and evaluating social innovation
• The application and impact of games and game technology in education and its accessibility
• The application and impact of games for training, learning, and personal development (habit building, empathy, social skills, etc.)
• Evaluation approaches, criticality, quality measures, and ethics of using and adapting games and games technology in other fields such as health, rehabilitation, education, social innovation, citizen science
• Use of novel interaction modalities, platforms and/or controllers, IoT, VR-AR-MR
• Analysis for socio-cultural context of games for impact
• Demographics, persona studies, and ethics of the application, adoption, and impact of games and game technology for purposes beyond entertainment

We welcome contributions on design and development methods, technical studies that focus on implementation and development guidelines, case studies with novel interaction modalities including platforms (mobile, AR-VR-MR) and/or controllers, user experience (UX) approaches, user interface (UI) techniques, analysis for socio-cultural context of games for impact, demographics and persona studies, and ethical studies for the aforementioned research fields.

Minitrack Co-Chairs:

Asli Tece Bayrak (Primary Contact)
Media Design School at Strayer
tece.bayrak@mediadesignschool.com

Dan Staines
Torrens University Australia
daniel.staines@torrens.edu.au

Generative AI (GenAI) refers to a class of AI systems capable of producing novel content, such as text, images, audio, video, code, and simulations, by learning patterns from large-scale data. Recent advances in foundation models, multi-modal large language models (LLMs), agentic AI, and emotion-aware systems have expanded the role of AI beyond content generation toward autonomous reasoning, goal decomposition, planning, tool use, emotional responsiveness, and human-centered interaction.

Conversational AI enables natural, context-aware, and increasingly affective interactions between humans and intelligent systems. Recent advances in agentic AI allow systems to autonomously decompose goals, coordinate with other agents, invoke external tools, and adapt their behavior over time. At the same time, Emotional AI and Compassionate AI further allows systems to recognize and respond to human emotions, empathy, and social cues, creating new opportunities and ethical challenges for the design and use of Information Systems (IS).

However, these advances also introduce significant risks, including hallucinations, overconfidence, excessive agreeableness, and emotionally persuasive behaviors that may mislead users, reduce critical thinking, or erode trust. Understanding when AI systems should be expressive, empathetic, uncertain, or constrained is a central challenge for IS research and education.
This minitrack provides a forum for exploring the design, governance, evaluation, and pedagogical integration of Generative, Conversational, Agentic, Emotional, and Compassionate AI in IS contexts. The minitrack is of interest to IS researchers, educators, and practitioners seeking to advance responsible, effective, and human-centered AI-enabled information systems.

We particularly encourage submissions that offer strong theoretical contributions and novel empirical insights into human–AI collaboration, digital innovation, governance, and broader socio-technical change.

• Design and Deployment of Human-centered GenAI Systems
  1. Design and deployment of Generative and Conversational AI in IS context
  2. GenAI applications for decision support, personalization, recommendations, and enhanced user experience
  3. LLMs and multimodal AI for decision support and intelligence assistance
  4. Augmented intelligence that enhances human capabilities rather than replace it
• Human–AI Interaction
  1. Human–AI collaboration and AI-mediated work practices in organizations
  2. Trust, transparency, calibration, and alignment in human-centered AI interactions
  3. Emotional, compassionate, and socially responsive AI: measuring empathy, agreeableness, and user trust
  4. Generative AI as enabler or disabler of critical thinking, creativity, and decision quality
• Governance, Ethics, and Societal Implications
  1. Ethics, privacy, regulation, and responsible governance of Generative, Agentic, and Emotional AI
  2. Platform governance and ecosystem dynamics, including emotionally persuasive AI
  3. Misinformation, manipulation, and the dark side of GenAI
  4. Detection, accountability, and transparency of AI-generated content
• IS Education and Workforce Development
  1. Integrating GenAI and Conversational AI into IS curricula
  2. Educational resources, teaching cases, and AI literacy development
  3. Preparing students for AI-enabled careers and future-of-work transformation
• Research Methods and Evaluation
  1. Metrics for evaluating effectiveness, trust, and behavioral impact
  2. Design science, experiments, field studies, and mixed method approaches in GenAI
  3. Synthetic data generation and validation challenges

Minitrack Co-Chairs:

Nargess Tahmasbi (Primary Contact)
Pennsylvania State University
nvt5061@psu.edu

Elham Rastegari
Creighton University
elhamrastegari@creighton.edu

Guohou (Jack) Shan
Northeastern University
g.shan@northeastern.edu

Aaron French
Kennesaw State University
afrenc20@kennesaw.edu

Intelligent Edge Computing focuses on the synergy of software, algorithms, computing, and devices operating at or near the source of data generation and use, spanning autonomous, mobile, robotic, IoT/IoE, and human-in-the-loop systems. While edge systems are ubiquitous, their intelligence capabilities increasingly lag behind desires and demands due to constraints in Size, Weight, and Power (SWaP), hardware architectures, communication, and deployment environments.

This minitrack highlights advances in low-SWaP computing, neuromorphics, reconfigurable and FPGA-based platforms, continual and federated learning, generative AI models, and edge-optimized AI. Given the computational demands of modern AI, including large language models and generative models, the track emphasizes AI+X approaches where application-driven deployment, sustainability, energy efficiency, and adaptable federated edge solutions are central. The topics of interests include (not limited to):

• Hardware Solutions for Edge Applications
  1. Edge hardware platforms, including neuromorphic processors, TPUs, FPGAs, microcontrollers, PLCs, and reconfigurable devices
  2. Edge–cloud–fog computing paradigms, including serverless and hybrid deployment models
  3. Autonomous systems, mobile robots and UAVs, their development, architectures, and use
  4. Computability, scalability, and sustainability within constrained edge environment
  5. Edge processing of sensor and streaming data for autonomous and cyber-physical systems
• Edge Intelligence & AI+X
  1. Architectures and roadmaps for AI at the computational edge
  2. AI+X approaches tailored to specific edge applications and domains
  3. Edge-native foundation models and task-specialized micro-models
  4. Model composition, routing, and specialization across heterogeneous edge devices
  5. Federated, collaborative, and continual learning across distributed edge devices
  6. Adversarial, cooperative, and human-machine decision-making at the edge
  7. Potential convergence of humans,” things” and AI in creating edge intelligence
• Generative AI and Sustainable Edge Computing
  1. Resource-aware deployment of generative and foundation models at the edge
  2. Energy-efficient inference, specialization, and adaptation of large models
  3. Sustainable edge platforms for industrial, environmental, and societal applications
  4. Hardware–software co-design for generative AI under SWaP constraints
  5. Agentic and goal-driven edge intelligence with autonomous adaptation and control
  6. Self-optimizing, self-healing, and self-configuring edge systems under resource constraints
• Edge Robotics and Cognitive IoT
  1. Edge-enabled robotics, wearables, and human augmentation
  2. Neuromorphic and lightweight learning for robotic control and perception
  3. Swarm, multi-robot, and collaborative intelligence at the edge
  4. Industry-specific challenges in edge robotics and cognitive IoT systems
  5. World models, predictive cognition, and internal simulation for edge robotics and autonomous systems
  6. Edge-based digital twins for perception, control, and decision support

Minitrack Co-Chairs:

Trevor Bihl (Primary Contact)
Ohio University
bihlt@ohio.edu

Radmila Juric
ALMAIS Consultancy
radjur3@gmail.com

Frank Zhang
Wright State University
xiaodong.zhang@wright.edu

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

This minitrack provides a dedicated place for research where the main contribution is technical depth on security, correctness, verifiabiltiy, cryptography, and real-world robustness of decentralized systems, including the measurement of failures and defenses in deployed ecosystems. We invite research on building, analyzing, and validating blockchain and Web3 systems under realistic adversarial and economic conditions. The focus is on protocol and smart contract security, privacyenhancing cryptography, verification and formal methods, interoperability, scaling, and empirical measurement of attacks and mitigations. Work can be technical or empirical as long as the core contribution advances the security and verifiability of decentralized systems and applications. Topics of interest include:

• Consensus and protocol security, adversarial modeling, censorship resistance, resilience, performance
• Layer 2 and modular scaling, rollups, data availability, state channels, shared security
• Cross-chain interoperability, bridge designs, bridge security, composability risks
• Smart contract security, auditing methods, program analysis, secure languages and tooling
• Formal methods and verification for protocols and contracts, specification, proofs, certified implementations
• Zero knowledge and privacy tech for Web3, MPC, threshold cryptography, verifiable computation, privacy preserving compliance
• Cryptoeconomics and mechanism design, incentive attacks, MEV, transaction ordering, oracle games
• Measurement and telemetry, on-chain security analytics, incident datasets, forensic methods, ecosystem risk metrics
• Key management and wallet security, custody, recovery, phishing resistance, usable security
• Oracles and secure off-chain components, TEEs, hardware anchored trust, secure integration patterns

Minitrack Co-Chairs:

Ivan Homoliak (Primary Contact)
Brno University of Technology
homoliak@fit.vutbr.cz

Claudio Tessone
University of Zurich
tesone@ifi.uzh.ch

Ivan Visconti
Sapienza University of Rome
visconti@unisa.it

Information security and privacy are a non-negotiable factor in the design and operation of information systems. Especially users, the so-called human factor, are a pivotal role in information security and user-privacy concepts. Often, their knowledge about security aspects and ways of user-manipulation tactics are the last line of defense against cyber-attacks. However, they are also the primary target of attackers and need to be sensitized about security-compliant behavior.

In addition to the traditional forms of user-computer-interactions in the form of mouse-keyboard-inputdevices, new ways of system-interactions, e.g., physiological data from fitness-trackers, eye-tracking devices or even pupillary responses indicating cognitive-load-levels, are increasingly feasible as everyday HCI-components. With the interest in data privacy increasing, are users aware how valuable those personal input data is and how do they value data privacy measures. Therefore, we have identified two main aspects relevant to researchers within the domain of Software Technology:

• How to securely deal with input data (also focusing on privacy aspects)
• How this data can be utilized to increase secure behavior or to raise awareness among users (help the users to make better security-related decisions)

In this Minitrack, we seek papers that explore concepts, prototypes, and evaluations of how users interact with information systems and what implications these interactions have for information security and privacy. Further, we welcome new and innovative ways of human-computer-interaction and security-related concepts currently examined in the field. Topics of interest include but are not limited to:

• Security related devices
• Physiological sensors
• Human-Computer-Interaction
• (Conversational) Artificial intelligence
• Blockchain applications
• Sensor analysis
• Data visualization
• Biometrics authentication
• Security and privacy awareness
• Accessibility
• Usable security design
• Privacy and security by design
• Privacy and smart contracts
• User valuation of privacy
• Validation of user data

Minitrack Co-Chairs:

Tobias Fertig (Primary Contact)
Technical University of Applied Sciences Würzburg-Schweinfurt
tobias.fertig@thws.de

Nicholas Müller
Technical University of Applied Sciences Würzburg-Schweinfurt
nicholas.mueller@thws.de

Paul Rosenthal
University of Rostock
paul.rosenthal@uni-rostock.de

The minitrack is devoted to the technological background while keeping an eye on business value, user 2 experience, and domain-specific issues. Contributions may take a sociotechnical view or report on technological progress. We are particularly interested in applied software technology but also welcome theoretical work. Topics of interest include the full spectrum of research on software development, for example (but not limited to):

• Case studies of development
• Development methods, software architecture, and specification techniques
• Economic and social impact, behavioral aspects
• Software engineering education
• User interface (UI) design and user experience (UX) research
• Hybrid and cross-platform development
• Web technology
• Security, safety, and privacy
• Energy-efficient computing
• Machine learning on device
• The convergence between mobile devices, IoT, and CPS
• Fog, edge, and dew computing and their computational applications

Minitrack Co-Chairs:

Tim A. Majchrzak (Primary Contact)
CAIS and Ruhr University Bochum
tim.majchrzak@rub.de

Tor-Morten Grønli
Kristiania University College
tor-morten.gronli@kristiania.no

Hermann Kaindl
TU Wien
kaindl@ict.tuwien.ac.at

The Sustainable Software: Usable, Maintainable, Reproducible minitrack at HICSS continues to address the evolving landscape of research software, now increasingly shaped by artificial intelligence (AI) and machine learning (ML). As AI becomes a foundational component of scientific research, additional challenges emerge in ensuring software usability, sustainability, and reproducibility. The integration of AIdriven workflows, automated code generation, and large-scale foundation models introduces complexities in software maintainability, explainability, and ethical considerations. This minitrack explores how research software can adapt to these advancements, ensuring long-lasting, reusable, and trustworthy tools that support diverse scientific communities.

The focus on software usability, sustainability, and reproducibility is more critical than ever, including new trends initiated via AI and ML spanning diverse scientific domains and receiving significant investment in the U.S., Europe, the U.K., and beyond. Research software remains a fundamental driver of discovery, with over 90% of researchers relying on software and more than 65% indicating that their work would be impossible without it. As AI and ML become deeply embedded in research software, new usability challenges arise, such as ensuring transparency in AI-driven decision-making, designing intuitive interfaces for complex models, and supporting reproducibility in evolving AI ecosystems. The computational landscape has shifted from system-centered design to user-centered approaches, and now, increasingly, AI-assisted software development, where automation plays a role in code generation, debugging, and optimization. The prominence of AI-powered research software raises important concerns about long-term maintainability, ethical AI practices, and the ability to reproduce computational results in a rapidly changing technological environment. Addressing these issues is essential for enabling researchers to build on existing work, validate findings, and accelerate scientific progress.

The three concepts of usability, sustainability, and reproducibility are deeply interconnected and span all stages of the research software lifecycle. AI-powered tools introduce new dimensions to these challenges, from enabling reproducible experiments through automated workflows to ensuring model transparency and interpretability. Techniques such as containerization, automated machine learning, and AI-driven software testing are increasingly used to enhance application portability and maintainability. Such concepts are also relevant in the building of Science Gateways (also known as virtual laboratories or virtual research environments), which by definition serve communities with end-to-end solutions tailored specifically to their needs. As research software continues to evolve, this minitrack will highlight novel methodologies, case studies, and best practices that ensure research software remains usable, sustainable, and reproducible in the long term. Consequently, we anticipate submissions not limited to but in the scope of the following topics:

• Web-based solutions (web sites, science gateways, virtual labs, etc.)
• Application Programming Interfaces (APIs)
• Computational and Data-Intensive Workflows
• Novel approaches in containerization
• Sustainability practices in software development
• System architectures for testing and continuous integration
• Emerging best practices in Machine Learning software
• Best practices and Key Success Factors for usability, sustainability and reproducibility
• Community building practices
• Sustainability practices in software development, with a focus on AI applications
• System architectures for testing and continuous integration in AI systems
• Emerging best practices in AI and Machine Learning software
• Addressing ethical considerations in AI-related software
• Best practices and Key Success Factors for usability, sustainability, and reproducibility in the context of AI
• AI-assisted software development (automated code generation, debugging, and optimization)
• Explainability and transparency in AI-driven software
• Automated reproducibility in AI workflows (versioning, benchmarking, and validation of ML models)

Minitrack Co-Chairs:

Maytal Dahan (Primary Contact)
Texas Advanced Computing Center
maytal@tacc.utexas.edu

Joe Stubbs
Texas Advanced Computing Center
jstubbs@tacc.utexas.edu

Sandra Gesing
San Diego Supercomputer Center
sgesing@ucsd.edu