Lixiang Yan, Tsinghua University

Yizhou Fan, Peking University

Yueqiao Jin, Monash University

Nancy Law, University of Hong Kong

Yu Zhang, Tsinghua University

Xibin Han, Tsinghua University

This special section examines agentic AI in education, defined as computational systems exhibiting interactivity, bounded autonomy, and adaptability within human-defined objectives. Unlike reactive AI tutoring systems or prompt-based generative AI tools, agentic AI initiates action, sustains participation, and maintains functional roles through persistent memory and task decomposition. The shift toward such systems reconfigures how epistemic labour and coordination are distributed within learning communities, affecting learners' perceptions of agency, shared regulation, and collaborative reasoning.

The section welcomes empirical and conceptually grounded studies exploring how agentic AI reshapes learner perceptions, teacher roles, epistemic labour, and social-cognitive dynamics. Particular emphasis is placed on understanding when artificial agency supports productive learning versus constraining learner autonomy, examining this across diverse educational contexts, learner populations, and cultural settings. Contributions addressing governance, responsibility, equity, and methodological innovation are encouraged.

Appel publié par British Journal of Educational Technology. Lire l'appel complet sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Conceptualising Agentic AI as Artificial Agency in Education: Frameworks and models that define agentic AI explicitly in terms of interactivity, bounded autonomy, and adaptability, and that clarify boundaries between agentic AI, generative AI assistants, and reactive AIED systems.
• Learner and Teacher Perceptions of Artificial Agency: Empirical investigations of how learners and educators interpret AI initiative, persistence, and role adoption, and how these perceptions shape trust, attribution of intent, responsibility, and participation in learning activities.
• Epistemic Labour and Initiative Redistribution: Studies examining how agentic AI redistributes epistemic labour within individual or collaborative learning, including effects on problem framing, task coordination, evaluation, and decision-making when AI initiates actions rather than responding to prompts.
• Regulation, Coordination, and Social-Cognitive Dynamics: Analyses of how agentic AI influences self-regulation, co-regulation, and shared regulation, particularly in group settings where AI adopts persistent roles that shape interactional norms, turn-taking, and closure of inquiry.
• Temporal Persistence and Path Dependence in Learning Processes: Research that captures how agentic AI's sustained participation and memory create path-dependent learning trajectories, including benefits for coherence and risks of early framing effects or cascading errors.
• Identity Inference, Social Presence, and Responsibility: Empirical work on how proactive AI behaviour affects identity inference and social presence in cue-lean environments, and how responsibility and accountability are negotiated when learning actions originate from artificial agents.
• Methodological Approaches for Studying Artificial Agency: Methodological contributions that advance the measurement of artificial agency and its effects, including multimodal analyses, process-level modelling, and designs that distinguish agentic participation from tool-based assistance.
• Governance and Educational Implications of Agentic AI: Analyses addressing accountability, transparency, and institutional responsibility when agentic AI functions as a collaborator, coordinator, or evaluator in educational settings.

Timeline

• August 1, 2026: Abstract submission deadline
• October 28, 2026: Full manuscript submission deadline
• March 15, 2027: Final manuscript acceptances
• May 1, 2027: Issue publication online

Yingjie Zhang, Peking University

Shunyuan Zhang, Harvard University

Tianshu Sun, Cheung Kong Graduate School of Business

Liangfei Qiu, University of Florida

Nagesh Murthy, University of Oregon

This special issue addresses how autonomous AI systems are fundamentally changing decision-making across organizations, markets, and society. It recognizes that AI has shifted from systems that respond to instructions to agents that initiate workflows, use tools, and operate with meaningful autonomy. The issue aims to advance rigorous understanding of how agentic AI reshapes decisions while developing research practices accessible to AI agents themselves.

Two tracks serve different purposes. Track A (preferred) focuses on original decision science research on agentic AI behavior and human-agent collaboration, requiring submissions to include agent-native research artifacts that allow future agents to reconstruct and extend studies. Track B addresses broader consequences of agentic AI for organizations, markets, labor, and society without such artifact requirements. Both tracks welcome rigorous empirical and analytical research grounded in decision science.

Appel publié par Decision Sciences. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Agent reasoning, planning, and decision strategies in task environments
• Multi-agent coordination, competition, and emergent collective behavior
• Agent capabilities, limitations, and failure modes in decision-relevant contexts
• Agent identity, role-taking, and consistency under manipulation
• Trust, deception, and norm violation in agent systems
• Agent learning and behavioral change over repeated interactions
• Human reliance, override, and complementarity in agentic decision systems
• Oversight, accountability, and governance design in human-agent systems
• Human capacity in human-agent systems and AI Quotient (AQ)
• Interface and system design conditions that shape human-agent collaboration outcomes
• Societal consequences of agentic AI, including inequality, power, and human autonomy
• Organizational transformation involving strategy, structure, and governance
• Labor market change and workforce displacement
• Market and competitive dynamics when agents act for firms or consumers
• Accountability, transparency, and fairness in agentic decision pipelines
• Regulation and governance of autonomous AI systems

Timeline

• August 1, 2026: CFP Launch & Editorial Piece Published
• August 1, 2026: Submissions open (both tracks)
• January 15, 2027: Fast Track Submission Deadline (Track A only)
• March 15, 2027: Submission Deadline (Track B)

Associate editors

• Dominik Gutt, RWTH Aachen University
• Miguel Godinho de Matos, Católica-Lisbon
• Yicheng Song, University of Minnesota
• Yifan Yu, Hong Kong University
• Hyeokkoo Eric Kwon, Nanyang Technological University
• Yang Gao, University of Illinois at Urbana-Champaign
• Zhe Yuan, Zhejiang University
• Brian Han, University of Illinois at Urbana-Champaign
• Yan Leng, University of Texas at Austin
• Wen Wang, University of Maryland
• Liu Ming, The Chinese University of Hong Kong, Shenzhen
• Zhenyu Zheng, Zhejiang University
• Xilin Li, Cheung Kong Graduate School of Business

Joon Mo Ahn, Korea University

Alberto Di Minin, Scuola Superiore Sant'Anna

Sungjoo Lee, Seoul National University

Ahreum Hong, Kyung Hee University

This special issue addresses how artificial intelligence systems function as autonomous innovators rather than tools within R&D management. As AI systems including large language models and physical AI become active participants in innovation processes, existing frameworks assuming human actors must be reconceptualized. The core challenge extends beyond how AI assists humans to how much cognitive work should be delegated to AI and what consequences this has for organizations, ecosystems, and intellectual property.

The special issue examines four interconnected themes: determining optimal levels of AI delegation; understanding how agentic and physical AI reshape innovation ecosystem architectures as autonomous actors; reconceptualizing absorptive capacity for evaluating AI-generated knowledge; and addressing IP ownership and inventorship when non-human systems generate patentable outputs. Collectively, these themes interrogate how the distinction between AI-as-tool and AI-as-innovator requires new governance mechanisms and strategic frameworks for innovation management.

Appel publié par R and D Management. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Is there an inverted-U relationship between the degree of AI delegation in R&D and innovation performance, mirroring the over-search paradox?
• How do firms determine the optimal breadth and depth of AI involvement across different stages of the R&D process? Specifically, under what conditions does algorithmic management enhance or hinder innovation performance?
• What is the AI-era analogue of the NIH syndrome: how does uncritical acceptance of AI-generated knowledge erode internal expertise?
• What governance architectures enable effective human–AI teaming while preserving accountability, creativity, and strategic judgment?
• How do agentic AI systems alter the architecture of knowledge flows in open innovation ecosystems, and what new governance mechanisms are required?
• How does the participation of autonomous AI agents change the logic of platform-based open innovation, including roles, incentives, and boundary conditions?
• How do digital twins and AI-enabled simulation reshape the scope and speed of distributed experimentation across organisational boundaries?
• What new forms of inter-organisational trust, contracting, and coordination are needed when AI agents act as innovation partners?
• How must absorptive capacity (or dynamic capabilities) be reconceptualised when the primary external knowledge source is an AI system rather than a human partner?
• What organisational routines and managerial processes enable firms to transform AI-generated knowledge into innovation value?
• What individual competencies — technical, cognitive, and relational — distinguish high-performing innovators in human–AI contexts?
• What learning mechanisms allow firms to continuously upgrade AI-related innovation capabilities over time, particularly in relation to exploration and exploitation?
• How should inventorship and IP ownership be attributed when agentic AI systems autonomously generate patentable outputs, and what theoretical frameworks from open innovation research best capture this challenge?
• What strategic logic governs firms' decisions to release model weights, training data, or fine-tuned AI capabilities into open-source commons?
• How do trained model weights, fine-tuning data, and emergent AI capabilities constitute a new category of strategic asset, and how do firms govern access to and monetisation of these assets?
• Under what regulatory and institutional conditions does the open-source AI movement accelerate versus impede innovation diffusion?

Timeline

• May 30, 2026: PDW (paper development workshop) at R&D Management Workshop, Seoul, Korea
• July 4, 2026: PDW at KOSIME summer conference, Jeju, Korea
• December 1, 2026: Special Issue Submission Open
• June 30, 2027: Deadline for SI Submission
• July 1, 2028: Publication of SI Articles

Rahul Sindhwani, Indian Institute of Management – Sambalpur

Varsha Jain, ESSCA School of Management

Justin Zhang, University of North Florida

Sudhir Rana, Liwa University

This Special Issue aims to advance both theory and practice regarding consumer behavior in the age of agentic AI. Theoretically, it seeks to deepen understanding of how autonomous AI agents influence fundamental consumer processes such as decision-making, trust, perceived autonomy, and engagement.

Appel publié par Journal of Consumer Behaviour. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Consumer decision-making with autonomous AI agents
• Trust in agentic AI systems
• Perceived autonomy in AI-influenced consumer processes
• Consumer engagement with autonomous AI agents
• Theoretical frameworks for agentic AI and consumer behavior
• Practical applications of agentic AI in consumer markets
• Autonomous agents and fundamental consumer processes

Timeline

• July 1, 2027: Submission window opens
• December 31, 2027: Submission window closes

Appel publié par Technological Forecasting and Social Change. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Timeline

• January 31, 2027: Full paper submission deadline

This Journal of Enterprise Information Management Special Issue seeks to understand Agentic Artificial Intelligence and Generative AI (GenAI) in healthcare, and how these technologies impact the governance, strategy, and value creation of healthcare organizations. In technological innovation, digital technologies are reconfiguring value creation processes and prompting organizations to develop new adaptive strategies. In healthcare, this transformation is driving the adoption of innovative solutions to enhance value for stakeholders while supporting more personalised, predictive, and preventive models of care. AI and GenAI are emerging as strategic levers for optimising resource allocation, supporting new care delivery paradigms, and accelerating research and development. The rapid emergence of Agentic AI systems introduces a further step in this transformation, with AI technologies moving from reactive tools towards semi-autonomous systems able to plan, coordinate and monitor actions across complex organizations.

Appel publié par Journal of Enterprise Information Management. Lire l'appel complet sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• How are GenAI and Agentic AI reshaping clinical, administrative, and managerial workflows in healthcare organizations?
• How do healthcare organizations govern Agentic AI systems across care pathways?
• What organizational capabilities are needed to move from experimental GenAI applications to integrated and scalable Agentic healthcare systems?
• How can healthcare organizations ensure meaningful human oversight when AI systems become more autonomous, proactive, and embedded in clinical or administrative processes?
• How do GenAI and agentic AI create, capture, or potentially destroy value for different healthcare stakeholders?
• How do Agentic and GenAI systems transform healthcare knowledge management?
• How can healthcare organizations evaluate and measure the clinical, organizational, economic, ethical, and societal value generated by GenAI and Agentic AI adoption?
• What governance mechanisms are needed to ensure accountability, transparency and regulatory compliance in AI-enabled healthcare organizations?
• How do GenAI and Agentic AI affect decision-making processes within healthcare organizations?
• How can healthcare organizations manage risks related to automation bias, inequitable outcomes and over-reliance on AI?
• How do agentic AI and GenAI support healthcare system sustainability?

Timeline

• January 1, 2027: Opening date for manuscript submissions
• June 30, 2027: Closing date for manuscript submissions

Asha Thomas, Wrocław University of Science and Technology

Moreno Frau, Corvinus University of Budapest

Dominyka Venciūtė, ISM University of Management and Economics

Across contemporary organizations, advances in artificial intelligence (AI) are transforming AI from a discrete technological resource into a systemic organizational capability that actively shapes decision-making, business model innovation, and competitive advantage. Traditionally, AI interfaces have largely been reactive, responding to human prompts and predefined inputs. The emergence of Agentic Artificial Intelligence represents a fundamental shift, as agentic systems are designed to operate with increasing autonomy, enabling goal-driven planning, workflow orchestration, coordination across systems, and machine-initiated action with limited human intervention.

Appel publié par Journal of Enterprise Information Management. Lire l'appel complet sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• How does Agentic AI enable new forms of value creation, capture, and measurement in organizations? Under what conditions can agentic systems also lead to value co-destruction or unintended negative outcomes due to misaligned autonomy, resource integration, or decision logic?
• How do organizations design governance, accountability, trust, and regulatory compliance mechanisms for Agentic AI systems operating with increasing autonomy? What challenges arise when human oversight is limited or distributed across functions?
• How do ethical considerations, responsibility, and moral agency evolve when AI systems act as semi-autonomous organizational actors rather than decision-support tools?
• How does Agentic AI reshape business process redesign, orchestration, and automation across organizational functions such as marketing, human resource management, operations, finance, and customer engagement?
• In what ways is artificial intelligence becoming normalized within organizational and marketing practice, shifting from experimental adoption to routinized, AI-embedded decision-making and workflows?
• How does Agentic AI influence workforce transformation, the future of work, and human resource management practices, including recruitment, performance evaluation, learning, and employee autonomy?
• How does the adoption of Agentic AI differ across organizational contexts, such as small and medium-sized enterprises versus large corporations, and what factors shape successful implementation and impact?
• How do emotional, relational, and interactional dynamics shape human–AI engagement in Agentic AI–driven sales, marketing, and customer experience contexts?
• How can human–AI collaboration and human-in-the-loop design be sustained when AI systems increasingly initiate actions, coordinate tasks, and learn autonomously?
• How do multi-agent systems, coordination mechanisms, and organizational architectures evolve as multiple human and artificial agents interact within complex socio-technical environments?
• How can existing theories of agency, organizational learning, and socio-technical systems be extended or reconfigured to explain machine agency and autonomous action in Agentic AI–enabled organizations?
• How does Agentic AI transform knowledge management, organizational learning, and decision support by enabling systems that not only retrieve and integrate knowledge but also reason, adapt, and act upon it?

Timeline

• March 1, 2026: Opening date for manuscript submissions
• September 30, 2026: Closing date for manuscript submissions

Mark Camilleri, University of Malta

Generative Artificial Intelligence (GenAI) and Agentic Artificial Intelligence (Agentic AI) are transforming how services are designed, delivered, experienced and led. While GenAI refers to systems, such as large language models (LLMs), that produce content in response to human prompts; Agentic AI technologies may be considered as active agents that can implement tasks (rather than merely functioning as passive generators). The latter can monitor situations, allocate resources, initiate and manage processes as well as co-ordinate multiple activities. Hence, Agentic AI algorithms and their governance affect service outcomes.

Appel publié par Journal of Services Marketing. Lire l'appel complet sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Theoretical perspectives on Generative and Agentic AI adoption in service environments
• Comparative or multi-theoretical frameworks for studying human-AI interaction in services
• Anthropomorphism, social presence and human-AI relationships
• Perceived affordances, interface design and service experiences
• Emotions, expectations and psychological responses to AI
• Adoption, acceptance and continued use of AI in services
• Trust, ethics, accountability and relational governance
• AI as a service actor within socio-technical systems
• Contextual and contingency-based perspectives
• Value co-creation, value co-destruction and service outcomes
• Organizational, strategic and policy implications of Generative and Agentic AI in services

Timeline

• June 23, 2026: Opening date for manuscripts submissions
• February 26, 2027: Closing date for manuscripts submission