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Call for Papers – C1: AIED/ITS

ICCE 2026 Subconference on

Artificial Intelligence in Education / Intelligent Tutoring Systems (AIED/ITS) and Adaptive Learning

The ICCE C1 sub-conference on AIED/ITS & Adaptive Learning focuses on research and development of interactive, intelligent, and adaptive learning environments for learners of all ages, across all domains. The sub-conference provides opportunities for the cross-fertilization of knowledge and ideas from researchers in the many fields that constitute this interdisciplinary research area, including artificial intelligence, computer science, cognitive science, education, learning sciences, educational technology, psychology, philosophy, sociology, anthropology, and linguistics. It also encompasses domain-specific areas for which AIED/ITS systems have been designed and built, including ITS for ill-defined domains.

For ICCE 2026, we particularly encourage submissions that engage with the conference theme—Reimagining Learning Ecologies in the Age of Intelligent Technologies—by investigating how rapid advances in generative AI, large language models, agentic AI systems, and multi-agent architectures are transforming the design, deployment, and evaluation of intelligent learning environments. As AI adoption in education has accelerated dramatically—with student AI usage surging from 66% to 92% between 2024 and 2025—the field faces urgent questions about human–AI co-orchestration, responsible integration, equity of access, and the evolving roles of teachers, learners, and AI agents within interconnected learning ecologies. Papers may address the following topics in the context of AIED/ITS systems (but are not limited to):

Foundational AIED/ITS Research

  • Architectures for AIED/ITS (distributed, agent-based, web-based, cloud-native)
  • Knowledge Modelling and Representation
  • Learner and Student Modelling
  • Cognitive Engineering and Cognitive Modelling
  • Instructional Planning, Strategies, and Tutoring Strategies
  • Assessments and Automated Assessment Design
  • Authoring Systems and Authoring Tools
  • Natural Language Processing, Dialogue Systems, and Conversational Tutoring
  • Educational Data Mining and Learning Analytics

Generative AI and Large Language Models in Education

  • LLM-Powered Tutoring, Scaffolding, and Feedback Systems
  • Multi-Agent and Agentic AI Architectures for Education
  • Retrieval-Augmented Generation (RAG) for Educational Content
  • Prompt Engineering and Guardrails for Pedagogical Applications
  • Foundation Models for Student Modelling and Knowledge Tracing
  • Generative AI for Automated Content Creation, Curriculum Design, and Assessment
  • Human–AI Co-Orchestration in Teaching and Learning
  • Hallucination Detection, Mitigation, and Trustworthy AI in Educational Contexts

Adaptive, Personalized, and Intelligent Learning Environments

  • Adaptive Learning Systems and Personalized Learning Paths
  • Context-Aware and Situation-Aware Learning Environments
  • Open-Ended Learning Environments and Ill-Defined Domains
  • Game-Based and Simulation-Based Learning Systems and Pedagogies
  • Virtual, Augmented, and Mixed Reality for Education
  • Intelligent Pedagogical Agents, Virtual Humans, and Learning Companions
  • Multimodal Interaction and Novel Learning Interfaces

Affect, Metacognition, and Learner Agency

  • Affective Modelling and Emotion-Aware Tutoring
  • Self-Regulated Learning, Metacognition, and Learner Agency
  • AI-Supported Motivation, Engagement, and Persistence
  • Cognitive Load Management and Adaptive Scaffolding

Responsible AI, Equity, and Policy

  • Ethical and Responsible Use of AI in Education
  • Fairness, Explainability, and Transparency of AI Systems in Education
  • AI Literacy, Critical AI Competencies, and AI Fluency Across Disciplines
  • AI in Education: Policy, Governance, and Institutional Adoption
  • Privacy, Data Sovereignty, and Learner Data Rights
  • Equity, Access, and Inclusion in AI-Powered Education
  • Academic Integrity in the Age of Generative AI

Emerging Frontiers

  • AI and Workforce Development, Upskilling, and Entrepreneurship Education
  • Ontology, Knowledge Graphs, Linked Open Data, and Semantic Web Technologies for Education
  • Multimodal and Vision–Language Models for STEM Education
  • AI for Low-Resource Languages and Underrepresented Educational Contexts
  • Benchmarking, Evaluation Frameworks, and Reproducibility in AIED Research
  • Teacher–AI Collaboration, Teacher-Facing AI Tools, and Professional Development

We welcome contributions that report accomplished research as well as work in progress. Papers that describe innovative architectures and models, discuss results from AIED/ITS systems in use, and present evaluations of AIED/ITS systems are strongly encouraged. The sub-conference also welcomes research works dealing with innovative ideas (theoretical or conceptual) that have the potential for significant impact on future research in the AIED/ITS field.

PC Executive Chair

  • Xiangen HU, The Hong Kong Polytechnic University, Hong Kong SAR, China

PC Co-chair

  • Jionghao LIN, The University of Hong Kong, Hong Kong SAR, China
  • Yu LU, Beijing Normal University, China
  • Michael KICKMEIER-RUST, St. Gallen University of Teacher Education, Switzerland

PC Members
To be confirmed