[CFP] WAILS2026: Workshop on Artificial Intelligence with and for Learning Sciences
Francesca Malloci
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Please send to interested colleagues and students.
Introduction
WAILS 2026 is the 3rd edition of the Workshop on Artificial Intelligence with and for Learning Sciences. Building on two successful editions (Salerno 2024, Cagliari 2025), this year focuses on the responsible, ethical, inclusive, and equitable use of AI in educational contexts. If AI makes learning easier, are we still learning or merely optimizing task completion? AI systems can support reasoning, creativity, and decision-making. Yet they can also obscure understanding, reinforce bias, and reduce cognitive effort. WAILS 2026 is grounded in a simple premise:
AI should not replace thinking, it should make thinking more visible, critical, and meaningful.
We invite the community to critically examine and shape the role of AI in education through evidence, design, and theory. The accepted papers are going to be published by Springer into an LNCS post-workshop proceedings volume, and submitted for indexing to DBLP, Google Scholar, and Scopus.
Location
Sorbonne Université - Campus Pierre et Marie Curie
Paris (France)
Important Dates
Paper Submission: July 03, 2026
Notification: July 31, 2026
Camera-ready Submission: August 7, 2026
Conference: December 9-11, 2026
Note: the submissions deadlines are at 11:59 pm AoE (Anywhere on Earth) time.
Topics
WAILS 2026 welcomes contributions from a wide range of disciplines, including computer science, education, cognitive science, psychology, sociology, ethics, economics, and human-computer interaction. Topics of interest include, but are not limited to:
Responsible AI in Education
• Transparency, explainability, and accountability in AI-powered learning systems
• Communicating AI limitations (e.g., hallucinations, uncertainty) to users
• Large language models in educational contexts
• Privacy, data protection, and governance of student data
• Learning analytics and educational data mining
• Ethical frameworks and governance for AI in education
• AI policy and regulation in educational institutions
• Designing systems that make risks visible, interpretable, and actionable
Equity, Fairness & Inclusion
• Bias detection, measurement, and mitigation in educational AI
• Inclusive and accessible AI-supported learning environments
• Addressing inequalities in access, representation, and AI literacy
• AI for supporting diverse and underserved learners
• Sociological and cultural factors in AI adoption across educational contexts
• Digital divide and unequal access to AI-powered tools
• Gender, race, and disability bias in AI-powered learning systems
• Community-based and participatory approaches to equitable AI design
Human-Centered AI
• Learner agency vs. over-reliance and automation bias
• Cognitive impacts of AI use (e.g., shallow learning, reduced effort, illusion of understanding)
• Intelligent tutoring systems and conversational agents
• Evolving teacher roles: augmentation, adaptation, or deskilling
• Designing for reflection, reasoning, and metacognition
• Cognitive science and psychological dimensions of AI-supported learning
• Student wellbeing and mental health in AI-mediated learning
• Adaptive and personalized learning environments
Serious Games & Inclusive Learning
• AI-enhanced serious games for education and training
• Game-based approaches for AI literacy and critical thinking
• Serious games for social-emotional learning and inclusion
• Participatory design of serious games with marginalized communities
• Narrative and storytelling in AI-driven educational games
• Emotional and motivational dimensions of game-based learning
• AI-generated content in serious games: opportunities and risks
• Accessibility and universal design in serious games
• Transfer of learning from game-based to real-world contexts
Interdisciplinary & Social Perspectives
• Sociological and psychological dimensions of AI in education
• Cognitive science approaches to AI-supported learning
• Economic and policy implications of AI adoption in education
• Cultural and contextual factors in AI-enhanced learning environments
• AI and the future of educational institutions
• Philosophical and ethical dimensions of AI in education
• Cross-cultural perspectives on AI-supported learning
• AI and educational equity from a policy perspective
Teaching AI
• Teaching AI literacy across formal and informal learning contexts
• Curriculum design for responsible AI education
• Teaching AI concepts across disciplines and age groups
• Pedagogical approaches to explaining AI limitations and risks
• Teacher training and professional development for AI-integrated classrooms
• Assessing student understanding of AI systems and their societal impact
• Evaluating the effectiveness of AI education programs and interventions
• Student and teacher perceptions of AI: trust, fear, and agency
Submission
Platform
Papers must be written in English and submitted electronically in a PDF format, through the CMT submission system, by selecting the type “Full Paper/Short Paper” or “Doctoral Consortium” based on their length. The link to the submission system will be available soon.
Format
We encourage three types of submissions (reviewers will comment on whether the size is appropriate for each contribution), in the Springer single-column format.
Fullpapers (11 to 15 pages at most; references, figures, tables, proofs, appendixes, acknowledgments, and any other content count toward the page limit) should report on substantial contributions of lasting value. They should reflect innovations or studies and have a more thorough discussion of related work.
Short papers (6 to 10 pages at most; references, figures, tables, proofs, appendixes, acknowledgments, and any other content count toward the page limit) typically discuss exciting new work that is not yet mature enough for a long paper – they are complete reports on a smaller or simpler-to-describe but complete research work on advances that can be described, set into context, and evaluated concisely. Note that novel but significant proposals will be considered for acceptance despite not having gone through sufficient experimental validation.
Doctoral Consortium (6 pages at most; references, figures, tables, proofs, appendixes, acknowledgments, and any other content count toward the page limit) invites doctoral students to present their research to experienced scholars who will provide constructive feedback and guidance. Students are encouraged to participate if they are at least six months from completing their dissertation and have identified a research area or dissertation topic. Submissions should consist of a concise paper outlining the candidate’s doctoral research. Papers are expected to clearly describe the research problem and motivation, situate the work within relevant literature, and present the proposed approach or methodology.