The 2nd International Workshop on Artificial Intelligence with and for Learning Sciences

Event Dates

Dec 10, 2025 - Dec 12, 2025

Location

CAGLIARI

Submission Deadline

Sep 18, 2025

Introduction

The 2nd Workshop on Artificial Intelligence with and for Learning Sciences: Past, Present, and Future Horizons (WAILS 2025) is a high-quality forum focused on achievements, challenges, solutions, and future perspectives concerned with the adoption of artificial intelligence methods and techniques in the context of learning sciences. Building on its successful inaugural edition, WAILS 2025 brings together researchers and practitioners working within academia, industry, and government, and coming from different fields such as computer science, education, cognitive science, economics, psychology, sociology, and human-computer interaction, with the goal of fostering a fruitful dialogue across disciplines. The accepted papers will be published by Springer in an LNCS post-workshop proceedings volume and submitted for indexing to DBLP, Google Scholar, and Scopus.

Deadlines

Paper submission: September 18, 2025

Notification: October 23, 2025

Camera-ready submission: November 6, 2025

Note: all the deadlines are at 11:59 pm Anywhere on Earth (AoE) time.

Topics

WAILS 2025 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:

Theoretical and methodological foundations

AI models informed by learning theories, pedagogy, or cognitive science

Educational and psychological theories guiding AI design and evaluation

Frameworks linking AI research with educational and social science

Responsible and transparent data practices in educational contexts

Emerging paradigms and novel perspectives on the role of AI in learning

Design and engineering of educational artificial intelligence

Human-centred and participatory design of intelligent learning systems

Co-design approaches involving educators, learners, and institutional actors

Software engineering for reliable, inclusive, and ethical educational AI

Accessibility, interoperability, and robustness in AI-powered learning tools

Functional and non-functional requirements in educational technology design

Approaches to designing educational modules or programs that promote AI literacy

Empirical studies and evaluation

Longitudinal, qualitative, and mixed-method research on AI in education

In-situ evaluation of AI systems across formal, informal, or hybrid learning

User studies involving students, teachers, parents, and administrators

Measurement of learning outcomes, engagement, and behavioral impact

Studies assessing the effectiveness, transferability, and impact of AI literacy programs

Socio-technical and ethical perspectives

Digital divide concerning AI between instructors and students

Explainability, transparency, and trust in educational AI

Fairness, bias mitigation, and inclusive design in AI applications

Privacy, data governance, and ethical data use in learning systems

Psychological and sociological effects of AI-mediated learning

Instructors’ engagement for the responsible usage of AI tools

Legal, policy, and institutional dimensions of AI adoption in education

Applications and emerging technologies

Intelligent tutoring, coaching, and adaptive learning systems

Educational recommender systems and personalised learning pathways

Learning analytics for formative assessment and educational decision-making

Generative AI and large language models in educational contexts

AI applications for accessibility, multilingualism, and global education equity

Submission

Platform

Papers must be written in English and submitted electronically in PDF format via the CMT submission system. To submit, please visit the submission platform at https://cmt3.research.microsoft.com/WAILS2025/ and select the “WAILS 2025 Full and Short Papers” track.

Format

We encourage two types of submissions (reviewers will comment on whether the size is appropriate), in the Springer single-column format.

Full papers (12 to 15 pages at most; references, figures, tables, proofs, appendixes, acknowledgements, and any other content count toward the page limit) should report on substantial contributions. They should reflect innovations and have a thorough discussion of related work.

Short papers (6 to 11 pages at most; references, figures, tables, proofs, appendixes, acknowledgments, and any other content count toward the page limit) discuss exciting new work that is not yet mature enough for a full paper – they report on a smaller or simpler-to-describe research work on some advances that can be described, set into context, and evaluated concisely.

Templates

The authors should submit manuscripts for review in the Springer single-column format. Templates for authors are given below:

Latex

Overleaf

Word

Non-anonymity

Submissions will be reviewed under a single-blind process; thus, authors are not required to anonymise their manuscripts before uploading them.

Non-dual policy

There is no dual submission policy, which is why submitted manuscripts must not be simultaneously under review or submitted for review elsewhere whilst under consideration for this workshop. A violation of this concurrent submission policy will be deemed a serious infraction of scientific ethics, and appropriate measures will be implemented in response.

Ethical and human subjects considerations

Submissions are expected to include a discussion of ethical considerations, as well as the impact of the presented work and/or its intended application, where appropriate. The authors are expected to comply with ethical standards and regulatory guidelines associated with human subjects research, including research involving human participants and research using personally identifiable data. Submissions reporting on such human subjects research must include a statement identifying any regulatory review the research is subject to or explaining the lack of required review.