Submission

An extended abstract of up to 4 pages (excluding references) describing the contribution, including its theoretical background, objectives, methodology, findings (if applicable), and implications for AI and data science education.

On OpenReview, please provide a short summary as plain text and upload your abstract as DOCX and PDF using the provided template.

The abstract will be reviewed by the Scientific Committee and included in the symposium proceedings.

Learning material or resources shared as Open Educational Resources: any form of teaching or learning artefact for AI and data science education — e.g. interactive apps, demonstrations, notebooks, visualisations, simulations, or teaching modules.

Upload a ZIP file containing your resource in any format together with your abstract on OpenReview.

The materials will be made available through a dedicated symposium platform — see the OER initiative below for details.

We welcome submissions contributing to the teaching and learning of AI and data science from disciplinary and interdisciplinary perspectives, including mathematics education, computer science education, statistics education, data science education, learning sciences, media education, ethics, and related fields.

Contributions may report ongoing or completed research, projects, conceptual analyses, or well-founded practice examples. Each participant — possibly as part of an author team — should submit an extended abstract and, where applicable, the corresponding learning material or resources.

The use of AI technologies in an educational context is not the focus of the symposium.

Topics include, but are not limited to:

• Data and problems in AI and data science education
• Tools and infrastructures for teaching and learning
• Explanatory and epistemic models
• Learning materials and pedagogical frameworks
• AI and data science competencies and assessment
• Curricula and implementation in schools and teacher education
• AI and data science education for social good

A ZIP file containing your educational resource in any format, uploaded together with your abstract on OpenReview. Ideally, include:

• a README file with instructions on how to access, display, and use the resource,
• a LICENSE file specifying the terms of use, e.g. Creative Commons or open source licenses.

Examples include, but are not limited to:

• Lesson plans and teaching units
• Classroom activities and worksheets
• Assessment tasks and rubrics
• Data sets and accompanying tasks
• Programming notebooks and code examples
• Interactive learning environments
• Web apps and explorables
• Teacher education materials
• Other resources supporting AI and data science learning

As a rule of thumb, any file, tool, or app that can be displayed in a web browser is a suitable submission. Formats requiring additional software (e.g. Jupyter or R Notebooks) are also explicitly invited.

If you attach OER material, your extended abstract should contain a short description of the submitted materials or application, outlining:

• the educational context and target audience,
• learning goals and intended competencies,
• how the materials can be used or adapted,
• licensing information.

We will establish a web platform hosting all submitted materials — and figure out how to host them, no matter what the exact format is:

• Review phase: accessible to reviewers
• Leading up to the symposium: available to all participants
• After the symposium: openly accessible

It is perfectly acceptable — and probably often the case — that submitted materials are already publicly available elsewhere, e.g. in another repository or as supplementary material for another publication.