Submission

Up to 4 pages (excluding references), describing the theoretical background, objectives, methodology, findings (if applicable), and implications for AI and data science education.

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

Abstracts are reviewed by the Scientific Committee and published in the symposium proceedings.

Any teaching or learning artefact related to your abstract, e.g. interactive apps, notebooks, visualisations, simulations, or teaching modules.

Upload it as a ZIP file together with your abstract on OpenReview, and describe it briefly in the abstract.

Details on formats, hosting, and licensing: see the OER initiative below.

We welcome contributions from disciplinary and interdisciplinary perspectives, including mathematics, computer science, statistics, and data science education, the learning sciences, media education, ethics, and related fields.

Contributions may report ongoing or completed research, projects, conceptual analyses, or well-founded practice examples, by individual authors or author teams.

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

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

As a rule of thumb, anything that can be displayed in a web browser is suitable. Formats requiring additional software (e.g. Jupyter or R notebooks) are explicitly welcome too.

A ZIP file containing your resource in any format. Ideally, include:

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

If you attach OER material, your extended abstract should briefly outline:

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

We host all submitted materials on the symposium platform, whatever their format:

• During review: accessible to reviewers
• Before the symposium: available to all participants
• After the symposium: openly accessible

Materials that are already publicly available elsewhere, e.g. in a repository or as supplementary material to another publication, are perfectly fine.