An XAI Social Media Platform for Teaching K-12 Students AI-Driven Profiling, Clustering, and Engagement-Based Recommending

Source publication / research team or educational organization described in paper

Finland

Researchers, educators, instructors, or facilitators as described in the source publication

• In-school (K-12)

Tool / platform-supported learning activity

Not specified in extracted text

An XAI Social Media Platform for Teaching K-12 Students AI-Driven Profiling, Clustering, and Engagement-Based Recommending Nicolas Pope1, Juho Kahila2, Henriikka Vartiainen2,

Explainable AI / robustness, Ethics / responsible AI

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

K-12

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• This paper presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for students aged 11-16.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

Explainable AI / robustness, Ethics / responsible AI

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Learning tool / resource design, Ethics / responsible AI education.
• AI capability focus: Explainable AI / robustness, Ethics / responsible AI.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Hands-on / experiential learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published empirical study.
• Pedagogical pattern: Hands-on / experiential learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• An Instagram-like interface and a monitoring tool for explaining the data-driven processes make these complex ideas accessible and engaging for young learners.

• An Instagram-like interface and a monitoring tool for explaining the data-driven processes make these complex ideas accessible and engaging for young learners.
• The tool provides hands-on experiments and real-time visualizations, illustrating how user actions influence their personal expe- rience on the platform as well as the experience of others.
• This approach seeks to enhance learners’ data agency, AI lit- eracy, and sensitivity to AI ethics.

This paper presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for students aged 11-16. The tool was designed for interventions on the fun- damental processes behind social media platforms, focusing on four AI- and data-driven core concepts: data collection, user profiling, engagement metrics, and recommendation al- gorithms.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Pre/post or experimental evidence
• Learning analytics
• Activity documentation

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• Conceptual understanding
• Engagement / motivation
• Ethics and responsible use
• Learning tool / resource design
• Ethics / responsible AI education
• Explainable AI / robustness
• Ethics / responsible AI

• Completed

K-12

In-school (K-12)

Explainable AI / robustness, Ethics / responsible AI

Hands-on / experiential learning

Medium

High

An XAI Social Media Platform for Teaching K-12 Students AI-Driven Profiling, Clustering, and Engagement-Based Recommending

• Nicolas Pope
• Juho Kahila
• Henriikka Vartiainen
• Mohammed Saqr
• Sonsoles López-Pernas
• Teemu Roos
• Jari Laru
• Matti Tedre

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35194

https://ojs.aaai.org/index.php/AAAI/article/view/35194

https://ojs.aaai.org/index.php/AAAI/article/view/35194/37349

031_An XAI Social Media Platform for Teaching K-12 Students AI-Driven Profiling, Clustering, and Engagement-Based Recommending.pdf

9

This paper presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for students aged 11-16. The tool was designed for interventions on the fun- damental processes behind social media platforms, focusing on four AI- and data-driven core concepts: data collection, user profiling, engagement metrics, and recommendation al- gorithms. An Instagram-like interface and a monitoring tool for explaining the data-driven processes make these complex ideas accessible and engaging for young learners. The tool provides hands-on experiments and real-time visualizations, illustrating how user actions influence their personal expe- rience on the platform as well as the experience of others. This approach seeks to enhance learners’ data agency, AI lit- eracy, and sensitivity to AI ethics. The paper includes a case example from 12 two-hour test sessions involving 209 chil- dren, using learning analytics to demonstrate how they navi- gated their social media feeds and the browsing patterns that emerged. App — https://somekone.gen-ai.fi Code — https://github.com/knicos/genai-somekone

• In-school (K-12)

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.