Mar 10, 2025

Morph: AI Safety Education Adaptable to (Almost) Anyone

Shafira Noh, Wan Aimran

🏆 Education Track Prize

Details

Details

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Summary

One-liner: Morph is the ultimate operation stack for AI safety education—combining dynamic localization, policy simulations, and ecosystem tools to turn abstract risks into actionable, culturally relevant solutions for learners worldwide.

AI safety education struggles with cultural homogeneity, abstract technical content, and unclear learning and post-learning pathways, alienating global audiences. We address these gaps with an integrated platform combining culturally adaptive content (e.g. policy simulations), learning + career pathway mapper, and tools ecosystem to democratize AI safety education.

Our MVP features a dynamic localization that tailors case studies, risk scenarios, and policy examples to users’ cultural and regional contexts (e.g., healthcare AI governance in Southeast Asia vs. the EU). This engine adjusts references, and frameworks to align with local values. We integrate transformer-based localization, causal inference for policy outcomes, and graph-based matching, providing a scalable framework for inclusive AI safety education. This approach bridges theory and practice, ensuring solutions reflect the diversity of societies they aim to protect. In future works, we map out the partnership we’re currently establishing to use Morph beyond this hackathon.

Cite this work:

@misc {

title={

Morph: AI Safety Education Adaptable to (Almost) Anyone

},

author={

Shafira Noh, Wan Aimran

},

date={

3/10/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Reviewer's Comments

Reviewer's Comments

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.