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Primary Sponsor

Last Action

Conf Com Appointed2026-06-24

Vote Breakdown

Plain Language Summary

This bill creates protections for children under 16 on social media platforms, requires schools to teach artificial intelligence literacy, and establishes AI policies and training for public schools. The social media portion bans children under 14 from most social platforms and requires parental consent for 14-15 year-olds; it also restricts how platforms can use children's data for recommendations and advertising. The education portions update computer science standards to include AI instruction, create a model AI policy for schools, establish an evaluation framework for AI educational tools, and require teacher training on AI use.

Arguments in Favor

• •Supporters argue this bill protects children from addictive social media features and data exploitation while preparing students for an AI-driven future.
• •They contend that restricting social media access for young teens reduces mental health risks, that limiting data use prevents manipulative algorithmic targeting, and that comprehensive AI literacy and educator training helps schools responsibly integrate AI tools while teaching students to evaluate and use them ethically.
• •Proponents note the bill provides schools with guidance through model policies and evaluation frameworks rather than imposing rigid mandates.

Arguments Against

• •Opponents raise concerns that the social media restrictions may be difficult to enforce against major platforms and could face constitutional challenges on free speech grounds.
• •They worry that age verification requirements—even anonymous ones—could create privacy risks or barriers to access.
• •Some question whether the AI education and training mandates place additional burdens on teachers and schools without sufficient funding or resources, and whether the evaluation framework might slow adoption of beneficial educational AI tools.
• •Critics also note that the bill's definitions of 'addictive platforms' could be interpreted broadly or narrowly depending on implementation.