Expert Urges Prioritizing Child Protection in Educational AI

A subject matter expert on human trafficking and child labor exploitation at the U.S. Department of Education has issued a strong warning regarding the rapid integration of artificial intelligence tools into educational settings, advocating for child protection to be prioritized over industry interests. The expert draws a parallel to the widespread adoption of cellphones in classrooms over 15 years, noting that societal protections for children lagged significantly behind the technology's proliferation, leading to a generation inundated by screens without adequate safeguards. Similarly, the early development of the commercial internet saw rules established to protect platform privacy and profitability, with child-specific protections like parental consent for data collection implemented much later, proving insufficient to rectify the initial ceded ground.

The expert recounts observing systems designed to protect children instead shielding individuals causing harm, a failure that underscored the permanent forfeiture of protections neglected at the outset. This historical pattern is now repeating with artificial intelligence companies prioritizing their own interests through the establishment of enforceable rules. The concern is that industry-backed precedents will hinder the subsequent implementation of robust child protections. Therefore, the expert asserts that protections for children must be established first in the current AI landscape.

A key proposal is that no AI tool intended for teaching, assessing, or tracking children should be eligible for a school district contract without first passing a bias test. This test, according to the expert, must be specifically designed around the student populations that have historically been harmed by digital tools. The urgency of this measure is highlighted by recent findings on AI bias in education. A study conducted last year on AI assistants used by teachers revealed that these tools recommended more severe approaches for struggling students with names perceived as Black. Furthermore, a separate investigation found that an AI grading system assigned lower scores to essays written by Black students compared to those by Asian students, replicating existing disparities in human grading.

These instances of bias demonstrate that AI systems can perpetuate and even amplify existing societal inequities within the educational environment. The expert emphasizes that such biases do not need to reach children, implying that proactive measures are essential to prevent their dissemination and impact. The call for mandatory bias testing before contract approval aims to create a critical safeguard, ensuring that AI technologies deployed in schools are equitable and do not further disadvantage vulnerable student groups. This proactive stance is presented as a necessary departure from past reactive approaches to technology adoption in education, where the well-being of children was often an afterthought.