Recent findings on the negative impacts of artificial intelligence on learning might be sparking national debate, but they are unsurprising to learning scientists. In fact, these results highlight a long-standing United States trend of using what “feels right” or “sounds good” instead of following well-established education research.
A recent Massachusetts Institute of Technology study is significant.
It shows that students’ over-reliance on technology in general, and AI in particular, bypasses essential learning processes during key stages of childhood and adolescent cognitive development.
Examples of technology interfering with the development of critical thinking skills are abundant. They include replacing handwriting with keyboarding, reducing the importance of students’ automatic recall of foundational knowledge, and providing answers before learners engage in productive struggle. AI should undoubtedly play a role in supporting learning, but it must do so in a way that enhances rather than interferes with core learning science principles.
Responsible and effective AI use requires strong data and oversight. When the technology is grounded in a complete, accurate view of each learner, educators can quickly make instruction more contextually relevant and deliver practice within each student’s zone of proximal development. Used this way, it becomes a tool that deepens thinking, supports personalization, and accelerates meaningful academic growth.
Declining Scores and Global Comparisons
To see the results of a failure to use technology in a way that follows, rather than circumvents, evidence-based guidance, one need only look at students’ declining test scores. Not only are scores lower in absolute terms, but they are also lower relative to Asian and European counterparts who have wisely been managing technology usage, particularly for younger students.
A notable exception to the collective national dissonance with the learning sciences is the widespread adoption of science of reading requirements in more than 40 states since 2019. While this recent success provides a symbol of hope, the full history is complex and leaves uncertainty regarding how schools will respond to AI.
The “Mississippi Miracle” began when the state went from worst in the nation to top 10 in NAEP fourth-grade reading scores in just six years. What is less well-known is that this adoption came 20 years after the National Reading Panel report left little debate about the best way to teach students to read.
Even then, Mississippi’s performance was not enough on its own. A groundswell of outrage from parents based on their firsthand experiences during the pandemic, spurred on by the 2022 podcast “Sold a Story,” led to the near nationwide mandate for evidence-based reading practices.
It remains unclear what spark could ignite a national mandate around AI and learning science. It might be family pushback against the $30 billion market for devices in schools or professional health advisories about AI and adolescent well-being.
To be clear, the Mississippi Miracle was no miracle. It came about through courageous leaders willing to put aside wishful thinking about technology and instead adopt the science—and associated hard work—of making systemic changes to properly teach kids how to read.
Glimmers of this courageousness are shining from organizations that lift up the most essential elements of effective learning, address ethical considerations around AI use, and highlight the complexity of human thought, which integrates emotion, context, nuance, and embodied experience. For instance, the Collaborative for Academic, Social, and Emotional Learning recently dedicated several conference sessions to the connections between social-emotional learning and AI.
At the state and local levels, Mississippi legislators and education leaders performed the boots-on-the-ground work. They changed literacy policies, implemented full strategies, adopted new standards, hired additional literacy coaches, and spent years honing communications and convincing families and educators to give the science-based approach time to demonstrate impact.
The real question now is not what works in education; the science of learning has already answered that. The question is whether there is collective will to ensure AI in schools is guided by that same evidence—and fueled by the kind of complete, high-quality student data that allows it to truly support learning. Strong AI will only come from strong data, grounded in learning science and used with intention. Without it, we risk repeating the very mistakes we are trying to solve.
Leave a Reply