Soon after ChatGPT broke the internet, it sparked an all-too-familiar question for new technologies: What can it do for education? Many feared it would worsen plagiarism and further damage an already decaying humanism in the academy, while others lauded its potential to spark creativity and handle mundane educational tasks.
Of course, ChatGPT is just one of many advances in artificial intelligence that have the capacity to alter pedagogical practices. The allure of AI-powered tools to help individuals maximize their understanding of academic subjects (or more effectively prepare for exams) by offering them the right content, in the right way, at the right time for them has spurred new investments from governments and private philanthropies.
There is reason to be excited about such tools, especially if they can mitigate barriers to a higher quality or life—like reading proficiency disparities by race, which the NAACP has highlighted as a civil rights issue. Yet underlying this excitement is a narrow view of the goals of education. In this framework, learners are individual actors who might acquire new knowledge and skills with the help of technology. The purpose of learning, then, is to master content—often measured through grades and performance on standardized tests.
But is content mastery really the purpose of learning? Naming reading proficiency as a civil rights issue likely has less to do with the value of mastering reading itself, and more to do with the fact that mastery of reading (or math, or other subjects) can help lay a foundation for what learning can unlock: breaking the intergenerational cycle of poverty, promoting greater self-awareness and self-confidence, and cultivating a stronger sense of agency over one’s destiny and the destinies of one’s communities. Content mastery is part of this equation, but making it the primary focus of education misses the fact that so much of a child’s future is shaped by factors beyond the classroom. Critically, networks, or who children and their families are connected to, and how, matter for helping children prepare to live fulfilling lives. This is especially true for networks that cut across socioeconomic, demographic, and other lines. Indeed, a large recent study highlighted how social capital, defined as friendships across socioeconomic divides, can play a larger role in fostering intergenerational economic mobility than school quality (often measured by the test scores of students who go there).
Networks that connect parents to coaches to help them navigate their children’s schooling can forge new support structures and trusting relationships between families and educators. Networks that connect students to role models and mentors can change the course of their academic and professional lives. A child’s broader social context, in addition to the knowledge and skills they gain through school, matters deeply for their future outcomes. Left without intervention, however, real-world networks often form and evolve in inherently unequal ways. For example, patterns of preferential attachment can lead “the rich to get richer,” excluding many from accessing connections that might improve their lives in important ways.
In practice, each AI needs an objective function that represents what it is optimizing for. Applications of AI for pedagogy and content mastery might optimize for “helping students get the highest possible score on a test.” Fostering more inclusive network connections, however, is a more deeply rooted and structural type of change than improving test scores. Using AI to help cultivate these networks might do more for children’s life outcomes than focusing on pedagogy and content mastery alone.