Pandemic, Politics, Pre-K & More: 12 Charts That Defined Education in 2024

The 74

As 2024 reaches its end, it’s a good time to ask what’s coming next for K–12 education.

Nearly five years after the emergence of COVID, the pandemic’s after-effects still ripple through schools and communities, with student learning persistently failing to reach levels seen in 2019. Just under $200 billion in federal assistance to states, which was used to keep districts afloat during the crisis, expired in September — with no further help visible on the horizon.

Increasingly, though, the kids filling American schools have only dim memories of quarantines or virtual instruction. Their experience is instead defined by a rash of trends and technologies that sprang up, or became much more common, during the period when schooling was scrambled: a massive build-out of tutoring programs; the rapid adoption of artificial intelligence as a tool of both academic achievement and academic dishonesty; a rise in student despair and anxiety, which some experts attribute to the spread of smartphones; and, for adolescents, soaring recreational marijuana use under newly permissive state laws.

...

AI Could Get the Most out of Tutors

Tutoring programs exploded in the last five years as states and school districts searched for ways to counter plummeting achievement during COVID. But the cost of providing supplemental instruction to tens of millions of students can be eye-watering, even as the results seem to taper off as programs serve more students.  

That’s where artificial intelligence could prove a decisive advantage. A report circulated in October by the National Student Support Accelerator found that an AI-powered tutoring assistant significantly improved the performance of hundreds of tutors by prompting them with new ways to explain concepts to students. With the help of the tool, dubbed Tutor CoPilot, students assigned to the weakest tutors began posting academic results nearly equal to those assigned to the strongest. And the cost to run the program was just $20 per pupil. 

The paper suggests that tutoring initiatives may successfully adapt to the challenges of cost and scale. Another hopeful piece of evidence appeared this spring, when Stanford University researchers found that a “small burst” program in Florida produced meaningful literacy gains for young learners through micro-interactions lasting just 5–7 minutes at a time. If the success of such models can be replicated, there’s a chance that the benefits of tutoring could be enjoyed by millions more students.

...

Click here to read full article
News

Mentioned Publication

Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise

 

Generative AI, particularly Language Models (LMs), has the potential to transform real-world domains with societal impact, particularly where access to experts is limited. For example, in education, training novice educators with expert guidance is important for effectiveness but expensive, creating significant barriers to improving education quality at scale. This challenge disproportionately hurts students from under-served communities, who stand to gain the most from high-quality education and are most likely to be taught by inexperienced educators. We introduce Tutor CoPilot, a novel Human-AI approach that leverages a model of expert thinking to provide expert-like guidance to tutors as they tutor. This study presents the first randomized controlled trial of a Human-AI system in live tutoring, involving 900 tutors and 1,800 K-12 students from historically under-served communities. Following a preregistered analysis plan, we find that students working on mathematics with tutors randomly assigned to have access to Tutor CoPilot are 4 percentage points (p.p.) more likely to master topics (p<0.01). Notably, students of lower-rated tutors experienced the greatest benefit, improving mastery by 9 p.p. relative to the control group. We find that Tutor CoPilot costs only $20 per-tutor annually, based on the tutors’ usage during the study. We analyze 550,000+ messages using classifiers to identify pedagogical strategies, and find that tutors with access to Tutor CoPilot are more likely to use strategies that foster student understanding (e.g., asking guiding questions) and less likely to give away the answer to the student, aligning with high-quality teaching practices. Tutor interviews qualitatively highlight how Tutor CoPilot’s guidance helps them to respond to student needs, though tutors flag common issues in Tutor CoPilot, such as generating suggestions that are not grade-level appropriate. Altogether, our study of Tutor CoPilot demonstrates how Human-AI systems can scale expertise in real-world domains, bridge gaps in skills and create a future where high-quality education is accessible to all students.