One of the drivers of America’s literacy crisis is a simple numbers problem.

Across the country, elementary schools employ an estimated 67,000 speech language pathologists — experts on the front lines of diagnosing early language and reading struggles. 

The problem? To meet current demand, schools need 14,000 more of them. 

It’s the same for reading specialists, highly trained educators who have made literacy their professional calling card. There are simply too few teachers out there with that expertise. 

These shortfalls are part of the reason that students’ reading proficiency is worsening. Today, 40% of fourth graders can’t do things like provide support for ideas related to a story’s plot or characters. That’s up from 33% in 2022. A third of eighth graders can’t pick out the main idea of an informational text. 

Ensuring that every child has foundational literacy skills is going to be a Herculean task. Based on my work with researchers and technologists, I see enormous potential in automatic speech recognition as a tool for helping to make up for the shortage of specialists. After listening to children read aloud, this artificial intelligence-powered technology can help assess their progress by identifying weaknesses, provide them with feedback and give teachers and parents recommendations for interventions. The promise of this technology is substantial – if platform developers and their funders can get it right.

This is particularly true in K-2, when students are learning skills that lay the foundation for lifelong reading and success. By assisting speech pathologists and teachers, automatic speech recognition could make sophisticated early literacy screening more accessible and affordable — and it should only get better as AI advances.

Speech recognition technology has a long history, and it has been used to support literacy development in young readers since 1990. That’s when Carnegie Mellon University launched Project Listen, a computer program that displayed stories on a screen and listened to children as they read the text. The program intervened when the student made a mistake, got stuck or asked for help. It also gave teachers feedback and advice on the child’s progress and struggles.

The approach worked. A seven-month study compared the literacy skills of students in grades 1 to 4 who used the program with those of classmates who engaged in sustained silent reading. Those who used the technology-powered intervention, especially in first grade, outpaced their peers in word identification, comprehension, fluency, spelling and understanding of individual sounds.

Some modern versions of automatic speech recognition serve primarily as a screener, like Amplify’s mClass, which provides teachers with immediate feedback on students’ reading fluency and specific areas where they need extra support. Other innovations use the technology to not only assess, but intervene in real time, like Amira’s AI reading tutor. An outgrowth of Project Listen, the tool works in English and Spanish and serves 5 million students in all 50 states. Amira is also collaborating with the Children’s Hospital of Philadelphia and the University of Pennsylvania Data Center to develop an inclusive reading coach for neurodiverse students. 

Automatic speech recognition is particularly strong at helping students decode words, as it improves their ability to match written letters to the spoken sounds they make — a key skill in literacy development.

This spring, Renaissance Philanthropy launched LEVI Literacy with one moonshot goal: develop AI-powered tools that, over the next five years, will reduce the number of struggling young readers by half. 

Short for Learning Engineering Virtual Institute, LEVI provides five years of funding to researchers and developers who share their knowledge and learnings while tackling big challenges. We recently announced the teams selected to work toward the literacy moonshot: Reads Labs at Harvard University, OxEd, LitLab.ai and Project Read with the University of Florida Literacy Institute.

Of course, there are challenges to deploying automatic speech recognition in classrooms if it is to reach its full potential. Developers, companies and researchers need to invest in the high-quality datasets that are needed to train the AI behind these tools. Importantly, these must include a range of children — English learners, special education students, etc. — so the apps and platforms can assist kids with diverse needs. Finally, developers must ground their tools in cognitive science on how the human brain learns, then validate their platforms through rigorous classroom testing.

Automatic speech recognition can never replace a skilled and caring teacher, but it can serve as a patient, non-judgmental expert companion for every educator trying to better understand who is struggling to read, and why. With thoughtfully designed, research-backed screeners and tutors, the nation can have a powerful new tool to tackle its literacy challenge, and give all students more of the personalized reading help they deserve.