Can Small Language Models Help K–12 Schools?

What Are Small Language Models (SLMs)?

“SLMs are compact, efficient AI models designed to understand and generate human language and visual content. They can perform the same tasks as LLMs with a fraction of the computing requirements and cost,” says Julien Simon, chief evangelist at Arcee AI, an AI company based in San Francisco. Simon previously worked as global technical evangelist for AI and machine learning at Amazon Web Services.

Of course, AI is never truly small, says Tushar Katarki, senior director of product management at Red Hat. “‘Small’ is relative, but the term generally refers to models with a few million parameters to an upper limit of around 20 billion parameters. The parameters represent the internal variables of a model that are adjusted or tuned during the training phase and also serve to represent the quantity of data that the model has learned.”

According to Pew Research Center, only 6% of K–12 educators say AI does more good than harm. High school teachers are more likely to have negative views on AI, with some pointing to concerns about cheating and student safety, but SLMs could provide a safer and more tailored approach to AI than LLMs.

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Small Language Models vs. Large Language Models in K–12 Education

Compared to SLMs, LLMs can have up to a trillion parameters and vast knowledge of many different topics. “Their size enables them to understand queries over a wide range of topics,” Simon says. “LLMs are trained on extensive public data sets to emulate broad human knowledge. However, they’re often referred to as ‘a mile wide and an inch deep,’ as their knowledge of very specific issues can be quite shallow.”

For educators looking for deep dives into the efficacy of their teaching and learning practices, student data, or other aspects of education, that might not cut it.

LLMs are like a Jeopardy contestant who knows a bit about everything, Katarki adds, while SLMs are like a professor with deep knowledge of a specific subject. He explains that SLMs might power interactive educational chatbots, engaging students in dialogue simulations and Q&As and even simulating conversations with historical figures.

Benefits of Small Language Models for Schools

Cost Efficiency

Thinking small when it comes to AI can be a benefit, as it also means saving money and producing more efficient, tailored results for a district’s needs. “SLMs require fewer computational resources, making them a cost-effective solution for schools with limited budgets. They can run nicely on standard school PCs without the need for expensive GPUs,” Simon says.

Personalization

Adjusting an SLM is much easier than adjusting an LLM, which can feel like editing the whole internet. “The model doesn’t know the specific ins and outs of that organization; it needs to be fine-tuned on internal data sets that aren’t publicly available,” Katarki says. “This can be very expensive if using an LLM, not only due to accelerated compute architecture, like GPUs, but also because of the power consumption and the team skills LLMs require.”

Ethics and Safety

SLMs are less prone to bias, Simon says, making them safer for use with students. “LLMs can inherit language biases and factual inaccuracies from their massive training data sets. They cannot be efficiently further trained or realigned, whereas alignment for tone of voice or safety guidelines is rather straightforward for SLMs.”

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More Opportunities

Katarki gives a few examples of how SLMs might help a K–12 school. “A properly tuned SLM could augment a districtwide IT team, with the SLM handling lower-level maintenance and monitoring tasks while the team focuses on new student services,” he says. An SLM could also flag educators when patterns emerge in a student’s online coursework, such as moving through the material too quickly. They might not be absorbing the material and could be at risk of falling behind.

Drawbacks of Small Language Models

There are a few drawbacks to SLMs, including initial startup costs and necessary training resources. “They’re less expensive and time-consuming to customize than an LLM, but they still require more resources, at least right now, than traditional applications,” Katarki says.

K–12 tech leaders might notice SLMs underperforming on complex reasoning tasks. However, as DeepSeek recently demonstrated, “they’re making rapid progress,” Simon notes.

Experts say that AI is built to last and here to stay, leaving schools to determine which tools will best serve students. As IT leaders consider the top AI trends, SLMs for K–12 could certainly make the list.