Why AI Prefers German Over Math: The Surprising Edge of World Languages in Ed Tech
This twist of fate stems largely from NLP-trained AI’s bread and butter: language processing.
World languages can often feel like the overlooked and underprioritized stepchild of education, lounging in the corner while the core subjects like English language arts (ELA) and Math get all the funding and limelight. But guess what? Where the rapid integration of generative AI tools promises to redefine learning paradigms, World Languages presents a compelling case for prioritization over the traditional core subjects.
AI systems trained in Natural Language Processing (NLP) may be better positioned to handle world language assessments out of the gate — more so than they could with math or ELA. This twist of fate stems largely from NLP trained AI’s bread and butter: language processing. Here’s a closer look at why this might be the case.
In the context of educational and assessment frameworks, ability in learning languages is measured by how well you can communicate in real-world scenarios. You’ve got levels like novice, where you can barely ask where the bathroom is, to advanced, where you’re debating philosophy in Mandarin. This progression is not tied to age or grade level like in ELA and math, where you’re expected to master increasingly abstract concepts year after year. That is not to say that standardization doesn’t exist in World Languages; we’ve got our frameworks like the Common European Framework of Reference for Languages (CEFR) and the American Council on the Teaching of Foreign Languages (ACTFL) proficiency guidelines. It’s just that our frameworks describe what learners can do with the language in terms of language proficiency levels.
Now, while ELA and math are trapped in this grade-centric paradigm, language learning is all about functional ability. We don’t really care how old you are or what grade you are in. We want to know if you can order tapas in Spain without accidentally insulting restaurant staff.
So, why are NLP-trained AIs likely to be superstars in world language assessment? Essentially, it’s because they’re already trained on linguistic data available online — — eagerly waiting to assess language proficiency like a pro. Plus, NLP-trained AIs are really good at picking apart what you mean and how you say it, which is exactly what you need to figure out someone’s language proficiency. This makes NLP-based AI systems naturally suited for tasks involving language comprehension and production, which are central to language proficiency assessments.
While ELA also involves language use, proficiency assessments in ELA can require more nuanced understanding of complex literary concepts, critical thinking, and analytical skills that go beyond basic language processing. To really grade this stuff, NLP systems have to cram like a college student before finals, soaking up all sorts of academic and literary datasets and training content for an isolated task.
Then there’s math. Oh, math. Mathematics assessments often involve symbolic reasoning, problem-solving skills, and understanding of abstract concepts that have little to do with everyday chit-chat. NLP is less directly applicable to mathematical language and requires integration with other AI technologies, such as symbolic AI, to effectively process and evaluate mathematical content.
Bottom line? When we’re thinking about where to research and fund innovative AI initiatives, World Languages might just have the edge, since most of what we do aligns perfectly with what NLP does best. For once, it’s not so bad being the underdog. The core subjects can keep their traditional glory; we’ve got the future on our side. Now, isn’t that a refreshing twist?
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