How AI Summarization Can Make Learning Languages Easier

When it comes to learning about different parts of the world, one of the largest hurdles is gaining familiarity with different languages. Compared to math, history, and other subjects, mastering new languages can be particularly frustrating due to its reliance on the learner’s individual proactiveness. Most likely, even after carefully paying attention in an introductory course, there might still be a handful of beginner sentences that seem almost incomprehensible due to their use of unfamiliar vocabulary or obscure grammatical structures. It’s often up to the learner to go beyond the standard curriculum in order to truly pick up the language, a task which may seem daunting when done alone.

Fortunately, with the advent of generative AI tools like ChatGPT or Google’s Bard, there are many new tools available to help with this task — particularly AI summarization. Now more than ever, generative AI tools are utilizing what is called a “transformer” architecture. This state-of-the-art AI model enables tools like ChatGPT to understand relationships between words as well as to remember and utilize information from all over the piece of text. These traits, which previous iterations have struggled with, allow the AI to be highly adept at summarizing large bodies of text.

Content Summarization

The first thing that comes to mind with AI summarization — and summarization in general — is the distilling and condensing of information to help with comprehension. As is the case with most subjects, this can be extremely useful when learning new linguistic concepts or clearing up misconceptions about a language. Admittedly, topics like “future tense” or “direct objects” may have fairly short and straightforward explanations that don’t need much summarization. But depending on the language, it’s possible one might quickly run into more confusing topics like the “subjunctive” or “sequence of tenses,” the basics of which might already require multiple paragraphs to explain; many simply can’t afford to trudge through large blocks of dense text, spending valuable time deciphering technical linguistic jargon just to understand a single concept.

This is where contemporary AI summarization can help. Using prompts with the word “summarize” like the one found here might significantly decrease the time and effort required to learn a given topic. Additionally, users can customize AI responses just by altering their prompts accordingly. For instance, if a response is too technical, simply add “explain like I’m a kindergartener” to ensure a more understandable output. Well-known generative AI interfaces like ChatGPT learn from a large set of existing text scraped from every corner of the internet, so if there are any lingering questions, just add follow-up questions. As long as input prompts are continuously provided, the AI will always have a response. Additionally, unlike language teachers or professors, AI doesn’t have an individual life or personal schedule, so anyone can ask clarifying questions for however long, whenever convenient.

A similar logic can be applied to learning about a language’s respective culture. Regardless of the circumstances around one’s particular language learning pursuit, culture will always be an inevitable component. Virtually every language contains direct links back to its culture, either in food, arts, or dialectical variances. As a result, it’s necessary that students understand the surrounding culture in order to completely grasp a language. By using similar prompts as those described before (e.g., prompts using the term “summarize”), users can effectively instruct generative AI tools to scrape the entire internet for information about a specific tradition or cultural feature and string together a concise yet accurate summary of whatever is in question.

Vocabulary Help

But it doesn't end with content comprehension — summarization tools can help with vocabulary as well. According to 20th-century French-American linguist Paul Pimsleur, one of the most difficult aspects of language learning is mastering vocabulary. At the same time, a solid grasp of vocabulary can often be the most important aspect of learning a new language, even trumping a solid understanding of grammar when it comes to interpersonal communication.

As it turns out, summarization tools like ChatGPT are great at producing customized vocabulary summaries for particular situations. For instance, when the prompt “summarize a list of important Chinese vocabulary terms when dining at a restaurant” was used, the chatbot returned a list of twenty useful, commonly-used words when ordering food in Chinese, as well as an extra sentence stating: “Remember to use polite expressions like ‘请’ (qǐng) meaning ‘please’ when interacting with the restaurant staff.” If these aren’t enough, a follow-up prompt can be added, such as “list out some more terms.” The AI will remember the context in which the question is asked, including the previous prompt and any prompts before that, so it is not necessary to specify information already stated in the original question. The number of terms returned may also seem to arbitrarily vary each time, but this fluctuation can be suppressed by just specifying a specific number of vocabulary terms. And remember: more terms can always be added through follow-up prompts.

Of course, to truly take advantage of the capabilities of this mechanism, one can base the vocabulary summary around a particular body of text instead of the entire internet, which the AI will default to using. When given a body of text as well as the prompt “summarize a list of Chinese vocabulary terms from the following paragraph,” ChatGPT outputs an exhaustive list of vocabulary terms (as well as their translations in English) composed of the most useful phrases appearing in the given piece of text. Through this method, users can quickly parse through large bodies of unfamiliar vocabulary, eliminating the need to consult a dictionary and condensing what might’ve taken hours to a couple of seconds.

Practice!

Reading up on grammar and vocabulary is extremely important to language learning. After all, one’s ability to speak, read, write, and understand a language hinges on a solid grasp of these two pillars. Nevertheless, many studies have shown that grammar and vocabulary can be meaningless in isolation. Practical experience with the language can both help to clear up misunderstandings and improve one’s ability to remember facts and information. What’s ultimately needed to become truly proficient in a language is practice, something that AI summarization tools can once again aid in providing.

One of the main capabilities generative AI like ChatGPT is known for is the ability to formulate blocks of text that fall under certain user-provided constraints. This capacity can easily be applied in the context of language practice. Using the prompt “generate a X in language Y” (see example here) will instruct the AI model to generate unique sentences and paragraphs from any language sufficiently documented on the internet, effectively transforming the chatbot into a fast, customizable practice problem generator that can be conveniently used by both students seeking for more practice and teachers seeking to make more exercises.

These prompts can even be adjusted to the learner’s needs. For instance, if someone is learning about the passive voice, they can explicitly ask the AI to emphasize the passive voice in the prompt. One can also supply a list of vocabulary words from which to choose when generating the piece of text in order to simulate the experience of reading through textbook exercises in a controlled environment. In case the resulting output still contains unfamiliar words, the user can simply use the methods described above to generate vocabulary summaries that could both help with comprehending the text and serve as an additional study resource.

So when does summarization play a part? Well, once the foreign language text is produced, the user can then utilize AI summarization to create an English summary of the text, facilitating the process of checking their comprehension. This process isn’t limited to AI-generated texts, however. Textbook readings can be treated similarly: just copy-and-paste the desired piece of text into the prompt (e.g., “Generate an English summary of the following text: [copy-pasted text]”) and follow through with the steps prescribed above. If the textbook readings are in a physical textbook, OCR (Optical Character Recognition) software can be used to transcribe written text into text on one’s computer. There are plenty of freely available OCR tools on the internet with directions on how to use them. The entire process may be facilitated with the advent of ChatGPT 4 (which incorporates a built-in visual input mechanism) and other future generative AI tools that sport similar functionalities.

Conclusion

With the massive amount of information fed into modern translation models as well as the state-of-the-art transformer architecture, AI tools have never been more fit to aid in learning new languages. They can function as all-purpose summarizers that can be customized by the user to aid in whatever task is necessary, whether it be reading summaries on grammar and culture, aiding in vocabulary help, or producing self-checkable practice for reading comprehension. That said, while current AI models are definitely useful for this particular application, they are still limited when it comes to interpersonal communication. To ensure a well-rounded learning experience, AI summarization models should ultimately be used in conjunction with more traditional approaches, such as engaging with fluent speakers in conversation or immersing oneself in the language and its culture. But when it comes to tools that can facilitate the process, generative AI is definitely one of the best.