Artificial intelligence has opened new language acquisition pathways for learners and teachers. These programs allow any learner, anywhere, to receive personalized lesson plans and feedback. When part of a classroom (virtual, physical, or mixed), AI language learning programs help teachers assess student progress.
Reasons for learning a language abound, ranging from a genuine need to desire. Language learners may begin studies on their own or join a class looking for career advancement, preservation of brain health, personal enrichment, and more.
The population that may need AI language learning programs the most, however, are immigrant children. Due to Covid-19, they are missing both in-person instruction and the natural language acquisition they would develop through peer interaction. Virtual learning in general, and AI programs specifically, may be the only things keeping these children engaged in study.
AI essentially works by recording large amounts of individual data points then analyzing them at inhuman speeds. Programs using AI then adjust their behavior or output automatically. In this use case, AI language learning programs use the learner’s behavior to identify what they know and how they learn best. This includes recoding the time they use to recall material, what interests them, and so on. AI programs then adjust lessons in real time, providing highly personalized service.
Most AI language programs also have a gamification element. Gamification has been shown to improve language acquisition; it also, of course, keeps children and adults focused on the program.
Finally, these programs provide analysis to the teacher. These analyses include warnings that a student is falling behind so the teacher can reach out to them right away.
Developers, teachers, and many language learners may find that the AI program’s own aggregated, anonymized user data to be an invaluable source of language acquisition and education information which can be used to further improve the AI program.
Many programs also find a way to connect users to real people to add an important social dimension. For example, user data can match learners with each other, when each user speaks the other’s target language. These programs can also match learners to qualified tutors.
Much is made about NLP (natural language processing) programs for language learning apps. However, NLP programs that can recognize individual words and grammatically correct sentences in human speech are still unusual, despite their utility to learners who don’t have access to a native speaker of their target language.
Finally, from the point of view of the teacher, internal data about the progress of all students in a class or grade is absolutely vital.
As noted above, the most important AI language acquisition data is external user behavior data. The times a learner accesses the learning program, how long they spend on it, what methods of instruction they respond to best, what subjects they remember best, and so on—AI builds its language lessons on this data.
The culture and history of the target language as well as the history of the countries from which it developed are important external data sources. While many would consider this information essential, that may only be the case for more advanced lessons.
Other useful data includes media (especially television and music), news, and online forums associated with the AI program.
The main challenges of this use case include competition, user experience, and lack of speaking opportunities. A new language program, even one that makes good use of AI, must compete against huge numbers of digital language programs—and this market has exploded over the past year, with millions of people stuck at home.
Relatedly, the program must have an attractive and easy-to-use interface as most learners, who don’t have to use a certain program, have no patience for a confusing UI.
Finally, the reduced ability to speak with native language speakers might always dog these programs. Some, as mentioned above, have a speaking component built in. However, since many learners use these programs in their spare time, often in public, they will not use them.
Other programs—again, as mentioned—pair learners with each other or with tutors, or recommend associated online forums. However, it is usually only teachers that can make participation mandatory.
[Chivox Intelligent English Learning (驰声优学)] adopts OMO (Online-Merge-Offline) model and the company’s self-developed intelligent oral assessment technologies. Renowned teachers are invited to set assessment questions for the product, which focuses on nurturing listening and speaking skills. Together with the Chivox Intelligent English Learning Journal (“Chivox Journal”,《驰声优学报》), it provides teachers and students with both online and offline environments for teaching and learning.
Semasio Audience Targeting uses the Semasio semantic approach to optimize marketing strategies. This approach uses records of keywords and phrases used by site visitors to create Semantic User Profiles. Then Semasio takes keyword and phrasal similarities in the browsing habits of established customers to create Seed Audiences that you can use to plan your marketing campaigns.
In each case, Semasio provides companies the ability to tailor their marketing approach with either specific or more general keywords.
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Automaton AI’s dataset – ‘Automaton AI Agriculture raw data’ provides and Agriculture Data that can be used in AI Language Learning Programs and
TAUS’s dataset – ‘TAUS Language Translation Data | Parallel translation for Colloquial English into various languages for Machine Learning’ provides that can be used in AI Language Learning Programs and