The influence of AI in education has been transformative and is gradually being introduced to language education. Artificial Intelligence (AI) technologies in education are revolutionizing language learning platforms, introducing personalized learning experiences, and redefining traditional teaching methodologies.

Examples of Educational AI Technologies

As language learning organisations, understanding this shift is crucial for staying relevant and effective in the digital age.

What are some of these exciting educational AI technologies that can be offered to your students?

Intelligent Computer-Assisted Language Learning (ICALL)

In second language acquisition, recent developments in AI build on previous work in Intelligent Computer-Assisted Language Learning (ICALL) dating as far back as the late 1970’s. This system aims to investigate how learning a second language with the goal of being able to communicate in the target language can be enhanced through personalized learning materials, instruction and feedback.

ICALL consists of two levels of learning - vocabulary learning and sentence learning – while using numerous types of vocabulary learning techniques. A commonly used method is to represent the meaning of the new word with a related image to help the learner build a connection between the visual and the verbal. This is also categorized as a semi-contextualizing technique and helps the learner to absorb or memorize new words.

Computerised Dynamic Assessment (CDA)

Computerised Dynamic Assessments (CDA) provide learners with automatic corrections that allow them to analyse any language-related errors in their work. This corrective feedback is effective in helping students self-identify and self-correct a range of grammatical issues, such as punctuation, grammar and verb complement.

An added benefit of this system is the ability to facilitate many students at the same time, instead of waiting for one-on-one teacher feedback. But this corrective feedback is beneficial to educators as well, assisting them to gain a deeper understanding of their students’ abilities and progress.

Mobile-Assisted Language Learning (MALL)

Mobile-Assisted Language Learning (MALL) is second language learning that is assisted or enhanced through the use of a mobile device.

MALL is beneficial for language learners for many reasons:
  • The average person is that much more likely to have access to a mobile phone over a desktop computer.
  • It creates opportunity to learn from anywhere and at any time – during dedicated study time, when traveling, or simply while on the daily commute. Which makes mobile a perfect platform for micro-lessons!
  • When creating language learning apps and activities that are easy to access and enjoyable, students will be motivated to continue studying outside of class.
  • Furthermore, the ability to download content for offline use is very useful for students in areas without uninterrupted access to connectivity.

Speech Recognition Software

In the area of providing feedback, there have been some exciting advances in educational AI technologies in education, one of them being the development of automatic voice recognition software that language learners can use to record themselves speaking on their computers or mobile devices, and then receive a score and automated feedback through a speech evaluation system.

This method can also be used for students to read out loud into a programme that can transcribe their speech into text, whereafter they can review it for any mistakes.

This tool further enhances a learner’s experience to be more interactive, engaging and enjoyable!

Deep-learning Reading Bot

Reading comprehension is one of the primary ways for a student to acquire knowledge, which is why reading skills remain one of the central tasks in literary education.
So it’s no surprise that a focus area for development in deep learning-based Natural Language Processing (NPL) has been in reading comprehension. A reading bot can act as an instructor for readers with reading difficulties or can assist them in preparing for a language test.

It guides readers through activities in reading comprehension, including facilitating questions, vocabulary building, analysis of complex and long sentences, multiple-choice question quizzes, and writing tasks.

In addition, it can also assist educators in preparing reading course materials with automatically generated questions, image and audio resources retrieved from knowledge graphs, and automatic grading of essays.

Data-driven Learning

This learning method is an approach to foreign language learning that is facilitated by the use of corpora - collections of linguistic data used for research and teaching.

It encourages language exploration through students’ own investigation of patterns naturally occurring in the target language. From their findings, foreign language students can see how an aspect of language is typically used, which in turn informs how they can use it in their own speaking and writing.

When students arrive at their own conclusions through this learning method, they use their higher order thinking skills and are creating knowledge, which in turn becomes more memorable.

Foreign Language Speaking Practice

AI-powered platforms and applications can replicate real-life conversational scenarios, providing learners with opportunities to practice their spoken foreign language in a safe and supportive environment, which can significantly accelerate the English learning process.

AI algorithms can then further analyze individual learning patterns and adapt the curriculum to cater to specific needs, ensuring personalized and effective learning experiences by providing a wealth of resources, including vocabulary builders, pronunciation guides, and language drills, enhancing overall language proficiency.

Automated Writing Evaluation (AWE)

Other developments in NLP have led to the uptake in automated writing evaluation (AWE) - software that can automatically assess student writing by comparing a written text to a large database of writing of the same genre.

The software then analyzes measurable features in a text, such as syntax, text complexity, total word count, and vocabulary range, through statistical modelling and algorithms. The text is then graded and given an overall score, which is supported by suggestions for improvements and feedback on the overall writing style.

Peer Correction

Peer correction is a technique where students learn from their mistakes and provide feedback to their classmates. A study was done by taking an experimental approach to investigate the effectiveness of utilizing peer correction when using AI for ESL speaking practice.

The framework for this research was rooted in a socio-cultural theory which emphasises the pivotal role of social interaction and context in language learning. The findings reveal that peer correction can reinforce students' learning outcomes during their practice on AI speaking tools and should be encouraged.


Teaching online language students in an AI-driven future requires a strategic blend of traditional and evolving teaching values and cutting-edge technology.

By integrating educational AI technologies thoughtfully, language educators can create effective learning experiences that ensures their success in the evolving landscape of online language education.