Just before the dawn of the internet I worked with the CEO of a major CD language learning company. His business model was fascinating,
“I don’t sell language learning, I sell the false promise…. My customers are ‘false starters’ mostly middle-class people who think they’ll learn a language in a few months before they go to Italy, Spain or France on their holidays… they never do.” He explained that the whole market was based on this model. The BBC packages at the time were the worst, he explained, “They’d send a film team to France for a month or so, come back and write a book around it…. it is literally impossible to learn a language from their materials”. I stayed out of that market. But times they are a changin’….
The technology moved on from CDs, along came the internet, and we saw the first big effect on learning languages – mainly English. The abundance of music, films, sport in all media on the web, allowed ready access to content, allowing contact, practice and immersion. Huge numbers have learnt languages without direct instruction. But learning a second language remains one of the most difficult things one can do in life and direct instruction still has a place. The problem with online instruction is that the technology was still too flat, text based and restricted to simple drill and practice. The content was too linear, often dull and struggled when it came to the spoken word, practice and immersion. Technology is now influencing not only what languages we learn but how and even why we learn languages. Some argue that the Anglo-saxon domination of the internet has accelerated the expansion of English as a global language. Machine translation raises the interesting possibility in that it may lead to less people learning new languages, if frictionless, real-time translation is available. But the most obvious and immediate impact will be on the practical teaching and learning of languages, where smart technology is already having a global impact.
Of one thing we can be sure; AI brings a new paradigm to language learning. Natural Language Processing (NLP) has brought entity analysis, sentiment analysis, classification and machine translation. In addition we have text to speech and speech to text, now revolutionising interfaces. At the same time algorithmic techniques and machine learning brought adaptive, personalised and spaced learning. Even image recognition is being brought into identification and assessment. These technologies are being blended to produce sophisticated language learning and the possibility of learning a language without human instruction. One has to look across the whole learning journey to see how this is potentially possible.
To see how far AI has come in languages, Machine Translation is a good starting point . Google Translate can handle over 100 languages and us used over half a billion times a day. Launched in 2006 it used Statistical Machine Translation to match strings by probability against strings in another language, basically pattern matching. But in late 2016 it switched to Neural Machine Translation, making it much more successful and contextual. It is available as a browser extension and on Google Home and Google’s Pixel Buds. The ear ‘Buds’ can translate 40 languages in real time. To be fair, like Skype’s real time translation, it’s far from fluid and perfect but the direction of travel is clear – it will get better and better.
So what about learning a language? Most successful language learning models take the learner on a learning journey from simple basics to practice then production. This progression normally starts with structural basics on the alphabet, vocabulary and grammar. Practice usually starts with limited and controlled practice and moves towards more open and free practice. Finally, there is generative production and use of the language. In addition to the actual learning there are also pedagogic issues such as motivation (a particular problem in language learning) and assessment. AI has a role to play across the whole of this learning journey.
Drill and practice
My first ever computer-based learning programme was teaching the Russian alphabet, which I built using the Commodore 64 graphic characters. You saw a character and had to type in the corresponding English sound (as a letter or letters). I then programmed a behavioural drill and practice vocabulary programme. Randomisation was a feature, stratified with progress dependent on scores. This was typical of most early computer assisted language learning programmes.
Basic drill and practice is still a feature of most adaptive systems, such as Duolingo, with 200 million registered users, where structured topics are introduced, alongside basic grammar but adaptive algorithmic techniques track your progress and take into consideration, your forgetting curve, short-term success rate and effort. Adaptive systems can blend individual with aggregate data to optimise progress for the learner, depending on need. Every new learning event can be uniquely presented to that learner thus personalising the learning, an important form of optimisation in language learning, give the distribution of ability.
Spaced practice, where the learners use retrieval techniques in a structured reinforcement pattern to push knowledge and skills from working to long term memory is a good starting point for the consolidation of acquired knowledge and skills. Anki is a free package that uses the algorithmic control of spaced practice to determine the learning path.
Controlled practice, to varying degrees, can also be delivered using chatbots. There are many species of chatbots from learning engagement, teaching, mentorbots, and practice bots. Chat has overtaken social media on mobiles and is clearly the preferred interface. We seem to have a natural affinity to chat interfaces and in some cases, with wellbeing bots, even the anonymity of the machine has been shown to be an advantage. They have been successfully used in educational and corporate training environments. They offer a dialogue interface, so are eminently suitable for language learning, with flexibility around the recognition of replies by the learner and, of course, speech. They have huge potential and when embodied in consumer, home devices can bring language learning into to the home.
But active immersion is also now possible with home devices. You can switch your Amazon Echo to respond in German. Consumer technology, such as Alexa, Google Home and others will offer cheap, free and increasingly sophisticated language learning in your home. Ask it a question in English and it will reply in German. This is a bit like having a German person in your own home 24/7.
The internet provides a wide and deep set of resources in most major languages. There’s an endless amount of content in your target language, in all media – text, audio and video - movies, box sets, music videos, Youtube, Wikipedia, whatever. Here other immersive technologies come into play, such as VR and AR. These are not AI technologies but AI techniques can be used within these environments to provide immersion, attention and context for language learning. In a current project (WildFire) we have successfully integrated speech input within VR, which not only allows you to navigate through the learning using just your voice but also input open response input and so on.
Both Babbel and Doulingo offer paid English assessment testing. Face and digital recognition allow unique identification of candidates for assessment. Keyboard typing patterns can be recognised, along with adaptive assessment, which adapts to the candidate’s ability level, are all being used. Online assessment is now here, which increases accessibility and progress in language learning.
AI, with its rapid advances, specifically in technologies that aid language learning, may turn out to be the most significant technology in this field to date. The technology provides behind the scenes language processing that allows machine translation, speech recognition and many other services to be used across the learning journey to keep learners moving forward, optimising and personalising delivery. It has already accelerated the digitisation, disintermediation, decentralisation and democratisation of language learning. Yet we must be careful in attributing too much efficacy to AI. Its translation ability is nowhere near as good as human translation, speech recognition still a bit ropey and with other services, such as chatbots you need to be a bit forgiving. Nevertheless, it is constantly improving and on current rates of progress, it seems likely that it will have a major impact in language learning.