Virtual Worlds for Language Teaching and Learning


The COVID-19 pandemic has forever changed the way that we work and learn. The “new normal” in the 21st century is for students to engage with their teachers and peers in both physical and virtual learning environments. In the summer of 2022, Paul Raine and Raquel Ribeiro explored 6 different virtual worlds, and evaluated their viability for language teaching and learning. Only virtual worlds that run in a web-browser and have a free trial were selected for this project. In this article, we present the results of our investigation, with the hope that this information will be of interest to other language teachers looking to teach all or some of their classes online. A series of YouTube videos to accompany this article can be found here.


The following virtual worlds are review in this article:

  1. (video)
  2. Gather Town (video)
  3. Wonder* (video)
  4. Orbital** (video)
  5. Mozilla Hubs (video)
  6. KumoSpace (video)

*In April 2023, Wonder announced it would be shutting down. We list the platform here for informational purposes only.
**In December 2022, Orbital announced it would be shutting down. We list the platform here for informational purposes only.

SpatialGatherWonderOrbitalMozilla HubsKumospace
Audio & Video ChatYesYesYesYesYesYes
Text ChatYesYesYesYesYesYes
Screen SharingYes – EmbeddedYesYesYesYes –
Customizable AvatarYesYesNoNoYesNo
Sticky NotesYesNoYesYesNoNo
Object PickerYesYesNoYesYesNo
Interactive EnvironmentYesYesNoNoYesNo
Emoji ReactionsNoYesYesYesYesYes
Mini GamesNoYesNoNoNoYes
Web AppYesYesYesYesYesYes
iOS AppYesNoNoNoNoNo
Android AppYesNoNoNoNoNo
Forever-free PlanYesYesNo – Free TrialYesYesYes
Best FeatureDance movesMini GamesRandomise UsersPrivate IslandLaser PenPiano Music

Common Features


The virtual worlds (VWs) we evaluated for this report came in two different perspectives: top-down and 3D. Four of the six worlds had top-down perspectives, and two offered a full 3D experience.

Figure 1: The top-down perspective of Kumospace

Figure 2: The 3D environment of Spatial

Audio and video chat

All the VWs we evaluated had the ability to chat via live video and audio with other members. In some VWs, the video stream was rendered as the user’s avatar, and in other VWs, the video was rendered above or to the side of the environment.

Figure 3: In Orbital, the user’s video stream is rendered as their avatar.

Figure 4: In Wonder, the user’s video stream is rendered above or to the side of the environment.

Text chat

All of the VWs we investigated in this study offered the ability to send text-based messages to other members of the environment. We found that the text chat was a very useful supplement to audio-visual teaching methods, especially when teaching new words with unfamiliar pronunciations.

Screen Sharing [Embedded]

All of the VWs in this study offered the ability to share a screen with other users in the environment. In Mozilla Hubs and Spatial, the shared screen was “embedded” in the environment such that users could choose to either continue interacting with each other, or view the shared screen from a variety of perspectives.

Figure 5: Sharing a screen in Spatial

Custom Avatar

Some of the VWs we investigated allow the user to customise their avatar in various ways. The most advanced and personalised customization was offered by Spatial, which provided a way to convert a digital photograph into a 3D head for a user’s avatar.

Figure 6: Spatial offers the ability to convert a photo into a 3D head for a user’s avatar

Sticky Notes

Most of the VWs we investigated offered the ability to add “sticky notes” to the environment. These came in useful when teaching new words or phrases.

Figure 7: The “sticky note” function in Orbital

Interactive Environment

Some of the VWs we investigated offered the ability to interact with one’s environment. For example, by picking up and moving objects around. This feature could be used for teaching prepositions, by instructing learners to “put the goldfish on the wall” for example.

Figure 8: Interacting with a 3D goldfish in Mozilla Hubs

Mini Games

Two of the VWs we investigated featured “mini games” such as chess, which were completely contained within the virtual world. It is possible that these mini games could be used for spoken fluency practice by higher level language learners.

Figure 9: An invitation to play chess within the Kumospace virtual environment.

Object Picker

In addition to being able to interact with one’s environment, some VWs also offer an “object picker”, “designer”, or “build tool” that allows the user to add, remove, or change objects in the environment. This could either be used for introducing new vocabulary items, or for making the environment a more conducive space for language learning.

Figure 10: The object picker within Gather allows the user to add a wide range of weird and wonderful items to their virtual environment.

Emoji Reactions

Most of the VWs we investigated allow the user to react with a variety of emojis. These could be used for expressing comprehension, interest, or confusion when learning a language in a virtual environment.

Figure 11: Reacting with a “heart” emoji in Gather

General Suitability for Language Learning

The authors found that in general, it was possible to learn new foreign words and phrases inside of the VWs investigated in this study. This was verified in a rudimentary way by learning words and phrases in Portuguese and Japanese. One author had a native level of Portuguese, and attempted to learn some basic Japanese. The other author had an intermediate level in Japanese, and attempted to learn some basic Portuguese. Both authors were complete beginners in the language being taught to them. 

It was found that the fidelity of the audio stream was of paramount importance in the language teaching and learning process. Where the quality of the audio was bad (such as in Spatial) it was sometimes not possible to distinguish between similar consonant sounds, such as “d” and “b”. For instance, when the Portuguese word for “chair” was first introduced, it was initially pronounced by the learner as “cabeira” whereas the correct pronunciation is “cadeira”. The authors found that the chat function could be used to clarify the pronunciation of unfamiliar words when the audio was insufficiently clear.

Specific Methodologies

Because both authors were complete beginners in the languages they were learning (Portuguese and Japanese) simple “show and tell” and “listen and repeat” methodologies were the main ones adopted in this preliminary investigation. In addition, Total Physical Response (TPR) was also briefly trialled, with one author being instructed by the other to “go closer to the tree” in Portuguese. 

It is expected that, in reality, learners using VWs would not be complete beginners in the languages they are studying. Therefore, it seems reasonable that methodologies such as Communicative Language Teaching (CLT) or perhaps even Task Based Language Teaching (TBLT) could be adopted, and that this would result in improvements to communicative and pragmatic competence in a similar way that it would in real, physical classrooms.

Issues and Limitations

There were several issues and limitations with the current study. Firstly, the authors involved were living on opposite sides of the world, with a 12 hour time difference. This sometimes made it difficult to find a suitable time to meet up. It also caused occasional network issues. 

In addition, the authors encountered some audio fidelity problems in the Spatial virtual environment, which interfered with the ability to clearly understand the correct pronunciation of unfamiliar foreign words.

Finally, only the two authors in this study were able to participate in the environments investigated. In real life situations, it is highly likely that there would be more participants in a language learning environment, including the teacher and perhaps 10 to 20 students. The effect that this number of users would have on the quality of the language learning experience is not known, and should be further investigated. Many of the VWs investigated in this study were specifically designed to handle a large number of concurrent users, and it would be interesting to see how the affordances of these virtual environments could be leveraged for larger classes.


Although Zoom has become the de facto application for online synchronous communication, it is not the only way to connect with people in remote locations in real time. The authors found many of the above virtual worlds to be just as reliable as Zoom and in most cases more visually engaging and stimulating. Language teachers might like to consider one or more of the above options in addition to or instead of Zoom for their online language classes.

Learning Languages with AI-powered Chatbots

Advances in both Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) have raised the question of whether AI-powered chatbots could be an alternative or supplement to flesh-and-blood human teachers in some situations. Can these tools really help learners acquire foreign languages?

From the malevolent Hal 9000 in Stanley Kubrick’s 2001: A Space Odyssey to the charming Samantha in Spike Jonze’s Her, computers that talk have shocked and seduced us in popular culture for many decades.

Spike Jonze’s Samantha (pictured) developed an intimate relationship with its (her?) owner

When Apple officially incorporated their voice assistant Siri into iOS in 2011, the reality of having an intelligent assistant that understood and obeyed our every word seemed one step closer for everyone.

The Amazon Echo “smart speaker” hit the market in 2014, and has been the dominant device in that field ever since. Other products in the same space include Google’s Nest, and Apple’s HomePod. Social networks also started to jump on the AI assistant bandwagon, with Facebook incorporating chatbots into its Messenger platform in 2016, and LINE releasing the Clova assistant in 2017.

Smart Speakers such as Amazon’s Echo (pictured) have been gaining popularity since 2014

Advances in both Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) have raised the question of whether AI-powered chatbots could be an alternative or supplement to flesh-and-blood human teachers in some situations. Can these tools really help learners acquire foreign languages?

General purpose AI assistants for language learning

The applicable theory of language learning to bear in mind here is interactionism – the idea that languages are acquired by interacting with other speakers of those languages.

Even though smartphone and smart speaker based AI assistants haven’t usually been designed specifically with language learning in mind, some innovative teachers and researchers have used them for these purposes. One of the major issues to overcome here is the fact that these assistants aren’t optimized for non-native speech, and may struggle to correctly transcribe or understand it.

Research has shown that the tech behind these devices can recognize non-native speech to some extent (with Google Assistant recognizing 87% of learner utterances and Apple’s Siri recognizing 67% in one Japan-based study). 

So if a language learner is able to speak clearly enough for an AI assistant to understand them, what kinds of activities can be done to bring about further gains in language ability?

The applicable theory of language learning to bear in mind here is interactionism – the idea that languages are acquired by interacting with other speakers of those languages. 

Interacting with another person with language involves taking turns, negotiating meaning (figuring out what the other person is trying to say), and an information gap (transferring information from one speaker to another). There is no reason in theory why the tenets of interactionism cannot apply to human-computer interaction as well as human-human interaction.

General purpose AI assistants can stand in for human interlocutors in interview or quiz type activities, especially where the learner is asking the questions. However, the interaction tends to become one-sided, because AI assistants don’t ask questions unless programmed to do so. 

And while learners may receive implicit feedback on pronunciation or grammatical form where the AI doesn’t understand the question that has been uttered, they won’t receive explicit feedback unless they are using an app that has been specifically designed for language learners.

AI assistants specifically designed for language learning

There have been several attempts to develop AI assistants and other interactive speech apps specifically for language learners. Here we will take a look at some of these products and services, and evaluate their usefulness and effectiveness.

Duolingo Chatbots

Duolingo launched chatbots for its iOS app back in 2016, promising to help users “come up with things to say in real­-life situations”. Although the feature seemed to be well received by its users, it quietly slipped away and there is no sign of it returning yet.

Duolingo’s chatbots included a “Help me reply” feature, which would suggest words and phrases for the learner to use in their responses. The interactions with the chat bots would become more advanced as the users’ level progressed. There were some limitations to Duolingo’s chatbots though. For example, they only offered structured dialogues, as opposed to open-ended speech.

Duolingo’s chat bots (iOS only) promised to help users “come up with things to say in real life situations”.. but the feature quietly slipped away and shows no sign of returning

We hope to see a new version of these Chatbots from Duolingo as they have shown promising results for language learning outcomes.

Elai (ETS)

In December 2020, ETS released Elai (iOS/Android), an app that allows users to practice speaking about a range of topics and receive feedback on their speech.

Elai includes model answers from other learners and native speakers, and also provides tips for learners who want to repeat the same exercise.

Unlike Duolingo chatbots, Elai’s focus is on open speech. Users must respond to a prompt and record their answers within a 30 second time limit.

Elai offers a variety of feedback on learner speech, including the extent to which the learner repeated the same words; how often the learner paused in during their speech; and whether or not the learner used a lot of “filler” words, e.g. “uh”, “erm”, “ah”.

Elai attempts to improve the speaker’s vocabulary knowledge by providing a list of higher level words at the end of the exercise, which could also be used to respond to the prompt.

Elai is still in Beta status, and the extent to which it will be embraced by learners, teachers, and researchers is still an open question, but being developed by one of the world’s largest English language testing companies (ETS is behind the TOEFL and the TOEIC) certainly puts it in a strong position from the outset. is aimed specifically at young learners of English (iOS/Android) focusses on the young English language learner market, and promises to “[provide] unlimited practice of spoken English.. to millions of students”.

The app offers a variety of language games and activities, including listen and repeat, question and answer, and interactive videos.

One of the drawbacks of the app, however, is that it only supports users with Russian, Spanish, Turkish and Polish as a first language. The app has a bilingual interface, and if the user has a first language other than one of these four, they will struggle to understand the instructions.


Elsa (iOS/Android) is a mobile app that focuses specifically on improving the users pronunciation to help them “speak like an American” (although proponents of TEFL Equity might have something to say about this – should “American” be the target for all English learners?).

Through listen-and-repeat and interactive dialogue type exercises, Elsa teaches the user how words are blended together in casual speech, which in turn helps to improve the user’s fluency.


The principles of interactionism suggest that language learners can improve their skills simply by conversing with another speaker of the target language. However, there are issues to be overcome when using AI-powered virtual assistants for language learners, including lack of optimization for non-native speech, and lack of true discourse-level interaction.

Apps that specifically target language learners can do better when it comes to recognizing non-native speech, and offering more life-like interactions. 

English Central, for example, is one of the leaders in the recognition of non-native speech, and gives users instant feedback on their pronunciation and fluency while speaking lines from a library of thousands of videos.

However, many of the other apps discussed here focus on either niche segments of learners (e.g. Russian and Polish speaking children) or niche language language skills (such as fine-grain pronunciation problems). 

There is yet to emerge an artificially intelligent chatbot which can be used by all levels and all ages of learners that offers true human-like interaction and feedback.

In addition, student reactions to the recent COVID pandemic have shown that many students value face-to-face learning over online methods. Although chatbots and smart speakers could be a useful supplement to face-to-face or online learning with a human teacher, it seems unlikely that they will be a complete replacement for human teachers any time soon.

Introducing Learn-English.Org!

What is Learn-English.Org?

Learn-English.Org is a free website for learners of English to practice listening, speaking, reading, and writing online, anywhere, anytime!

How do I use Learn-English.Org?

Find an activity you would like to study by using the navigation panel on the left. There are three ways to navigate the activities on this site: by categoryby skill, and by level.

Can I track my progress?

Yes, you can track your progress on this site by creating an account and then checking your progress report.

Who is behind Learn-English.Org?

This site is produced and developed by English language and Ed-Tech experts, and powered by TeacherTools.Digital, an innovative digital assignment creation platform for language teachers.

More Meditations on Machine Translation

At this year’s CEGLOC virtual conference, I watched a couple of presentations about the role of Machine Translation (MT) in language teaching and learning.

They got me thinking again about a subject I’ve written about a few times before and also edited an article on.

Here are a few key assumptions about the intersection of MT and language teaching/learning:

#1 The accuracy/naturalness of MT is continuing to improve. The output produced by MT is approaching the point where it is virtually indistinguishable from the output produced by human translators.

#2 It is unacceptable for a student to rely solely on MT when submitting work for an assessed course of language learning. An example of this would be if a Japanese student wrote a report entirely in Japanese, pasted the report into an MT tool, copied the resulting English, and handed in the work in as their own (without even looking at the resulting English text)

#3 Notwithstanding #2, MT could have powerful pedagogical applications if used in the right way. 

#4 It is difficult, if not impossible, to completely prevent the use of MT without reverting to hand-written essays in exam conditions. Unlike plagiarism, MT cannot be easily detected by software. Although there are still some tell-tale idiosyncrasies of translations produced by MT (such as inappropriate grammatical subjects when translating Japanese to English, for example) such traits/mistakes are becoming less obvious as MT continues to improve

#5 The kind of behavior exemplified in #2 is clearly not a “CALL” technique. Computer Assisted Language Learning entails the use of computers to assist the learning of a language. Using MT to generate a piece of assessed work and handing it in sight-unseen is basically indistinguishable from plagiarism. But instead of passing off the work produced by another human intelligence as their own, the perpetrator is passing off the work generated by artificial intelligence as their own.

The above assumptions (if correct) raise some interesting questions, and force us to re-evaluate the reasons or motivations for learning a language.

The motivation for learning a language is often categorized into three main strands: integrative, intrinsic, and instrumental.

Integrative motivation compels language learners who wish to live in and integrate with a target language community. This kind of motivation might drive an American who wants to emigrate to and settle down in Japan, for example. 

Would such an individual be able to rely solely on MT tools and devices to achieve this goal? Could they whisper sweet nothings into their iPhone, and then place the iPhone on their pillow and allow it to translate and convey those sentiments to their significant other? Perhaps not.

Intrinsic motivation comes from inside the individual and often arises from a deep interest in the target language itself. Intrinsically motivated students are interested not only in the syntactic structure of the target language, but also in how speaking the language will change the way they perceive and interact with the world around them.

Could an intrinsically motivated individual leverage the power of MT to further increase their knowledge of the target language? I think so. Would they be happy to completely delegate to MT the task of translating their thoughts from L1 to L2? Would they want to miss out on the philosophical or cultural insights that learning another language can bring about? I think not.

Instrumentally motivated individuals simply treat the target language as a means to an end. They want to get a promotion or avoid being demoted. They want (or have) to do business with speakers of the target language. They want to quickly translate an email or subtitles for a video (e.g., They want to pass an exam or entrance test for a particular business organization or academic institution. 

Could such an individual rely extensively on MT to achieve their aims? I think so. Would it be fair to allow them to do so, especially with regards to assumptions #2 and #5 above? Perhaps not. 

That question would need to be decided by the organizations and institutions involved, who are best placed to judge the skills and competencies they require from candidates.

Given all of the above, I tend to believe that the use of MT tools and devices will continue to increase, especially in situations where instrumental motivation is paramount, or time and money costs are significant. 

But in my role as a language teacher who has to assess the written and spoken output of language learners, there are difficult questions to answer with regard to the role that MT can or should play in the language learning process.

MT surely has many powerful pedagogical applications, but the temptation for time-pressed and sleep-starved students to rely solely on MT to produce the required output is high.

And then we’re into the familiar territory of plagiarism – passing off another’s work as your own. Something most academic institutions seriously frown upon.

So, those are my current thoughts on MT.

Would love to hear others.

20 Tech Tips from Joe Dale

Joe Dale is a wealth of ed-tech tips and information

For anyone unfamiliar with Joe Dale, I highly recommend you check out his YouTube channel and follow him on Twitter. The man is an absolute wealth of tech tips for language teachers. Here are a few gems I picked up from him in a single Zoom session:

  1. Make any video your lesson with EdPuzzle
  2. Visualize your ideas in a new and collaborative way using JamBoard
  3. Easily add transcribed voice comments and feedback to shared documents using the Mote Google Chrome extension
  4. Allow students to create digital learning portfolios with Seesaw
  5. Quickly and easily record your voice with Vocaroo or OnlineVoiceRecorder
  6. Immersive Reader, included in OneNote Learning Tools, is a full screen reading experience to increase readability of content in OneNote documents
  7. Ferrite Recording Studio makes it fast and easy to record and edit audio, and includes powerful features such as effects and automation
  8. Voice Record Pro 7 is a professional voice recorder for iOS
  9. Textivate generates a wide range of interactive activities based on your own text and / or matching items. It works with texts of up to 500 words and / or up to 200 matching items
  10. Teach any language with YouTube + TeachVid
  11. is a Web 2.0 application, to support learning and teaching processes with small interactive modules
  12. You can easily allow anyone to create a copy of a Google doc you have created by changing the end of the URL from /edit?usp=sharing to copy: ->
  13. Easily create any kind of Google Drive doc with the following URL shortcuts:,,
  14. Use Ilini to learn French with the best videos on the web
  15. Create presentations, infographics, and more with Genially
  16. Create your own personal Emoji with Bitmoji
  17. Get popup translations for any website using Lingro
  18. Get easy-to-understand multilingual definitions with
  19. is a robust, easy-to-use and secure exam platform
  20. Draftback is a Chrome extension that lets you play back any Google Doc’s revision history