Archive for March, 2022

In May of last year, EL Gazette had a story entitled ‘Your new English language teacher is a robot’ that was accompanied by a stock photo of a humanoid robot, Pepper (built by SoftBank Robotics). The story was pure clickbait and the picture had nothing to do with it. The article actually concerned a chatbot (EAP Talk) to practise EAP currently under development at a Chinese university. There’s nothing especially new about chatbots: I last blogged about them in 2016 and interest in them, both research and practical, dates back to the 1970s (Lee et al., 2020). There’s nothing, as far as I can see, especially new about the Chinese EAP chatbot project either. The article concludes by saying that the academic behind the project ‘does not believe that AI can ever replace a human teacher’, but that chatbots might offer some useful benefits.

The benefits are, however, limited – a point that is acknowledged even by chatbot enthusiasts like Lee et al (2020). We are some way from having chatbots that we can actually have meaningful conversations with, but they do appear to have some potential as ‘intelligent tutoring systems’ to provide practice of and feedback on pre-designated bits of language (especially vocabulary and phrases). The main benefit that is usually given, as in the EL Gazette article, is that they are non-judgemental and may, therefore, be appropriate for shy or insecure learners.

Social robots, of the kind used in the illustration for the EL Gazette story, are, of course, not the same as chatbots. Chatbots, like EAP Talk, can be incorporated into all sorts of devices (notably phones, tablets and laptops) and all sorts of applications. If social robots are to be used for language learning, they will clearly need to incorporate chatbots, but in what ways could the other features of robots facilitate language acquisition? Pepper (the robot in the picture) has ‘touch sensors, LEDs and microphones for multimodal interactions’, along with ‘infrared sensors, bumpers, an inertial unit, 2D and 3D cameras, and sonars for omnidirectional and autonomous navigation’. How could these features help language acquisition?

Lee and Lee (2022) attempt to provide an answer to this question. Here’s what they have come up with:

By virtue of their physical embodiment, social robots have been suggested to provide language learners with direct and physical interactions, which is considered one of the basic ingredients for language learning. In addition, as social robots are generally humanoids or anthropomorphized animal shapes, they have been valued for their ability to serve as familiar conversational partners, having potential to lower the affective filter of language learners.

Is there any research evidence to back up these claims? The short answer is no. Motivation and engagement may sometimes be positively impacted, but we can’t say any more than that. As far as learning is concerned, Lee and Lee (2022: 121) write: involving social robots led to statistically similar or even higher [English language learning] outcomes compared with traditional ELT contexts (i.e. no social robot). In other words, social robots did not, on the whole, have a negative impact on learning outcomes. Hardly grounds for wild enthusiasm … Still, Lee and Lee, in the next line, refer to the ‘positive effectiveness of social robots in English teaching’ before proceeding to enumerate the ways in which these robots could be used in English language learning. Doesn’t ELT Journal have editors to pick up on this kind of thing?

So, how could these robots be used? Lee and Lee suggest (for younger learners) one-on-one vocabulary tutoring, dialogue practice, more vocabulary teaching, and personalized feedback. That’s it. It’s worth noting that all of these functions could equally well be carried out by chatbots as by social robots.

Lee and Lee discuss and describe the social robot, NAO6, also built by SoftBank Robotics. It’s a smaller and cheaper cousin of the Pepper robot that illustrates the EL Gazette article. Among Lee and Lee’s reasons for using social robots is that they ‘have become more accessible due to ever-lower costs’: NAO6 costs around £350 a month to rent. Buying it outright is also an option. Eduporium (‘Empowering the future with technology’) has one on offer for $12,990.00. According to the blurb, it helps ‘teach coding, brings literature to life, enhances special education, and allows for training simulations. Plus, its educational solutions include an intuitive interface, remote learning, and various applications for accessibility!’

It’s easy enough to understand why EL Gazette uses clickbait from time to time. I’m less clear about why ELT Journal would print this kind of nonsense. According to Lee and Lee, further research into social robots ‘would initiate a new era of language learning’ in which the robots will become ‘an important addition to the ELT arsenal’. Yeah, right …


Lee, H. & Lee, J. H. (2022) Social robots for English language teaching. ELT Journal 76 (1): 119 – 124

Lee, J. H., Yang, H., Shin D. & Kim, H. (2020) Chatbots. ELT Journal 74 (3): 338 – 3444

In the latest issue of ‘Language Teaching’, there’s a ‘state-of-the-art’ article by Frank Boers entitled ‘Glossing and vocabulary learning’. The effect of glosses (‘a brief definition or synonym, either in L1 or L2, which is provided with [a] text’ (Nation, 2013: 238)) on reading comprehension and vocabulary acquisition has been well researched over the years. See Kim et al. (2020) for just one recent meta-analysis.

It’s a subject I have written about before on this blog (see here), when I focussed on Plonsky ad Ziegler (2016), a critical evaluation of a number of CALL meta-analyses, including a few that investigated glosses. Plonsky and Ziegler found that glosses can have a positive effect on language learning, that digital glosses may be more valuable than paper-based ones, and that both L1 and L2 glosses can be beneficial (clearly, the quality / accuracy of the gloss is as important as the language it is written in). Different learners have different preferences. Boers’ article covers similar ground, without, I think, adding any new takeaways. It concludes with a predictable call for further research.

Boers has a short section on the ‘future of glossing’ in which he notes that (1) ‘onscreen reading [is] becoming the default mode’, and (2) that ‘materials developers no longer need to create glosses themselves, but can insert hyperlinks to online resources’. This is not the future, but the present. In my last blog post on glossing (August 2017), I discussed Lingro, a digital dictionary tool that you can have running in the background, allowing you to click on any word on any website and bring up L1 or L2 glosses. My reservation about Lingro was that the quality of the glosses left much to be desired, relying as they did on Wiktionary. Things would be rather different if it used decent content – sourced, for example, from Oxford dictionaries, Robert (for French) or Duden (for German).

And this is where the content for the Google Dictionary for Chrome extension comes from. It’s free, and takes only seconds to install. It allows you to double-click on a word to bring up translations or English definitions. One more click will take you to a more extensive dictionary page. It also allows you to select a phrase or longer passage and bring up translations generated by Google Translate. It allows you to keep track of the items you have looked up, and to download these on a spreadsheet, which can then be converted to flashcards (e.g. Quizlet) if you wish. If you use the Safari browser, a similar tool is already installed. It has similar features to the Google extension, but also offers you the possibility of linking to examples of the targeted word in web sources like Wikipedia.

Boers was thinking of the provision of hyperlinks, but with these browser extensions it is entirely up to the reader of a text to decide how many and which items to look up, what kind of items (single words, phrases or longer passages) they want to look up, how far they want to explore the information available to them, and what they want to do with the information (e.g. store / record it).

It’s extraordinary that a ‘state-of-the-art article’ in an extremely reputable journal should be so out of date. The value of glossing in language learning is in content-focussed reading, and these tools mean that any text on the web can be glossed. I think this means that further research of the kind that Boers means would be a waste of time and effort. The availability of free technology does not, of course, solve all our problems. Learners will continue to benefit from guidance, support and motivation in selecting appropriate texts to read. They will likely benefit from training in optimal ways of using these browser extensions. They may need help in finding a balance between content-focussed reading and content-focussed reading with a language learning payoff.


Boers, F. (2022). Glossing and vocabulary learning. Language Teaching, 55 (1), 1 – 23

Kim, H.S., Lee, J.H. & Lee, H. (2020). The relative effects of L1 and L2 glosses on L2 learning: A meta-analysis. Language Teaching Research. December 2020.

Nation, I.S.P. (2013). Learning Vocabulary in Another Language. Cambridge: Cambridge University Press

Plonsky, L. & Ziegler, N. (2016). The CALL–SLA interface: insights from a second-order synthesis. Language Learning & Technology 20 / 2: 17 – 37