An artificial turn in social interaction research?

Jakub Mlynář, Andreas Liesenfeld, Lynn de Renata Topinková, Wyke Stommel, Lynn de Rijk, and Saul Albert for the 6th Copenhagen Multimodality Day: Interacting with AI

The turn towards multimodality and embodiment in interaction research has yielded new terminology and representational schema in key publications (Nevile 2015). At the intersections between multidisciplinary fields, e.g., ethnomethodological and conversation analytic (EMCA) research exploring interactions between humans and ‘AI’, social robots, and conversational user interfaces, such methodological changes are even harder to track. How do these approaches to the meticulous, naturalistic study of technologies in (and of) social interaction reframe the key terms, schema and practices that constitute AI as a field of technosocial activity? Largely grounded in the EMCA Wiki bibliography, we map this emerging field and report on a bibliometric review of 90 publications directly relevant to EMCA studies of AI (broadly defined) including social robots and their components such as voice interfaces.

We found that the most works cited in the EMCA+AI corpus are classics from the canon of human interaction research (Garfinkel, Sacks, Schegloff, Goffman), including multimodality (Goodwin, Heath), human-machine interaction (Suchman), and STS (Latour). The most frequently cited texts are: Sacks, Schegloff and Jefferson’s (1974) ‘turn-taking paper’ (in 45% of items from the corpus), Garfinkel’s (1967) Studies (40%), and Suchman’s (1987) book (31%). Dealing specifically with AI from an EMCA perspective, Porcheron et al.’s 2018 paper on voice user interfaces is the most cited (11%). Apart from this one, two other texts feature as citation hubs: Alač’s (2016) and Pitsch et al.’s (2013) papers on social robots and embodiment. The study aims to provide a starting point for discussion about how concepts such as embodiment, agency and interaction are shared, used and understood through the practice of academic citation.


Nevile, M. (2015). The Embodied Turn in Research on Language and Social Interaction. Research on Language and Social Interaction, 48(2), 121–151.

The interactional coordination of virtual and personal assistants in a homecare setting

Saul Albert, Magnus Hamann & Elizabeth Stokoe (for the 6th Copenhagen Multimodality Day), October 2021.

Policymakers and care service providers are increasingly looking to technological developments in AI and robotics to augment or replace health and social care services in the context of a demographic ageing crisis (House of Lords, 2021; Kingston et al., 2018; Topol, 2019, pp. 54–55). However, there is still little evidence as to how these technologies might be applied to everyday social care situations (Maguire et al., 2021). This paper uses conversation analysis of ~100 hours of video recorded interactions between a disabled person, their virtual assistant (Alexa), and their (human) personal assistant to explore how routine care tasks are organized in a domestic setting. We focus on how the human participants organize conversational turn-space around ‘turns-at-use’ with the virtual assistant. Specifically, how turns-at-use ostensibly designed for the virtual assistant can recruit overhearing others. Further, we show how participants include the virtual assistant in their shared taskscape by, for example, putting ongoing activities and conversations on hold, visibly reorienting their bodies, or explicitly making themselves available for – or requesting – assistance when coordination trouble emerges between the machine-human dyad. Our findings show that virtual assistants expand the affordances of a homecare environment but do not replace the work of personal assistants.


House of Lords. (2021). Ageing: Science, Technology and Healthy Living (p. 132). House of Lords Science and Technology Select Committee.

Kingston, A., Comas-Herrera, A., & Jagger, C. (2018). Forecasting the care needs of the older population in England over the next 20 years: Estimates from the Population Ageing and Care Simulation (PACSim) modelling study. The Lancet Public Health3(9), e447–e455.

Maguire, D., Honeyman, M., Fenney, D., & Jabbal, J. (2021). Shaping the future of digital technology in health and social care. The King’s Fund.

Topol, E. (2019). The Topol Review: Preparing the healthcare workforce to deliver the digital future (p. 103). Health Education England.

Putting wake words to bed

Magnus Hamann and I wrote a provocation paper for the third conference on Conversational User Interfaces 2021.

In it, we argue (hopefully provocatively), that voice user interface designers should stop using wake words like “Alexa” and “Hey Siri” that are crowding each other out of the audible environment of the smart home. Our point is that, as interface elements, wake words are misleading for users who seem to treat them like fully-fledged interactional summons, when they’re really little more than glorified ‘on’ buttons.

We got a surprisingly positive response from the technically-inclined audience at the conference. I found it surprising mostly because wake words are so ubiquitous and central to the branding and functionality of today’s voice interfaces that it seems hard to imagine them being phased out in favour of something more prosaic.

You can read the full paper on the ACM site, or a preprint here.


  1. Charles Goodwin. 2007. Interactive footing. In Reporting Talk, Elizabeth Holt and Rebecca Clift (eds.). Cambridge University Press, Cambridge, 16–46. DOI:
  2. Alexa Hepburn and Galina B Bolden. 2017. Transcribing for social research. Sage, London.
  3. William Housley, Saul Albert, and Elizabeth Stokoe. 2019. Natural Action Processing. In Proceedings of the Halfway to the Future Symposium 2019 (HTTF 2019), Association for Computing Machinery, Nottingham, United Kingdom, 1–4. DOI:
  4. Razan Jaber, Donald McMillan, Jordi Solsona Belenguer, and Barry Brown. 2019. Patterns of gaze in speech agent interaction. In Proceedings of the 1st International Conference on Conversational User Interfaces – CUI ’19, ACM Press, Dublin, Ireland, 1–10. DOI:
  5. Seung-Hee Lee. 2006. Second summonings in Korean telephone conversation openings. Language in Society. 35, 02. DOI:
  6. Gene H Lerner. 2003. Selecting next speaker: The context-sensitive operation of a context-free organization. Language in Society. 32, 02, 177–201. DOI:
  7. Ewa Luger and Abigail Sellen. 2016. “Like Having a Really Bad PA”: The Gulf between User Expectation and Experience of Conversational Agents. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16), Association for Computing Machinery, New York, NY, USA, 5286–5297. DOI:
  8. Robert J. Moore and Raphael Arar. 2019. Conversational UX design: A practitioner’s guide to the natural conversation framework. Association for Computing Machinery, New York, NY, USA.
  9. Clifford Nass and Youngme Moon. 2000. Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues 56, 1 (2000), 81–103. DOI:
  10. Hannah R. M. Pelikan and Mathias Broth. 2016. Why That Nao? In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems – CHI \textquotesingle16, ACM Press. DOI:
  11. Danielle Pillet-Shore. 2018. How to Begin. Research on Language and Social Interaction 51, 3 (July 2018), 213–231. DOI:
  12. Martin Porcheron, Joel E Fischer, Stuart Reeves, and Sarah Sharples. 2018. Voice Interfaces in Everyday Life. In Proceedings of the 2018 ACM Conference on Human Factors in Computing Systems – CHI’18, ACM Press. DOI:
  13. Stuart Reeves, Martin Porcheron, and Joel Fischer. 2018. “This is not what we wanted”: designing for conversation with voice interfaces. Interactions 26, 1, 46–51. DOI:
  14. Harvey Sacks. 1995. Lectures on conversation. Wiley-Blackwell, London.
  15. Emanuel A Schegloff. 1968. Sequencing in Conversational Openings. American Anthropologist 70, 6, 1075–1095. DOI:
  16. Emanuel A Schegloff. 1988. Presequences and indirection: Applying speech act theory to ordinary conversation. Journal of Pragmatics 12, 1 (1988), 55–62.
  17. Emanuel A Schegloff. 2007. Sequence organization in interaction: Volume 1: A primer in conversation analysis. Cambridge University Press, Cambridge.

Digital transcription for EM/CA research

I have put my introduction to digital transcription workshop materials and tutorials online, here’s a little blog outlining some of the reasons I started developing the workshop, and how I hope researchers will use it.

There are very few – if any – software tools designed specifically for conversation analytic transcription, partly because so few conversation analysts use them, so there’s not really a ‘market’ for software developers to cater to.

Instead, we have to make do with tools that were designed for more generic research workflows, and which often build in analytic assumptions, constraints and visual metaphors that don’t necessarily correspond with EM/CA’s methodological priorities.

Nonetheless, most researchers that use digital transcription systems choose between two main paradigms.

  1. the ‘list-of-turns’-type system represents interaction much like a Jeffersonian transcript: a rendering of turn-by-turn talk, line by line, laid out semi-diagrammatically so that lines of overlapping talk are vertically aligned on the page.
  2. the ‘tiers-of-timelines’ system uses a horizontal scrolling timeline like a video editing interface, with multiple layers or ‘tiers’ representing e.g., each participant’s talk, embodied actions, and other types of action annotated over time.


A key utility of both kinds of digital transcription systems is that they allow researchers to align media and transcript, and to use very precise timing tools to check the order and timing of their analytic observations.

I used these terms to describe this distinction between representational schema in a short ‘expert box’ for Alexa Hepburn and Galina Bolden’s excellent (2017) book Transcribing for Social Research entitled “how to choose transcription software for conversation analysis“, where I tried to explain what is at stake in choosing one or the other type of system .

For the most part, researchers choose lists-of-turns tools when their analysis is focused on conversation and audible turn-space, and tiers-of-timelines when their analysis focuses on video analysis of visible bodily action.

The problem for EM/CA researchers working with both these approaches, however, is that neither representational schema on its own, (nor any schema save whatever schema may have been constituted through the original interaction itself), is ideal for exploring and describing participants’ sense-making processes and resources.

Tiers-of-timelines representations are great for showing the temporal unfolding of simultaneous action, but it is hard to read more than a few seconds of activity at a glance. By contrast, lists-of-turns use the same basic schema as our well-practiced, mundane reading abilities to scan a page of text and take in the overall structure of a conversation, but reduce the fine-grained timing and multi-activity organization of complex embodied activities.

In any case, neither of these representational schema, nor any currently available transcription tools adequately capture the dynamics of movement in the way that, for example, specialized graphical methods and life drawing techniques were developed to achieve (although our Drawing Interactions prototype points to some possibilities).

The reason I put this digital transcription workshop together was to combine existing, well-used software tools for digital transcription from both major paradigms, and to show how to work on a piece of data using both approaches. It’s not intended as a comprehensive ‘solution’, and there are many unresolved practical and conceptual issues, but I think it gives researchers the best chance to address their empirical concerns to help break away from the conceptual and disciplinary constraints that come from analyzing data using one, uniform type of user interface.

The workshop materials include slides (so people can use them to teach collaborators/students) as well as a series of short tutorial videos accompanying each practical exercise in the slides, along with some commentary from me.

My hope is that researchers will use and improve these materials, and possibly extend them to include additional tools (e.g., EXMARaLDA project tools, with which I’m less familiar). If you do, and you find ways to improve them with additional tips, hacks, or updated instructions that take into account new versions, please do let me know.

Moving into step: The embodiment of social structures of action

The abstract for a forthcoming article by myself and Dirk vom Lehn, soon to be liberated from the stalled pandemic year R&R cycle. Draft available if you’re willing to give feedback!


While dance has often featured in sociological theory, there are relatively few empirical studies that explore the social practices through which people learn to dance together. This paper takes as its point of departure the way that partner dance is often featured as a metaphor to illustrate theories about social order and interaction. We examine a corpus of video data gathered as part of a day-long workshop and explore how novice dancers learn to perform some of the basic steps of a social dance in time with their partner and with the rhythmical environment. The analysis shows how dancers use rhythm, bodies, language and other resources to organize their social interactions and shows how ethnomethodology and conversation analysis provide a critical standpoint for examining sociological theories about the relationship between the body and the social.  

Keywords: ethnomethodology, conversation analysis, multimodality, dance, culture, 

Three meeting points between CA and AI

I gave this keynote at the first European Conference on Conversation Analysis (ECCA 2020), which, due to C-19, had to be delivered as a video instead of a stand-up talk.

I tried to make a mix between a film essay and a research presentation of work-in-progress, so it didn’t always work to put references on every slide. I’ve added them below with links to the data used where available.


Sacks’ (1963) first published paper on ‘sociological description’ uses the metaphor of a mysterious ‘talking-and-doing’ machine, where researchers from different disciplines come up with incompatible, contradictory descriptions of its functionality. We may soon find ourselves in a similar situation to the one Sacks describes as AI continues to permeate the social sciences, and CA begins to encounter AI either as a research object, as a research tool, or more likely as a pervasive feature of both.

There is now a thriving industry in ‘Conversational AI’ and AI-based tools that claim to emulate or analyse talk, but both the study and use of AI within CA is still unusual. While a growing literature is using CA to study social robotics, voice interfaces, and  conversational user experience design (Pelikan & Broth, 2016; Porcheron et al., 2018), few conversation analysts even use digital tools, let alone the statistical and computational methods that underpin conversational AI. Similarly, researchers and developers of conversational AI rarely cite CA research and have only recently become interested in CA as a possible solution to hard problems in natural language processing (NLP). This situation presents an opportunity for mutual engagement between conversational AI and CA (Housley et al., 2019). To prompt a debate on this issue, I will present three projects that combine AI and CA very differently and discusses the implications and possibilities for combined research programmes.

The first project uses a series of single case analyses to explore recordings in which an advanced conversational AI successfully makes appointments over the phone with a human call-taker. The second revisits debates on using automated speech recognition for CA transcription (Moore, 2015) in light of significant recent advances in AI-based speech-to-text, and includes a live demo of ‘Gailbot’, a Jeffersonian automated transcription system. The third project both uses and studies AI in an applied CA context. Using video analysis, it asks how a disabled man and his care worker interact while using AI-based voice interfaces and a co-designed ‘home automation’ system as part of a domestic routine of waking, eating, and personal care. Data are drawn from a corpus of ~500 hours of video data recorded by the participants using a voice-controlled, AI-based ‘smart security camera’ system.

These three examples of CA’s potential interpretations and uses of AI’s ‘talking-and-doing’ machines provide material for a debate about how CA research programmes might conceptualize AI, and use or combine it with CA in a mutually informative way.

Videos (in order of appearance)

The Senster. (2007, March 29).

MIT AI Lab. (2011, September 25).

Keynote (Google I/O ’18). (2018, May 9).

Online Data

Linguistic Data Consortium. (2013). CABank CallHome English Corpus [Data set]. Talkbank.

Jefferson, G. (2007). CABank English Jefferson NB Corpus [Data set]. TalkBank.


Agre, P. (1997). Toward a critical technical practice: Lessons learned in trying to reform AI. Social Science, Technical Systems and Cooperative Work: Beyond the Great Divide. Erlbaum.

Alač, M., Gluzman, Y., Aflatoun, T., Bari, A., Jing, B., & Mozqueda, G. (2020). How Everyday Interactions with Digital Voice Assistants Resist a Return to the Individual. Evental Aesthetics, 9(1), 51.

Berger, I., Viney, R., & Rae, J. P. (2016). Do continuing states of incipient talk exist? Journal of Pragmatics, 91, 29–44.

Bolden, G. B. (2015). Transcribing as Research: “Manual” Transcription and Conversation Analysis. Research on Language and Social Interaction, 48(3), 276–280.

Brooker, P., Dutton, W., & Mair, M. (2019). The new ghosts in the machine: “Pragmatist” AI and the conceptual perils of anthropomorphic description. Ethnographic Studies, 16, 272–298.

Button, Graham. (1990). Going Up a Blind Alley: Conflating Conversation Analysis and Computational Modelling. In P. Luff, N. Gilbert, & D. Frolich (Eds.), Computers and Conversation (pp. 67–90). Academic Press.

Button, Graham, & Dourish, P. (1996). Technomethodology: Paradoxes and possibilities. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.

Button, G., & Sharrock, W. (1996). Project work: The organisation of collaborative design and development in software engineering. Computer Supported Cooperative Work (CSCW), 5(4), 369–386.

Casino, T., & Freenor, Michael. (2018). An introduction to Google Duplex and natural conversations, Willowtree.

Duca, D. (2019). Who’s disrupting transcription in academia? — SAGE Ocean | Big Data, New Tech, Social Science. SAGE Ocean.

Fischer, J. E., Reeves, S., Porcheron, M., & Sikveland, R. O. (2019). Progressivity for voice interface design. Proceedings of the 1st International Conference on Conversational User Interfaces  – CUI ’19, 1–8.

Garfinkel, H. (1967). Studies in ethnomethodology. Prentice-Hall.

Goodwin, C. (1996). Transparent vision. In E. A. Schegloff & S. A. Thompson (Eds.), Interaction and Grammar (pp. 370–404). Cambridge University Press.

Heath, C., & Luff, P. (1992). Collaboration and control: Crisis management and multimedia technology in London Underground Line Control Rooms. Computer Supported Cooperative Work (CSCW), 1(1–2), 69–94.

Heritage, J. (1984). Garfinkel and ethnomethodology. Polity Press.

Heritage, J. (1988). Explanations as accounts: A conversation analytic perspective. In C. Antaki (Ed.), Analysing Everyday Explanation: A Casebook of Methods (pp. 127–144). Sage Publications.

Hoey, E. M. (2017). Lapse organization in interaction [PhD Thesis, Max Planck Institute for Psycholinguistics, Radbound University, Nijmegen].

Housley, W., Albert, S., & Stokoe, E. (2019). Natural Action Processing. In J. E. Fischer, S. Martindale, M. Porcheron, S. Reeves, & J. Spence (Eds.), Proceedings of the Halfway to the Future Symposium 2019 (pp. 1–4). Association for Computing Machinery.

Kendrick, K. H. (2017). Using Conversation Analysis in the Lab. Research on Language and Social Interaction, 50(1), 1–11.

Lee, S.-H. (2006). Second summonings in Korean telephone conversation openings. Language in Society, 35(02).

Leviathan, Y., & Matias, Y. (2018). Google Duplex: An AI System for Accomplishing Real-World Tasks Over the Phone [Blog]. Google AI Blog.

Local, J., & Walker, G. (2005). Methodological Imperatives for Investigating the Phonetic Organization and Phonological Structures of Spontaneous Speech. Phonetica, 62(2–4), 120–130.

Luff, P., Gilbert, N., & Frolich, D. (Eds.). (1990). Computers and Conversation. Academic Press.

Moore, R. J. (2015). Automated Transcription and Conversation Analysis. Research on Language and Social Interaction, 48(3), 253–270.

Ogden, R. (2015). Data Always Invite Us to Listen Again: Arguments for Mixing Our Methods. Research on Language and Social Interaction, 48(3), 271–275.

O’Leary, D. E. (2019). Google’s Duplex: Pretending to be human. Intelligent Systems in Accounting, Finance and Management, 26(1), 46–53.

Pelikan, H. R. M., & Broth, M. (2016). Why That Nao? Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems – CHI \textquotesingle16.

Pelikan, H. R. M., Broth, M., & Keevallik, L. (2020). “Are You Sad, Cozmo?”: How Humans Make Sense of a Home Robot’s Emotion Displays. Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 461–470.

Porcheron, M., Fischer, J. E., Reeves, S., & Sharples, S. (2018). Voice Interfaces in Everyday Life. Proceedings of the 2018 ACM Conference on Human Factors in Computing Systems (CHI’18).

Reeves, S. (2017). Some conversational challenges of talking with machines. Talking with Conversational Agents in Collaborative Action, Workshop at the 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing.

Relieu, M., Sahin, M., & Francillon, A. (2019). Lenny the bot as a resource for sequential analysis: Exploring the treatment of Next Turn Repair Initiation in the beginnings of unsolicited calls.

Robles, J. S., DiDomenico, S., & Raclaw, J. (2018). Doing being an ordinary technology and social media user. Language & Communication, 60, 150–167.

Sacks, H. (1984). On doing “being ordinary.” In J. Heritage & J. M. Atkinson (Eds.), Structures of social action: Studies in conversation analysis (pp. 413–429). Cambridge University Press.

Sacks, H. (1987). On the preferences for agreement and contiguity in sequences in conversation. In G Button & J. R. Lee (Eds.), Talk and social organization (pp. 54–69). Multilingual Matters.

Sacks, H. (1995a). Lectures on conversation: Vol. II (G. Jefferson, Ed.). Wiley-Blackwell.

Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4), 696–735.

Sahin, M., Relieu, M., & Francillon, A. (2017). Using chatbots against voice spam: Analyzing Lenny’s effectiveness. Proceedings of the Thirteenth Symposium on Usable Privacy and Security, 319–337.

Schegloff, E. A. (1988). On an Actual Virtual Servo-Mechanism for Guessing Bad News: A Single Case Conjecture. Social Problems, 35(4), 442–457.

Schegloff, E. A. (1993). Reflections on Quantification in the Study of Conversation. Research on Language & Social Interaction, 26(1), 99–128.

Schegloff, E. A. (2004). Answering the Phone. In G. H. Lerner (Ed.), Conversation Analysis: Studies from the First Generation (pp. 63–109). John Benjamins Publishing Company.

Schegloff, E. A. (2010). Some Other “Uh(m)s.” Discourse Processes, 47(2), 130–174.

Soltau, H., Saon, G., & Kingsbury, B. (2010). The IBM Attila speech recognition toolkit. 2010 IEEE Spoken Language Technology Workshop, 97–102.

Stivers, T. (2015). Coding Social Interaction: A Heretical Approach in Conversation Analysis? Research on Language and Social Interaction, 48(1), 1–19.

Stokoe, E. (2011). Simulated Interaction and Communication Skills Training: The `Conversation-Analytic Role-Play Method’. In Applied Conversation Analysis (pp. 119–139). Palgrave Macmillan UK.

Stokoe, E. (2013). The (In)Authenticity of Simulated Talk: Comparing Role-Played and Actual Interaction and the Implications for Communication Training. Research on Language & Social Interaction, 46(2), 165–185.

Stokoe, E. (2014). The Conversation Analytic Role-play Method (CARM): A Method for Training Communication Skills as an Alternative to Simulated Role-play. Research on Language and Social Interaction, 47(3), 255–265.

Stokoe, E., Sikveland, R. O., Albert, S., Hamann, M., & Housley, W. (2020). Can humans simulate talking like other humans? Comparing simulated clients to real customers in service inquiries. Discourse Studies, 22(1), 87–109.

Turing, A. (1950). Computing machinery and intelligence. Mind, 49, 433–460.

Walker, G. (2017). Pitch and the Projection of More Talk. Research on Language and Social Interaction, 50(2), 206–225.

Wong, J. C. (2019, May 29). “A white-collar sweatshop”: Google Assistant contractors allege wage theft. The Guardian.

Collecting data from streaming cameras with youtube-dl

I’ve been fascinated by a live camera stream showing a UK street since the start of the lockdown on the 23rd March 2020 because it’s shown how pedestrians interpret the 2m physical distancing rule.

Some of the data from this camera was incorporated into a very nice ROLSI blog post by Eric Laurier, Magnus Hamann and Liz Stokoe that I helped with about the emergence of the ‘social swerve’.

I thought others might find it useful to read a quick how-to about grabbing video from live cameras – it’s a great way to get a quick and dirty bit of data to test a working hunch or do some rough analysis.

There are thousands of live cameras that stream to youtube, but it can be a bit cumbersome to capture more than a few seconds via more straightforward screen capture methods.

NB: before doing this for research purposes, check that doing so is compliant with relevant regional/institutional ethical guidelines.

Step 1: download and configure youtube-dl

Youtube-dl is a command line utility, which means you run it from the terminal window of your operating system of choice – it works fine on any Unix, on Windows or on Mac Os.

Don’t be intimidated if you’ve never used a command line before, you won’t have to do much beyond some copying and pasting.

I can’t do an installation how-to, but there are plenty online:



I’ll assume that if you’re a Unix user, you know how to do this.

Step 2: copy and paste the video ID from the stream

Every Youtube video has a video ID that you can copy from the address bar of your browser. Here’s the one I used for the blog post mentioned above – which we’ve affectionately nicknamed the ‘kebab corpus’. The video ID is circled in red:

Step 3: use youtube-dl to begin gathering your video data

This bit is a little hacky – as in not really using the software as intended or documented, so I’ve created a short howto video. There might be better ways. If so, please let me know!

As I mention in that video – probably best not to leave youtube-dl running for too long on a stream as you might end up losing your video if something happens to interrupt the stream. I’ve captured up to half an hour at a time.

It’s possible to create scripts and automated actions for a variety of operating systems to do this all for you on a schedule – but if you need extensive video archives, I’d recommend contacting the owner of the stream to see if they can simply send you their high quality youtube archives.

How do people with dementia and their carers use Alexa-type devices in the home?

We have a a fully funded PhD position available (deadline 6th March 2020) to work with myself, Prof. Charles Antaki and Prof. Liz Peel in collaboration with The Alzheimer’s Society to explore the opportunities, risks and wider issues surrounding the use of AI-based voice technologies such as the Amazon Echo and home automation systems in the lives of people with dementia.

Voice technologies are often marketed as enabling people’s independence. For example, Amazon’s “Sharing is Caring” advert for its AI-based voice assistant Alexa shows an elderly man being taught to use the ‘remind me’ function of an Amazon Echo smart speaker by his young carer. But how accessible are these technologies in practice? How are people with dementia and carers using them in creative ways to solve everyday access issues? And what are the implications for policy given the consent and privacy issues?

The project will combine micro and macro-levels of analysis and research. On the micro-level, the successful applicant will be trained and/or supported to use video analysis to study how people with dementia collaborate with their assistants to adapt and use voice technologies to solve everyday access issues. On the macro-level, the project will involve working on larger scale operations and policy issues with Ian Mcreath and Hannah Brayford at The Alzheimer’s Society and within the wider Dementia Choices Action Network (#DCAN).

Through this collaboration, the research will influence how new technologies are used, interpreted and integrated into personalised care planning across health, social care and voluntary, community and social enterprise sectors.

The deadline is the 6th March 2020 (see the job ad for application details). All you need to submit for a first round application is a CV and a short form, with a brief personal statement. We welcome applications from people from all backgrounds and levels of research experience (training in specific research methods will be provided where necessary). We especially welcome applications from people with first hand experience of disability and dementia, or with experience of working as a formal or informal carer/personal assistant.

This research will form part of the Adept at Adaptation project, looking at how disabled people adapt consumer AI-based voice technologies to support their independence across a wide range of impairment groups and applied settings.

The successful applicant will be supported through the ESRC Midlands Doctoral Training Partnership, and will have access to a range of highly relevant supervision and training through the Centre for Research in Communication and Culture at Loughborough University.

Feel free to contact me on with any informal inquiries about the post.

Adept at Adaptation

AI and voice technologies in disability and social care

There is a crisis in social care for disabled people, and care providers are turning to AI for high-tech solutions. However, research often focuses on medical interventions rather than on how disabled people adapt technologies and work with their carers to enhance their independence.

This pilot project explores how disabled people adapt consumer voice technologies such as the Amazon Alexa to enhance their personal independence, and the wider opportunities and risks that AI-based voice technologies may present for future social care services.

We are using a Social Action research method to involve disabled people and carers in shaping the research from the outset, and conversation analysis to examine how participants work together using technology (in the broadest sense – including language and social interaction), to solve everyday access issues.

The project team includes myself, Elizabeth Stokoe, Thorsten Gruber, Crispin Coombs , Donald Hislop, and Mark Harrison.


Voice technologies are often marketed as enabling people’s independence.

For example, a 2019 Amazon ad entitled “Morning Ritual” features a young woman with a visual impairment waking up, making coffee, then standing in front of a rain-spattered window while asking Alexa what the weather is like.

Many such adverts, policy reports and human-computer interaction studies suggest that new technologies and the ‘Internet of Things’ will help disabled people gain independence. However, technology-centred approaches often take a medicalized approach to ‘fixing’ individual disabled people, which can stigmatize disabled people by presenting them as ‘broken’, offering high-tech, lab-based solutions over more realistic adaptations.

This project explores how voice technologies are used and understood by elderly and disabled people and their carers in practice. We will use applied conversation analysis – a method designed to show, in procedural detail, how people achieve routine tasks together via language and social interaction.

A simple example: turning off a heater

Here’s a simple example of the kind of process we are interested in.

In the illustration below, Ted, who is about to be hosted out of his bed, gives a command to Alexa to turn off his heater (named ‘blue’) while his carer, Ann moves around his bed, unclipping the wheel locks so she can move it underneath the hoist’s ceiling track. Before Ann can move the bed, she has to put away the heater. Before she can put it away, it must be switched off.

Ann leaves time and space for Ted to use Alexa to participate in their shared activity.

While Ann could easily have switched off the heater herself before moving it out of the way and starting to push the bed towards the hoist, she pauses her activity while Ted re-does his command to Alexa – this time successfully. You can see this sequence of events as it unfolds in the video below.

Here are a few initial observations we can make about this interaction.

Firstly, Ann is clearly working with Ted, waiting for him to finish his part of the collaborative task before continuing with hers. By pausing her action, she supports the independence of his part of their interdependent activity.

Secondly, using conversation analytic transcriptions and the automated activity log of the Amazon device, we can study the sequences of events that lead up to such coordination problems. For example, we can see that when Ted says the ‘wake word’ Alexa, it successfully pauses the music and waits for a command. We can see how Alexa mishears the reference to the heater name ‘blue’, then in lines 7 and 8, it apologizes and gives an account for simply giving up on fulfilling the request and unpausing the music.

Alexa mishears Ted’s reference to the heater ‘blue’, then terminates the request sequence

These moments give us insight both into the conversation design of voice and home automation systems, and also into how interactional practices and jointly coordinated care activities can support people’s independence.

Thanks to

The British Academy/Leverhulme Small Research Grants scheme for funding the pilot project.

The EMCAwiki reached a milestone in 2019

I recently wrote this congratulatory email to the wonderful admins of the Ethnomethodology and Conversation Analysis Wiki ( to congratulate them on reaching a real milestone in this community project. We don’t really have a place to share these things yet so I’m putting it here.

If you are reading this and would like to get involved in the wiki or related projects mentioned here, please drop me an email or message me on Twitter.

Dear Paul and the EMCA wiki team,

I can’t quite believe it’s been six years since we started the EMCAwiki project – when Paul sent out an email via the languse mailing list asking for help with his original EM/CA news site and we began the discussions that led to the lovely bibliography wiki we now run.

Towards the end of 2019 we finally completed the transfer of all remaining legacy bibliography entries to the new wiki format from Paul’s original very long PDF files. We now host a grand total of 8537 entries – from our first entry: (Harold Garfinkel, (1949), “Research Note on Inter- and Intraracial Homicides”, Social Forces, vol. 27, no. 4, pp. 369–381.) – to a host of new papers published as recently as this first week of 2020. We can now begin consolidating and standardizing our work (as Andrei Korbut has been doing brilliantly over the last few months) – making sure things are consistent, and then thinking about how to explore, analyze and share the EM/CA bibliometric data we now have at our disposal. I’ll write more about that below – but this is quite an achievement, and I’m very grateful to all of you, for putting in such incredibly generous and dedicated work.

Before I say anything more or propose any new projects or initiatives, I should say that one of the things I like most about the EM/CA wiki is the almost total lack of administrative overheads. So many things in academic life are bogged down with committees, meetings, action items etc… I love the fact that from the beginning we’ve not really done that, but have mostly just got on with the tasks we thought necessary to the best of our individual and collective abilities. We’ve sometimes made efforts to met up at conferences, which has been fun, and have continued to take the pragmatic approach of just doing what we can when possible without undue pressure or overarching expectations. This is outstanding, and long may it continue.

Having said that, I did take on a new role in 2019 – that of ISCA communications & information officer, and I now participate in more admin meetings than I would usually aim for. These are great fun, and some have included ideas that involve EMCA wiki. I wanted to share some of those ideas with you now, and leave it open to you all to respond (or not) in what is now a time-honored laid-back tradition of the EMCAwiki admins.

Firstly, I am aware that the reason I was elected to the ISCA board was because of this project and all of your work. I would like to acknowledge that publicly by adding a page to the new ISCA website I’m currently developing – aiming to launch it towards the end of January 2020. I have kept a list of admins here: – I like the fact that we have all done different things at different times – and some of us have been more active than others. I hope that continues. If you would really prefer not to be acknowledged for what you’ve done – or what you may do in the future – let me know.

Secondly, I am working with Lucas Seuren and a great group of ECRs from around the world on an exciting new ISCA project. This will draw on the content in the EMCA wiki and promote it to a wider audience, as well as inviting contributions beyond bibliography entries (e.g. lists of up-to-date equipment, cross-cultural ethics frameworks for data recording, shared syllabi, useful youtube videos etc.). I hope that this will contribute positively to the wiki, without increasing any administrative overhead. Of course if any of you would like to contribute to that project too, please let me know.

Thirdly, I am aware that there are lots of features of the wiki that I have long promised to implement – and I have long delayed that implementation. I’ve written many of them down here: There is also a list of ‘known issues’ that have been pointed out as problems over the years: – I’m going to acknowledge now that I doubt I’ll ever have time to implement any of these myself. I have fewer and fewer of the kind of uninterrupted stretches of code hacking-time that are required for software development. Instead, I’m going to try to raise funds to pay professional programmers and systems administrators to do this. I think it’s something I could find a funder to support, and I’ll work on this – with your consent – and (if you have any ideas/time/funders) your involvement and collaboration.

I hope all of that sounds OK. I’m just going to get on with it slowly, and will welcome any thoughts/feedback/initiatives/and ideas that you all have over the next decade.

All the best, and happy 2020,