What a piece of work is a man! How noble in reason, how infinite in faculty! In form and moving how express and admirable! In action how like an angel, in apprehension how like a god! The beauty of the world. The paragon of animals. And yet, to me, what is this quintessence of dust?Hamlet Act 2 Scene 2
In 2014, controversy rocked the IATEFL Conference in Harrogate, Yorkshire. Plenary speaker Sugata Mitra, Professor of Educational Technology at Newcastle University, suggested “taking the teacher out of the equation” in favour of computer systems. The response was immediate and fiery. But was this a knee-jerk Luddite reaction to the threat of language teachers going the way of the 19th century British textile worker? Are we to expect the runaway success of trainer-free language learning courses, powered by the monstrous capacity of modern computing?
It is certainly worth remembering Philip Kerr’s admonition that “predictions about the impact of technology on education have a tendency to be made by people with a vested interest in the technologies.” The Brookings Institution (according to The Economist, perhaps the most prestigious think tank in the United States of America), published the following: “The types of jobs that are at the least risk of being replaced by automation involve problem solving, teamwork, critical thinking, communication, and creativity. The education profession is unlikely to see a dramatic drop in demand for employees given the nature of work in this field. Rather, preparing students for the changing labor market will likely be a central challenge for schools and educators.”
Why does this hold true for language learning? The Australian linguist and founder of Systemic Functional Linguistics, Michael Halliday, explains: “First, (language) is transmitted physically, by sound waves traveling through the air; secondly, it is produced and received biologically, by the human brain and its associated organs of speech and hearing; thirdly, it is exchanged socially, in contexts set up and defined by the social structure; and fourthly, it is organized semiotically as a system of meanings.” It is these social structure constraints in terms of what is acceptable in language use and the famously complex semiotic layer involved in determining what people mean, that pose problems for AI systems.
John Searle, the philosopher and mind behind the seminal 1980 Chinese Room thought experiment, wrote as recently as 2010 “Computation is defined purely formally or syntactically, whereas minds have actual mental or semantic contents, and we cannot get from syntactical to the semantic just by having the syntactical operations and nothing else.” A critical implication of the above statement is that without brains we cannot have minds. And without minds we cannot have semantic understanding. I would go further. Without semantic understanding we cannot develop communicative competence in foreign languages. Despite the impressive and exciting advances in technology that we see around us, we will continue to need the exquisitely sophisticated humans that ply their trade in classrooms both traditional and virtual, offices and conference rooms all over this world of ours.
So, what does happen in courses in which there is no human element? Dr Katherine Nielsen of the University of Maryland’s found that professionals employed by the U.S. government were extremely unlikely to persist in using purely self-paced materials. This made it impossible to collect enough data to analyze proficiency gains.
“The most striking finding…was severe attrition in participation. Despite initial participant interest as well as active researcher involvement and encouragement, participants in both phases of the study spent very little time using the CALL (computer assisted language learning) materials before stopping completely, if they used the materials at all.” 
How can we simultaneously acknowledge the overwhelming value of the experienced human trainer, as well as the equally clear advantages of modern educational technology? The answer lies in blended learning. This is the technological route to expanding the faculties of the human trainer in the classroom. Technology in the service of human-led education.
Transhumanism is a class of philosophies of life that seek the continuation and acceleration of the evolution of intelligent life beyond its currently human form and human limitations by means of science and technology, guided by life-promoting principles and values.Max More, philosopher and futurist (1990)
Through learning analytics, the trainer can be fed detailed and relevant information about students’ learning behaviour outside the classroom. How many times did they attempt an exercise? How long did they spend on the most recent unit of content? How many times did they watch a video, or listen to an audio track? What percentage of the class were able to complete the homework? This information can be accessed in real-time by the classroom trainer and used to calibrate the classroom approach. Previously, trainers were blind to this kind of data and had to adapt on the hoof. The cognitive load invested in establishing how comfortable your students are with the content has been lessened. Resources have been freed up.
Similarly, computer-graded exercises reduce the administrative load of trainers, allowing them to focus on complex or nuanced aspects of the course curriculum. No more squinting at rows of letters and comparing with an answer key to grade a lengthy multiple-choice exercise.
Insights can be generated at student, class or cohort level, showing bell-curve distribution of performance on tests, for example. This data can be shown to the trainer just-in-time, unprompted, or on demand.
Simply put, blended learning allows for the integration of machine and human cognition, each playing to their strengths. We are now in a place to combine limitless memory and processing power via cloud computing, with the nuanced interpersonal communicative competence and judgement of the human trainer. If you are interested, as further reading I recommend Satya Basu’s excellent article on what he calls “extended intelligence”. 
Written by Robert Szabó, Director, Pedagogy for Business Language and Communication, Learnship
 Halliday, M in Tincheva, N. 2015. Text Structure: A Window into Discourse, Context and Mind, pp. 12-37, Publisher: Sofia: POLIS Publ., pp.12-37
 Nielsen, K. “Self-study with language learning software in the workplace: what happens?” Language Learning & Technology. October 2011, Volume 15, Number 3 pp. 110–129 http://llt.msu.edu/issues/october2011/nielson.pdf