The ethical dilemma of the robot teacher

The rise of automated teaching technologies

We need to talk about robots.

Specifically, we need to talk about the new generation of AI-driven teaching technologies now entering our schools. These include various ‘autonomous interactive robots’ developed for classroom use in Japan, Taiwan and South Korea. Alongside these physical robots, are the software-based ‘pedagogical agents’ that now provide millions of students withbespoke advice, support and guidance about their learning. Also popular are ‘recommender’ platforms, intelligent tutoring systems and other AI-driven adaptive tutoring – all designed to provide students with personalised planning, tracking, feedback and ‘nudges’. Capturing thousands of data-points for each of its students on a daily basis, vendors such as Knewton can now make a plausible claim to know more about any individual’s learning than their ‘real-life’ teacher ever could.

One of the obvious challenges thrown up by these innovations is the altered role of the human teacher. Such technologies are usually justified as a source of support for teachers, delivering insights that “will empower teachers to decide how best to marshal the various resources at their disposal”. Indeed, these systems, platforms and agents are designed to give learners their undivided attention, spending indefinitely more time interacting with an individual than a human teacher would be able. As a result, it is argued that these technologies can provide classroom teachers with detailed performance indictors and specific insights about their students. AI-driven technology can therefore direct teachers’ attention toward the most needy groups of students – acting as an ‘early warning system’ by pointing out students in most need of personal attention.

On one hand, this might sound like welcome assistance for over-worked teachers. After all, who would not welcome an extra pair of eyes and expert second opinion? Yet rearranging classroom dynamics along these lines prompt a number of questions about the ethics, values and morals of allowing decisions to be made by machines rather than humans. As has been made evident by recent AI-related controversies in healthcare, criminal justice and national elections, the algorithms that power these technologies are not neutral value-free confections. Any algorithm is the result of somebody deciding on a set of complex coded instructions and protocols to be repeatedly followed. Yet in an era of proprietary platforms and impenetrable coding, this logic typically remains imperceptible to most non-specialists. This is why non-specialist commentators sometimes apply the euphemism of ‘secret sauce’ when talking about the algorithms that drive popular search engines, news feeds and content recommendations. Something in these coded recipes seems to hit the spot, but only very few people are ‘in the know’ over the exact nature of these calculations.

This brings us to a crucial point in any consideration of how AI should be used in education.

If implementing an automated system entails following someone else’s logic then, by extension, this also means being subject to their values and politics.

Even the most innocuous logic of [IF X THEN Y] is not a neutral, value-free calculation. Any programmed action along these lines is based on pre-determined understandings of what X and Y is, and what their relation to each other might be. These understandings are shaped by the ideas, ideals and intentions of programmers, as well as the cultures and contexts that these programmers are situated within. So key questions to ask of any AI-driven teaching system include who is now being trusted to program the teaching? Most importantly, what are their values and ideas about education? In implementing any technological system, what choices and decisions are now being pre-programmed into our classrooms?

The ethical dilemma of robot teachers

The complexity of attempting to construct a computational model of any classroom context is echoed in the ‘Ethical Dilemma of the Self-Driving Car’. This test updates a 1960s’ thought experiment known as ‘the Trolley Dilemma’ which posed a simple question: would you deliberately divert a runaway tram to kill one person rather than the five unsuspecting people it is currently hurtling toward? The updated test – popularised by MIT’s ‘Moral Machine’ project – explores human perspectives on the moral judgements made by the machine intelligence underpinning self-driving cars. These hypothetic scenarios involve a self-driving car that is imminently going to crash through a pedestrian crossing. The car can decide to carry on the same side of the road or veer onto an adjacent lane and plough into a different group of pedestrians. Sometimes another option allows the car to self-abort by deciding to swerve into a barrier and sacrifice its passengers.

Unsurprisingly, this third option is very rarely selected by respondents. Few people seem prepared to ride in a driverless car that is programmed to value the lives of others above their own. Instead, people usually prefer to choose one group of bystanders over the other. Contrasting choices in the test might include hitting a homeless man as opposed to a pregnant woman, an overweight teenager or a healthy older couple. These scenarios are complicated further by considering which of these pedestrians is crossing on a green light or jaywalking. These are extreme scenarios, yet neatly illustrate the value-laden nature of any ‘autonomous’ decision. Every machine-based action has consequences and side-effects for sets of ‘users’ and ‘non-users’ alike. Some people gets to benefit from automated decision-making more than others, even when the dilemma relates to more mundane decisions implicit in the day-to-day life of the classroom.

So what might an educational equivalent of this dilemma be? What might the ‘Ethical Dilemma of the Robot Teacher’ look like? Here we might imagine a number of scenarios addressing the question: ‘Which students does the automated system direct the classroom teacher to help?’. For example,

who does the automated system tell the teacher to help first – the struggling girl who rarely attends school and is predicted to fail, or a high-flying ‘top of the class’ boy?

Alternately, what logic should lie behind deciding whether to direct the teacher toward a group of students who are clearly coasting on a particular task, or else a solitary student who seems to be excelling. What if this latter student is in floods of tears? Perhaps there needs to be a third option focused on the well-being of the teacher. For example, what if the teacher decides to ignore her students for once, and instead grab a moment to summon some extra energy?

#1 Who should the robot help next?

#2 Who should the robot help next?

The limits of automated calculations in education

Even these over-simplified scenarios involve deceptively challenging choices, quickly pointing to the complexity of classroom work. Tellingly, most teachers quickly get frustrated when asked to engage in educational versions of the dilemma. Teachers complain that these scenarios seem insultingly simplistic. There are a range of other factors that one needs to know in order to make an informed decision. These might include students’ personalities and home lives, the sort of day that everyone has had so far, the nature of the learning task, the time of academic year, assessment priorities, and so on. In short, teachers quickly complain that their working lives are not this black-and-white, and that their professional decisions are actually based on a wealth of considerations.

This ethical dilemma is a good illustration of the skills and sensitivities that human teachers bring to the classroom setting. Conversely, all the factors that are not included in the dilemma point to the complexity of devising algorithms that might be considered appropriate for a real-life classroom. Of course, many system developers consider themselves well-capable of being able to provide sufficient measurement of thousands (if not millions) of different data-points to capture this complexity. Yet such confidence of quantification quickly diminishes in light of the intangible, ephemeral factors that teachers will often insist should be included in these hypothetical dilemmas. The specific student that a teacher opts to help at any one moment in a classroom can be a split-decision based on intuition, broader contextual knowledge about the individual, as well as a general ‘feel’ for what is going on in the class. There can be a host of counter-intuitive factors that prompt a teacher to go with their gut-feeling rather than what is considered to be professional ‘best practice’.

So, how much of this is it possible (let alone preferable) to attempt to measure and feed into any automated teaching process? A human teacher’s decision to act (or not) is based on professional knowledge and experience, as well as personal empathy and social awareness. Much of this might be intangible, unexplainable and spur-of-the-moment, leaving good teachers trusting their own judgement over what a training manual might suggest that they are ‘supposed’ to do. The ‘dilemmas’ just outlined reflect situations that any human teacher will encounter hundreds of time each day, with each response dependent on the nature of the immediate situation. What other teachers ‘should do’ in similar predicaments is unlikely to be something that can be written down, let alone codified into a set of rules for teaching technologies to follow. What a teacher decides to do in a classroom is often a matter of conscience rather than a matter of computation. These are very significant but incredibly difficult issues to be attempting to ‘engineer’. Developers of AI-driven education need to tread with care. Moreover, teachers need to be more confident in telling technologists what their products are not capable of doing.


The two ‘dilemma’ images were illustrated using graphics designed by Katemangostar / Freepik


Author: Neil Selwyn

Neil Selwyn is a Professor in the Faculty of Education, Monash University and previously Guest Professor at the University of Gothenburg. Neil’s research and teaching focuses on the place of digital media in everyday life, and the sociology of technology (non)use in educational settings.

@neil_selwyn is currently writing a book on the topic of robots, AI and the automation of teaching. Over the next six months he will be posting writing on the topic in various education blogs … hopefully resulting in:  Selwyn, N. (2019)  Should Robots Replace Teachers? Cambridge, Polity

Mässor och konferenser centrala när agendan sätts för skolans digitalisering

Konferenser, skolmässor och sociala medier beskrivs ofta som viktiga och demokratiska mötesplatser för skolan och för lärare. Enligt den bilden kan lärare där göra sina röster hörda, lyfta fram och diskutera relevanta pedagogiska frågor och dela framgångsrika undervisningskoncept och metoder. I en studie från Göteborgs universitet framställs en helt annan bild.

– Forum och event av denna typ skildras ofta som en ny typ av underifrån framväxande folkrörelse som genom mötesplatser kan påverka beslutsfattare och stärka lärarprofessionen, men vad event inom IT-området tillåter i form och utbyte för lärare och skolledare är väldigt begränsat och ensidigt, säger Catarina Player-Koro som tillsammans med Annika Bergviken Rensfeldt och Neil Selwyn står bakom studien.

Mässor, konferenser och sociala medier har för skolan fått ett allt större inflytande över policyfrågorna när det gäller digitalisering. Platser där privata och offentliga aktörer och intressen möts. Här framställer vinstdrivande IT- och utbildningsföretag, teknik- och infrastrukturleverantörer och skolaktörer digital teknik som lösning på ofta komplexa problem i skolan.

I studien har de båda forskarna använt IT-mässan SETT som ett exempel på event som fått stort genomslag. SETT utger sig själv för att vara ”Skandinaviens största mässa och konferens inom det moderna och innovativa lärandet” och arrangeras sedan 2011 årligen i Stockholm.
En rad olika aktörer som vinstdrivande IT- och utbildningsföretag, teknik- och infrastrukturleverantörer men även kommuner och fackförbund samarrangerar mässan. Internationellt finns motsvarande event som BETT, BETT Latin America, EdTechXAsi med flera.

I sin studie har forskarna följt mässan före, under och efter eventet, både genom att besöka eventet, intervjua lärare och analysera mässan i sociala media. Forskarnas huvudsakliga resultat är att SETT och liknande mässor bör ses som en del i ett globalt policynätverk där idéer, teknik och föreläsare av olika slag rör sig över olika länder och sammanhang och därigenom sätter agendan för skolans digitalisering. Ofta gör detta att agendan blir likriktad, ytlig, anpassad till kommersiella intressen snarare än pedagogiska.

– SETT-mässan är en del i en ny typ av policyprocess som vi ser internationellt och som drivs av en ekonomisk agenda. Det problematiska är att en betydande del av policyarbetet, riktat mot skolans innehåll, läroplan, resurser och teknik, sker utanför de vanliga demokratiska forumen för skolbeslut, som klassrum, skolor, kommuner och stat, säger Annika Bergviken Rensfeldt.

Studien beskriver detaljerat villkoren för mässdeltagarna och hur mässan erbjuder en tillrättalagd förmedling av budskap, med liten möjlighet utbyte av kunskap och information lärare emellan. Lärarna på plats har få möjligheter att göra sina röster hörda, att påverka, ifrågasätta eller på andra sätt uttrycka kritiska kommentarer eller inbjudas till längre diskussion. Lärarna på plats visar sig också ha svårt att uppfatta vem som är budbärare för budskapen och teknikföretag, apptillverkare, lärare och forskare kan ha lika stort inflytande över vad man anser värdefullt.

– Kravet om att all undervisning ska vila på vetenskaplig grund och beprövad erfarenhet blir i relation till detta omöjlig för lärare att bedöma och de uppfattar det som att mässan ska förmedla det som de förväntas göra i klassrummet, säger Catarina Player-Koro.

Istället för utbyte av kunskap och information erbjuds budskap i form av korta slogans som lösning på de komplexa utmaningar som finns inom utbildningssystemet. Såväl programmering som inkludering har sin plats, ”Programmering och kodning – tillgängligt för alla”, ”Förbättrade studieresultat”, ”Inkludering för invandrare”. Formen för detta är oftast starka berättelser om framgångsrika metoder och en marknadsplats för säljbara applikationer och metoder. Sällan framställs utmaningar och problem med digital teknik utifrån en skolvardag.

– Eftersom vi idag har öppnat skolan för både privata och offentliga intressen på olika sätt är det också viktigt att undersöka konsekvenserna av det. Om den här typen av IT-mässor är vad lärare erbjuds som fortbildning inom IT i skolan är det väldigt problematiskt. Samtidigt finns det en demokratisk potential i en mer öppen diskussion om IT i skolan, men då måste de offentliga intressena få ett större inflytande över vilka frågor som är viktiga för lärare och skolor, säger Catarina Player-Koro.

Catarina Player-Koro, Annika Bergviken Rensfeldt, Neil Selwyn

Läs artikeln Selling tech to teachers: education trade shows as policy events i Journal of Education Policy: http://www.tandfonline.com/doi/abs/10.1080/02680939.2017.1380232

 

Programmering på schemat (igen) – hur gick det till?

Sedan mars i år finns programmering inskriven i skolans läroplan. Det har lett till febril aktivitet i skolsverige där aktiviteterna nu är många för att omsätta detta till undervisning.

Införandet av programmering ingår i arbetet för en samlad nationell IT-strategi för skolväsendet. Programmeringens intåg gick snabbt och utmärktes av några avgörande händelser från 2013 och framåt. För oss policyforskare var det också en ovanlig, men dock inte unik process internationellt sett och för att få till stånd en utbildningspolicy för skolans digitalisering.

Fortsätt läsa ”Programmering på schemat (igen) – hur gick det till?”