Artificial Intelligence and teaching

Five thoughtful ways to approach artificial intelligence in schools

The use of artificial intelligence in schools is the best example we have right now of what we call a sociotechnical controversy. As a result f of political interest in using policy and assessment to steer the work that is being done in schools, partly due to technological advances and partly due to the need for digital learning during COVID lockdowns, controversies have emerged regarding edtech and adaptive technologies in schools. 

An emerging challenge for research has been how to approach these controversies that require technical expertise, domain expertise and assessment expertise to fully grasp the complexity of the problem. That’s what we term a ‘sociotechnical controversy’, an issue or problem where one set of expertise is not enough to fully grasp, and therefore respond, to the issue at hand. A sociotechnical problem requires new methods of engagement, because: 

  1. No one set of expertise or experience is able to fully address the multi-faceted aspects of a sociotechnical controversy.
  2. We need to create opportunities for people to come together, to agree and disagree, to share their experience and to understand the limits of what is possible in a given situation. 
  3. We have to be interested in learning from the past to try to understand what is on the horizon, what should be done and who needs to be made aware of that possible future. In other words, we want to be proactive rather than reactive in the policy space.

We are particularly interested in two phenomena seemingly common in Australian education. The first of these concerns policy, and the ways that governments and government authorities make policy decisions and impose them on schools with little time for consideration, resourcing devoted to professional preparation and awareness of possible unintended consequences. Second, there tends to be a reactive rather than proactive posture with regard to emerging technologies and potential impacts on schools and classrooms. 

This particularly pertains to artificial intelligence (AI) in education, in which sides tend to be drawn over those who proselytize about the benefits of education technology, and those worried about robots replacing teachers. In our minds, the problem of AI in education could be usefully addressed through a focus on the controversy in 2018 regarding the use of automated essay scoring technology, that uses AI, to mark NAPLAN writing assessments. Our focus on this example was because it crystallised much about how AI in schools is understood, is likely to be used in the future and how the impacts that it could have on the profession. 

On July 26 2022, 19 academic and non-academic stakeholders, including psychometricians, policy scholars, teachers, union leaders, system leaders, and computer scientists, gathered at the University of Sydney to discuss the use of Automated Essay Scoring (AES) in education, especially in primary and secondary schooling. The combined expertise of this group spanned: digital assessment, innovation, teaching, psychometrics, policy, assessment, privatisation, learning analytics, data science, automated essay feedback, participatory methodologies, and emerging technologies (including artificial intelligence and machine learning). The workshop adopted a technical democracy approach which aimed not at consensus but productive dialogue through tension. We collectively experimented with AES tools and importantly heard from the profession regarding what they knew would be challenges posed by AES for schools and teachers. Our view was that as AI, and tools such as AES are not going away and are already widely used in countries like the United States, planning for its future use is essential. The group also reinforced that any use of AI in schools should be rolled out in such a way as to place those making decisions in schools, professional educators, at the centre of the process. AI and AES will only be of benefit when they support the profession rather than seek to replace it..

Ultimately, we suggested five key recommendations.

  1. Time and resources have to be devoted to helping professionals understand, and scrutinise, the AI tools being used in their context. 
  2. There needs to be equity in infrastructure and institutional resourcing to enable all institutions the opportunity to engage with the AI tools they see as necessary. We cannot expect individual schools and teachers to overcome inequitable infrastructure such as funding, availability of internet and access to computational hardware. 
  3. Systems that are thinking of using AI tools in schools must prioritise Professional Learning opportunities well in advance of the rollout of any AI tools. This should be not be on top of an already time-poor 
  4. Opportunities need to be created to enable all stakeholders to participate in decision-making regarding AI in schools. It should never be something that is done to schools, but rather supports the work they are doing.
  5. Policy frameworks and communities need to be created that guide how to procure AI tools, when to use AI, how to use AI why schools might choose not to use AI in particular circumstances. 

From working with diverse stakeholders it became clear that the introduction of AES in education should always work to reprofessionalise teaching and must be informed by multiple stakeholder expertise. These discussions should not only include policymakers and ministers across state, territory, and national jurisdictions but must recognise and incorporate the expertise of educators in classrooms and schools. A cooperative process would ensure that diverse stakeholder expertise is integrated across education sector innovation and reforms, such as future AES developments. Educators, policymakers, and EdTech companies must work together to frame the use of AES in schools as it is likely that AES will be adopted over time. There is an opportunity for Australia to lead the way in the collective development of AES guidance, policy, and regulation. 

Link to whitepaper & policy brief. https://www.sydney.edu.au/arts/our-research/centres-institutes-and-groups/sydney-social-sciences-and-humanities-advanced-research-centre/research.html

Greg Thompson is a professor in the Faculty of Creative Industries, Education & Social Justice at the Queensland University of Technology. His research focuses on the philosophy of education and educational theory. He is also interested in education policy, and the philosophy/sociology of education assessment and measurement with a focus on large-scale testing and learning analytics/big data.

Kalervo Gulson is an Australian Research Council Future Fellow (2019-2022). His current research program looks at education governance and policy futures and the life and computing sciences. It investigates whether new knowledge, methods and technologies from life and computing sciences, with a specific focus on Artificial Intelligence, will substantively alter education policy and governance.

Teresa Swist is Senior Research Officer with the Education Futures Studio and Research Associate for the Developing Teachers’ Interdisciplinary Expertise project at the University of Sydney. Her research interests span participatory methodologies, knowledge practices, and emerging technologies. She has a particular interest in how people with diverse expertise can generate ideas, tools, and processes for collective learning and socio-technical change.

Six reasons Artificial Intelligence technology will never take over from human teachers

The next twenty years will see teachers under increasing pressure to convincingly justify their existence. Advances in artificial intelligence (AI) technologies are already prompting calls for teaching to be automated, learner-driven and ‘teacher-proof’. While these technologies might still require non-specialised classroom facilitators and technicians, the role of the highly trained expert teacher is coming under increasing threat. There is a growing sense that “we don’t really need teachers in the same way anymore”.

Put bluntly, the entire premise of ‘the teaching profession’ faces an impending challenge. In a future where education can be reliably provided by machines, why continue to invest millions of dollars in training human experts to do the job? Given the likely trajectory of technological developments over the next few decades, is there anything that an expert teacher does that machines will never be able to do? As an education researcher and teacher, I would like to think that there is! Here, then, are six aspects of expert human teaching which are getting overlooked in the current rush toward automating the classroom:

1. Human teachers have learned what they know

There is clear benefit from being with someone who can pass on knowledge, especially someone who has previously been in the position of having to learn that knowledge. This latter qualification is a uniquely human characteristic. When a learner learns with an expert teacher, they are not simply gaining access to the teachers’ knowledge but also benefiting from the teachers’ memories of learning it themselves. Technology can be pre-loaded with content of what is to be learned. Yet, no AI technology is going to ‘learn’ something exactly the way a human learns it, and then help another human learn accordingly.

2. Human teachers make cognitive connections

A human is uniquely placed to sense what another human is cognitively experiencing at any moment, and respond accordingly. In this sense, face-to-face contact with a teacher offer learners a valuable opportunity to engage in the process of thinking withanother human brain. On one hand, there is something thrilling about witnessing an expert who is modelling the process of thinking things through. Conversely, a human teacher is also able to make a personal ‘cognitive connection’ with another individual who is attempting to learn. As David Cohen puts it, teachers are uniquely able to “put themselves into learners’ mental shoes”. Despite the best efforts of computer science, many aspects of thinking cannotbe detected and modelled by machines in this way.

3. Human teachers make social connections

Teaching is a mutual obligation between teachers and learners. No teacher can stimulate the learning process without the cooperation of those who are learning. Good teachers make personal connections with their students, helping them gauge what might work best at any particular time. Before attempting to intellectually engage with a group, teachers will “take a mental pulse of students’ demeanours”. Teachers work hard to establish this mutual commitment to learning, as well as sustaining engagement through motivating, cajoling and enthusing individuals. All of these are interpersonal skills that come naturally to people rather than machines.

4. Human teachers talk out loud

There is something transformative about being in the presence of an expert teacher talking about their subject of expertise. Listening to an expert talk can provide a real-time, unfolding connection with knowledge. A good speaker does not stick rigidly to a written text, but refines, augments and alters their argument according to the audience reactions. A teacher speaking to a group of learners therefore engages in a form of spontaneous revelation. Key to this is the teacher’s role in leading and supporting learners to engage in active listening. As Gert Biestareasons, being addressed by another person interrupts one’s ego-centricism – drawing an individual out of themselves and forcing them into sense-making.

5. Human teachers perform with their bodies

The bodies of human teachers are an invaluable resource when engaging learners in abstract thought. Teachers use their bodies to energize, orchestrate and anchor the performance of teaching. Many subtleties of teaching take place through movement – pacing around a room, pointing and gesturing. Teachers make use of their ‘expressive body’ –  lowering their voice, raising an eyebrow or directing their gaze. Crucially, a human will respond to the living biological body of another human in a completely different way to even the most realisticsimulation. Being looked in the eye by another person is a qualitatively different experience than being looked at by a 3D humanoid robot, let alone a 2D cartoon agent on a screen.

6. Human teachers improvise and ‘make do’

A key part of good teaching is the human capacity to improvise. Rather than sticking tightly to a pre-planned script, teachers will adjust what they do according to the circumstances. Like most performative events, teachers approach a session with a rough plan or structure. However, thereafter they improvise their way around these aims and objectives. Teaching requires acts of creativity, innovation and spontaneity – akin to dancing or playing jazz. Teachers and learners feel each other out, find common ground and build upon it. Teaching also demands a tolerance for imprecision, messiness and not knowing. Most human actions involve a degree of guesswork, bluff and willingness to ‘make do. These are processes that computer systems are largely incapable of.

As these examples illustrate, an expert human teacheris able to support learning in ways that can never be fully replicated through technology. Unfortunately, these qualities remain largely unrecognised, even by teachers themselves. Many educators consider teaching to be an ‘unconscious’ act that is difficult to pin down and articulate. Yet such coyness does little to dispel the technology-driven arguments currently being made against the teaching profession. Teachers need to speak up and make an irrefutable case for the continued presence of expert professionals at the forefront of classrooms.

So how can we rehabilitate human teachers in the minds of their detractors? The uphill battle in countries like Australia is to revitalise schools and classrooms to allow teachers to work in the ways just outlined. These are all characteristics that a good teacher should have, but are considerably restricted in an era of ‘teaching out-of-field’, templated lesson plans and rigid standardised testing.

A first step in this direction might be to alter the ways that people think and talk about teaching. Teachers need to speak forcibly about these qualities – amongst themselves, within their professional associations, withparents, politicians, pundits and anyone else with influence. Teachers also need to argue directly against the tech industry and corporate reformers looking to replace them with machines. There is obvious value in the human expert teacher. Yet unless teachers are able to make a convincing case, they may well lose the argument before they even realise that there was one.

 

Neil Selwyn is a professor in the Faculty of Education, Monash University (Australia). He previously worked in the UCL Institute of Education, and Cardiff School of Social Science (UK). Neil 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, hopefully resulting in: Selwyn,  N. (2019) Should Robots Replace Teachers? Cambridge, Polity.

Neil can be found on Twitter @neil_selwyn