• June 22, 2017

AI and the Democratisation of Education

Performance Learning Blog - Ed Tech Week London 2017

AI and the Democratisation of Education

Performance Learning founder, Tej Samani, took part in a panel discussion on AI and the Democratisation of Education on 20 June as part of London EdTech Week and EdTechXEurope 2017.

The panelists included Prof Rose Luckin, Alyssa M. Alcorn and Eloise Ainger, Mutlu Cukurova, Tej Samani, Kaśka Porayska-Pomsta and Manolis Mavrikis.

Prof Rose Luckin started the discussion by giving the audience in The Logan Hall at UCL an overview of AI and current perceptions of what it is – beyond the common media portrayals of robots – and what it is capable of doing.

When applied to education, Prof Luckin emphasised that AI cannot understand itself, but can help humans to better understand themselves, and can therefore help educators to better understand themselves, students and peers.

We need to teach students to cope with a changing world, providing them with lifelong learning and self-efficacy.

The possibilities for AI to revolutionise the education industry are profound, particularly when it comes to democratising and making it a fair system for everyone, without the need to rely on traditional exams as a way of assessing ability.

“You can’t stake your lives on a Saviour Machine.”

To paraphrase David Bowie, we can’t use AI to replace teachers (and nor would we want to), but we can use it to revolutionise assessments and open up the black box of learning to build self-efficacy.

With the UK predicted to be 69 million teachers short in 2030 to deliver targets for primary and secondary school education, it’s also becoming vital to supplement traditional teaching with other methods. The key is the effective blending of Artificial and Human Intelligence to design AI assistance and assistants that work alongside educators.

Dr Alyssa M. Alcorn discussed how the De-Enigma project uses a humanoid robot with a realistic face to teach emotion-recognition skills to autistic children. The inherent predictability of robots is a key factor in making this work, as Ms Eloise Ainger demonstrated with ZENO, who works with autistic children aged 5-12 on a range of cognitive and language skills.

Using robots in a meaningful way and integrating them into existing practises and settings.

Again emphasising the key point that AI is not a replacement for human interaction in education, the De-Enigma project has created an intelligent, adaptive robot as an educational tool that works very much in tandem with the educator.

Dr Mutlu Cukurova discussed Project-Based Learning (PBL) and AI, focusing on how AI in education is not limited to virtual learning environments and can help to provide support to every student.

Again the importance of developing the skills of the future workforce came into play, with PBL as an effective way of achieving this – as long as the students are appropriately monitored and not left to their own devices.

As with so many other aspects of education, it is often the final outcome or product that is assessed in PBL, rather than the process the children go through during the session. But it’s not possible for a teacher to effectively monitor every student at all times.

Which is where AI can come in by providing data to optimise PBL environments for every student.

Tej Samani spoke about how Performance Learning is helping to redefine how students are taught. Offering an overview of how much pressure children are currently feeling when it comes to not only their schoolwork – with 74% nervous and worried about school and 86% going to bed after 12.15am – as well as many other aspects of their lives.

The ultimate goal of all educators, and anyone working in the world of education, is to move the student forward. Looking at the child, at how they think and feel must be a vital part of designing any AI that can then react appropriately. So first comes the process of really listening to and understanding the child and how they feel about learning.

No one can work to their best ability if they’re feeling unhappy or insecure – least not the children in our schools who we should be giving every support and chance to read their full potential, no matter who or where they are.

When a system is driven by fear, true democracy cannot manifest itself.

The fear of AI, of change and of adopting a new way of doing things, is a major stumbling block in bridging the gap between humans and AI.

Working closely with everyone – from the student and their parents to the school and it’s teachers – is vital in helping to bridge that gap and make sure that any process or system that incorporates AI or any other digital technologies will be effective.

AI that addresses behaviour before content can be the difference that makes a difference in the classroom.

By rethinking how we monitor learning ability Performance Learning delivers an assessment framework that allows students – as well as parents and teachers – to tell us how they feel.

A sophisticated algorithm then organises the responses into key behaviours and states, e.g. dedication and self-awareness. Again, this reiterates one of the overriding themes of the whole discussion – self-efficacy and developing the future workforce.

Dr Kaśka Porayska-Pomsta from UCL Knowledge Lab discussed democratising education through helping learners know themselves.

Democratisation of education - equality of access, respect for diversity, how our education impacts our environment in the future.

The concept of self-determination and metacognition, a higher order of thinking that involved monitoring and controlling our own cognitive processes (“How good am I at solving algebraic problems?”) and affective processes (“How do I feel about solving algebraic problems?”).

How do I feel – as well as how good am I at something. How emotion overshadows our cognitive processes.

TARDIS is a project that combines the interactive rehearsal of skills with socially credible artificial agents, as well as exploring another agent’s reactions in real time.

Through these types of applications AI can help to give data back to learners and empower them, for example by getting them to inspect, explain or dispute their own interaction behaviours. The point at which they question is the point at which the learning takes place.

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