University of Manchester, Manchester Institute of Education, MA Digital Technologies and Communication in Education.
Artificial Intelligence (A.I.) is a huge inter disciplinary field of research drawing on Computer Science, Psychology, Neuroscience, Cognitive Science, Physics and Philosophy of Mind. The EDUC72142 unit explores theories of learning and teaching that are informed by the history and development of A.I., to expand our understanding of what it means to learn, as well as to consider the impact of the increasing use of A.I. in classrooms and other educational contexts. This includes work in social and developmental robotics.
This unit is a unit of breadth – we cover many topics as the field is so broad. The aim is to challenge your existing views of what A.I. is, what it can contribute to education and learning and to act as a map and a compass for further explorations into this exciting and rapidly changing landscape.
This page describes what the course covers week by week, with details of how the unit is assessed. Each week the unit starts by considering some movie clips that present a particular view of A.I. and a core reading from Learning Re-Imagined, by Graham Brown-Martin. This book has case studies from all over the world about technology use in education, that act as provocations to think about how A.I. might automate or transform institutional and informal learning. A.I. is the computational modelling or representation of human intelligence and each week we use a practical tool that makes concrete some of the theories, concepts and tools for thought that we explore.
In week 1 we introduce the course unit and some fundamentals of computational representation. This is not a computer science course, we won’t be learning to be programmers, but we do explore some of the principles of computational abstraction on which A.I. developments are founded. This week we explore the idea of ‘levels’ of representation, emergence of complexity and we play with simulations such as this: https://traffic-simulation.de/
One of the key readings for this week is: Wilensky, U., and Resnick, M. (1999). Thinking in Levels: A Dynamic Systems Approach to Making Sense of the World. Journal of Science Education and Technology, vol. 8, no. 1, pp. 3-19.
In week 2 we start to explore Seymour Papert’s work. Papert saw computation as a new literacy that provided an alternative way to represent ideas. He worked with Jean Piaget early in his academic career, and later worked with Marvin Minsky in A.I.. An introduction to his book Mindstorms can be found here: http://www.papert.org/articles/GearsOfMyChildhood.html
This week we start to learn how to use a tool inspired by Papert’s work, Scratch, to understand some basic computational principles and develop code literacy.
The notion of ‘interface’ becomes arguably more complex when we move from exploring humans interacting with a screen to humans interacting with robots. This week we will look at some fundamental aspects of exploring interactions with screens, how those screens are designed and how we store the data being displayed on a screen. In later weeks we consider how these aspects are evolving into human-robot relations and how these evolutions are beginning to change learning and education.
A key reading from this week is Dr. Simon Harper’s UX from 30,000 ft
This week we get physical! In some ways this is the lowest level of computation at which we will work in the unit, because we’re programmatically controlling an electronic device, Codebug. We use the Codebug web based interface to explore how using this kind of coding to control a device is different, or not, to an on screen representation. This will inform our explorations of embodied A.I. and embodied learning through robotics, later in the unit.
This week we are exploring games, gamification and play for learning. In doing so we will also include object oriented programming, using Java and Discourse analysis on our journey. The connections between these might not be obvious at first, but they will emerge as you read and play with our tool for the practical this week, Greenfoot.
Why are we talking about play in an AI course? When we come to robotics and consider how this expanding field could be used in education and learning, what we believe a robot can and cannot do will be central to how we think about these potentialities. Ontologies of play, imagination and improvisation, the nature of what these terms refer to, help us question our own thinking and to critically engage with debates around the development of embodied A.I.s for education. So this week we play, create games to play and talk about play.
In week six, having covered a lot of ground already, we have some time to reflect and work on the concept map stories that are the first assessed task in the unit (see Assessments below). There are still lots of materials to explore something new. Starting from the perspective of The Matrix, we look at ‘low level’ programming with machine code, and computation and maths as ways of representing the world at, arguably, the most abstract level. We start to explore what we mean by abstraction, and relate this to Papert’s ideas of the computer as supporting learning through its position ‘betwixt and between’ the concrete and the abstract.
Artificial intelligence can be defined in many different ways and has many different facets, but these are all related by the attempts to understand and emulate human intelligence. As with all computational investigations, the attempt to model something computationally, helps us to think about what intelligence is and what it is not, what learning is and what it is not. So in addition to all the fields that have developed within A.I. that focus on specific techniques – text mining; neural nets; machine learning; natural language processing robotics; etc – there is also parallel work on the philosophy of mind and knowledge that has helped explore questions such as: What is consciousness? What is knowledge? How do we represent knowledge in order to communicate it? How does our knowledge develop? These questions are also central to the practices of learning and teaching.
This week we explore chatbots, starting with the ‘original’, ElizaBot.
This week we explore the emerging field of social robotics. We look at what robots are, our understandings of robots and ourselves through the mind-as-machine-metaphor. As robots are increasingly used in health care and medical fields, we explore the pragmatics, theories, discourses and ethics of robot development and use to help us consider how this might be important as robots become more common place in learning and education. We play with Edbots and circumstances allowing, Nao robots.
We also explore other robots such as Sophia, and use our code literacy skills to read and understand the aiml files – this leads us to explore issues of bias in A.I. code, and emerging work in the field such as the Algorithmic Justice League.
This week we look at work that models human learning on robots both to represent and understand how a machine can learn and to inform developmental psychology. We also see how robots are being explored in acting as tutors to young children in classrooms and how children are learning through teaching robots.
This week is the last week of content to explore as for the final two weeks we will focus on designing and building artefacts for the second assignment. We bring together the concepts, the tools for thought and the practical tools that we have used throughout the unit to think about what ethical issues need to be considered as A.I. becomes increasingly embedded into education. We will consider not only how A.I. tools should support education and how they model and inform our understandings of learning, but whether we should explicitly teach A.I. concepts in schools and if so, how we might best go about doing so.
Week 11 and 12
In the last two weeks of the unit we spend time learning through making, using one of the tools introduced throughout the unit. Students make a digital artefact about A.I. and evaluate it using a method called Think Aloud Protocol (TAP). This journey of making and evaluating is then an experience that students draw on for the second assessed task
This unit explores the process of learning, not working towards fixed or static outcomes. The assessments are then designed to do exactly this. The first assessment is a concept map story, using a tool that captures the development over time of a concept map, representing student perspectives on A.I. This work is done over the first nine weeks of the unit. Below is an example of a concept map story:
In the second assessed task, students write a reflective report on their experience of learning through making during the last two weeks of the unit, drawing on all the theories, concepts and tools for thought that the unit has introduced.