What’s it about?
The term ‘artificial intelligence’ (AI) was coined in 1956, however recorded history indicates that the notion of AI and attempts to create it have been around for a while. In 1949, Computer scientist Alan Turing, considered to be the father of theoretical computer science and artificial intelligence stated:
“I do not see why it (the machine) should not enter any one of the fields normally covered by the human intellect, and eventually compete on equal terms.”
Since Turing’s observation, artificial intelligence has developed exponentially. As a sub-field of computer science, AI focuses on the development of intelligent machines that work and react like humans. Today, the broad goal of AI is to better support the global workforce and individuals in their daily lives. As teaching and learning increasingly take place in a flexible online environment, AI is becoming an interesting and promising innovation in education.
“The recent developments in AI and machine-learning are a major exception with the potential to revolutionise how young people learn, teachers and tutors teach, and how society drives forward learning in the future.”
What’s driving this?
Current developments in AI indicate how this field may change the learning process and impact on education. AI-based tutors (Intelligent Tutor Systems) are being created to complement in-class teaching, with programmes that:
- analyse interaction data to provide insights into the user behaviour of both teacher and student
- find positive and negative teaching and learning patterns.
Some computer scientists argue that AI will not replace teachers but support them to become better teachers. Many of the aims in the development of AI are to provide platforms that better:
- monitor student responses to warn teachers that extra support is required
- provide realtime feedback on a user’s progress
- inform a teacher’s performance
- assist teachers and schools to create textbooks and exercises that are customised to the needs of their specific courses and students
- work within simulated and gamified virtual reality-type learning experiences that help engage learners
- assist students to adopt productive learning behaviors, such as self-regulation and self-explanation.
What examples of this can I see?
AI has the potential to enhance online learning using adaptive learning software and simulations in ways that more intuitively respond to and engage with students. Today, perhaps the most popular incarnations of AI have materialized in a growing host of virtual assistants, including Alexa, Cortana, and Siri. Chatbots are one form of AI that can potentially support students as personal, virtual tutors that could facilitate more opportunities for real-time interaction and feedback. As the technology becomes more refined, these AI advisors will be better equipped to interpret and respond to the subtleties of linguistics, gestures, and tones that vary in all learners. AI has tremendous potential to enhance creative inquiry and informal learning. The days of learners poring over pages of search results to uncover the most relevant graphic design tutorial or niche scholarly work are numbered, as AI recognizes users based on their previously specified interests and quickly returns fine-tuned data that will be most useful to them.
The implications of AI on education
Reinforcement-learning algorithms, currently being developed as a mechanism of AI are going to get increasingly ‘data smart’. This means that interactive data and statistical techniques will help an educational programme to best ‘reason’ on what is the next best course of action to take for a particular teacher or learner. Computer scientists at Stanford University and the University of Washington are currently collaborating on the creation of a tutoring system that learns from the behaviour of its users and the feedback it receives from people. This informs the programme if its curriculum isn’t enabling all students. In turn, the programme adapts to the learning needs and capabilities of individuals. Learning can be made explicit for both teachers and students as the continuous analysis of interactions helps a teacher understand an individual’s approach and learning style in a given situation. This allows the teacher to adapt their teaching approach to better suit the needs of their student. It also has positive implications for teachers dealing with growing class sizes, yet endeavouring to personalise learning for every student in the classroom. Emma Brunskill, an assistant professor of computer science at Stanford University states:
“Such human-computer collaborations could help students to learn using approaches we can’t yet imagine. This vision of reinforcement learning has artificially intelligent agents redefining what outstanding human performance looks like—and enabling all of us to achieve it.”
The implications of AI on the workforce
AI is and will have a significant impact on the human workforce. In our everyday lives, an example of an area where AI is impacting on a current workforce is in intelligent writing systems – where so many of the articles and reports we read in newspapers and magazines nowadays are actually written with AI. Data-rich areas such as the economy and sports are relatively easy for AIs to write about.
Another development is the evolution of AI enhanced robots This reflects a movement beyond automation; that is simply about replacing tasks that are routine to ‘era three’ of automation; where machines take away the decision-making responsibility of humans.
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A math education company that uses intelligent software to support individual and group learning.
An online math tutoring programme, Third Space has launched an AI project that aims to find positive teaching and learning patterns.
A social network that helps millions of students collaborate, which is exploring the power of AI on it's platform.
Professor Tanja Mitrovic, University of Canterbury
College of Engineering Associate Dean.
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