Artificial Intelligence and Learning Environments

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Siri manages our calendars, Facebook suggests our friends, Computers trade our stocks.

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We have cars that park themselves, and air traffic control is almost fully automated. So, virtually every field has benefited from advances in artificial intelligence.

Artificial intelligence and education

However, almost none of the recent advancements in artificial intelligence have led to an advancement in the education industry. Since evolution, learning has been a vital part of our life. We, humans, are the most powerful species on this planet just because we have been able to learn from the surroundings and adapt to the changes. There have been huge amounts of changes in the systems around us over the time from traditions, communities, classes, government, technology and to everything.

The change itself has become so rapid that things get outdated within a few days.

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The system which has carried us to where we are today, is itself outdated to take us forward at the required pace. The education sector is so neglected in this era of advancement that we have forgotten to look back and see the difference between a 20th-century classroom and the one from today. This situation has to be addressed and the education system needs to be advanced enough to carry the present and future generation with the required pace. If we drop our preconceived sci-fi image of AI developed from Hollywood and land ourselves into the present, that will help us better in predicting what role AI can play in enhancing the experience of learning.

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Artificial intelligence and education - EduTech Wiki

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Artificial Intelligence AI makes it possible for machines to learn from experience, adjust to new inputs, and perform human-like tasks. Taking these cues, AI can also be applied to learning. In this article, I will be sharing my views about the ways in which AI can be used in learning. Listen to the audio version Continue listening Pause Stop. Background vector created by Freepik. As paradoxically as that sounds, even the most experienced AI experts have been guilty of rushing into proposing deep learning algorithms and exoteric optimization techniques without fully understanding the problem at hand.

When we think about an AI problem, we tend to link our reasoning to two main aspects: datasets and models. However, that reasoning is ignoring what can be considered the most challenging aspect of an AI problem: the environment. When designing artificial intelligence AI solutions, we spend a lot of time focusing on aspects such as the structure of learning algorithms [ex: supervised, unsupervised, semi-supervised], the architecture of a neural network [ex: convolutional, recurrent…] or the characteristics of the data [ex: labeled, unlabeled…].

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However, little attention is often provided to the nature of the environment on which the AI solution operates. As it turns out, the characteristics of the environment are the number one element that can make or break an AI model. There are several aspects that distinguish AI environments. The shape and frequency of the data, the nature of the problem , the volume of knowledge available at any given time are some of the elements that differentiate one type of AI environment from another.

Deep diving into those characteristics will guide the strategies of AI experts in areas such as algorithm selections, neural network architectures, optimization techniques and many other relevant aspects of the lifecycle of AI applications. Understanding an AI environment is an incredibly complex task but there are several key dimensions that provide clarity on that reasoning.

One of the most effective methodologies for understanding an AI environment is to classify it across a series of well-known dimensions that are often only segmented in two or three classifications. Among the different characteristics that can be used to classify an AI environment, there are seven key exclusive dynamics that provide a rapid understanding of the challenges and capabilities needed by AI agents. One of the most obvious dimensions to classify and AI environment is based on the number of agents involved.

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  • The vast majority of AI models today focus on environments involving a single agent but there is an increasing expansion in multi-agent settings. The introduction of multiple agents in an AI problem raises challenges such as collaborative or competitive dynamics which are not present in single-agent environments. Complete AI environments are those on which, at any given time, the agents have enough information to complete a branch of the problem. Chess is a classic example of a complete AI environment.

    Most of the famous Nash equilibrium principles are particularly relevant in incomplete AI environments.

    A fully observable AI environment has access to all required information to complete target task. Image recognition operates in fully observable domains.

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