AI Applications in Higher Education

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AI Applications means, the different kinds of applications that currently exist for artificial intelligence in higher education. First, as I’ve discussed above, is institutional use. Schools, particularly in higher education, increasingly rely on algorithms for marketing to prospective students, estimating class size, planning curricula, and allocating resources such as financial aid and facilities.

This leads to another AI application, student support, which is a growing use in higher education institutions. Schools utilize machine learning in student guidance. Some applications help students automatically schedule their course load. Others recommend courses, majors, and career paths – as is traditionally done by guidance counselors or career services offices. These tools make recommendations based on how students with similar data profiles performed in the past. For example, for students who are struggling with chemistry, the tools may steer them away from a pre-med major, or they may suggest data visualization to a visual artist.

Another area for AI use in student support is just-in-time financial aid. Higher education institutions can use data about students to give them microloans or advances at the last minute if they need the money to, for example, get to the end of the semester and not drop out. Finally, one of the most prominent ways that predictive analytics is being used in student support is for early warning systems, analyzing a wide array of data – academic, nonacademic, operational to identify students who are at risk of failing or dropping out or having mental health issues. This particular use shows some of the real advantages of artificial intelligence – big data can give eduators more holistic insight into students’ status. Traditionally, an institution might use a couple of blunt factors – for example, GPA or attendance – to assess whether a student is at risk. AI software systems can use much more granular patterns of information and student behavior for real-time, up-to-the-minute assessment of student risk. Some even incorporate information such as when a student stops going to the cafeteria for lunch. They can include data on whether students visit the library or a gym and when they use school services. Yet while these systems may help streamline success, they also raise important concerns about student privacy and autonomy, as I discuss below.

Lastly, colleges and universities can apply artificial intelligence in instruction. This involves creating systems that respond to individual users’ pace and progress. Educational software assesses students’ progress and recommends, or automatically delivers, specific parts of a course for students to review or additional resources to consult. There are often called “personalized learning” platforms. I put this phrase in quotation marks because it has been sucked into the hype machine, with minimal consense about what personalized learning actually means. Here I’m using the phrase to talk about the different ways that instructional platforms, typically those used in a flipped or online or blended environment, can automatically help users tailor different pathways or provide them with feedback according to the particular error they make. Learning science researchers can put this information to long-term use by observing what pedagogical approaches, curricula, or interventions work best for which types of students.

Artificial Intelligence in Higher Education

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What is artificial intelligence? In any discussion of artificial intelligence (AI), this is almost always the first question. The subject is highly debated, and I won’t go into the deep technical issues here. But I’m also starting with this question because the numerous myths and misconceptions about what artificial intelligence is, and how it works, make considering its use seem overly complex.

When people think about artificial intelligence, what often comes to mind is The Terminator movies. But today we are far from machines that have the ability to perform the myriad of tasks even babies shift between with ease – although how far away is a matter of considerable debate. Today’s artificial intelligence isn’t general, but narrow. It is task-specific. Consider the computer program that infamously beat the world’s champion in the Chinese game Go. It would be completely befuddled if someone added an extra row to the playing board. Changing a single pixel can throw off image-recognition systems.

Broadly, artificial intelligence is the attempt to create machines that can do things previously possible only through human cognition. Computer scientists have tried many different mechanisms over the years. In the last wave of AI enthusiasm, technologists tried emulate human knowledge by programming extensive rules into computers, a technique called expert systems. Today’s artificial intelligence is based on machine learning. It is about finding patterns in seas of data – correlations that would not be immediately intuitive or comprehensible to humans – and then using those patterns to make decisions. With “predictive analytics,” data scientists use past patterns to guess what is likely to happen, or how an individual will act, in the future.

All of us have been interacting with this type of artificial intelligence for years. Machine learning has been used to create GPS systems, to make translation and voice recognition much more precise, to produce visual digital tools that have facial recognition or filters that create crazy effects on Snapchat or Instagram. Amazon uses artificial intelligence to recommend books, Spotify uses machine learning to recommend songs, and schools use the same techniques to shape students’ academic trajectories.

Fortunately or not, depending on one’s point of view – we’re not at the point where humanoid robot teachers stand at the front of class. The use of artificial intelligence in education today is not embodied, as the roboticists call it. It may have physical components, like internet of things (IoT) visual or audio sensors that can collect sensory data. Primarily, however, educational artificial intelligence is housed in two-dimensional software-processing systems. This is perhaps a little less exciting, but it is infinitely more manageable than the issues that arise with 3-D robots.

In January 2019, the Wall Street Journal published an article with a very provocative title: “Colleges Mine Data on Their Applicants.” The article discussed how some colleges and universities are using machine learning to infer prospective students’ level of interest in attending their institution. Complex analytic systems calculate individuals’ “demonstrated interest” by tracking their interactions with institutional websites, social media posts, and emails. For example, the schools monitor how quickly recipients open emails and whether they click on included links. Seton Hall University utilizes only about 80 variables. A large software company, in contrast, offers schools dashboards that “summarize thousands of data points on each student.” Colleges and universities use these “enrollment analytics” in determining which students to reach out to, what aspects of campus life they should emphasize, and assessing admissions applications.

Brain Science and Problem Solving

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Through research of intelligent systems we can try to understand how the human brain works and then model or simulate it on the computer. Many ideas and principles in the field of neural networks stem from brain science with the related field of neuroscience.

A very different approach results from taking a goal-oriented line of action, starting from a problem and trying to find the most optimal solution. How humans solve the problem is treated as unimportant here. The method, in this approach, is secondary. First and foremost is the optimal intelligent solution to the problem. Rather than employing a fixed method (such as, for example, predicate logic) AI has as its constant goal the creation of intelligent agents for as many different tasks as possible. Because the tasks may be very different, it is unsurprising that the methods currently employed in AI are often also quite different. Similar to medicine, which encompasses many different, often life-saving diagnostic and therapy procedures. Just as in medicine, there is no universal method for all application areas, rather a great number of possible solutions for the great number of various everyday problems, big and small.

Cognitive science is devoted to research into human thinking at a somewhat higher level. Similarly to brain science, this field furnishes practical AI with many important ideas. On the other hand, algorithms and implementations lead to further important conclusions about how human reasoning functions. Thus these three fields benefit from a fruitful interdisciplinary exchange. The subject of this book, however, is primarily problem-oriented AI as a sub discipline of computer science.

There are many interesting philosophical questions surrounding intelligence and artificial intelligence. We humans have consciousness; that is, we can think about ourselves and even ponder that we are able to think about ourselves. How does consciousness come to be? Many philosophers and neurologists now believe that the mind and consciousness are linked with matter, that is, with the brain. The question of whether machines could one day have a mind or consciousness could at some point in the future become relevant. The mind-body problem in particular concerns whether or not the mind is bound to the body.

What are Artificial Neural Networks (ANNs)?

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The inventor of the first neurocomputer, Dr. Robert Hecht-Nielsen, defines a neural network as:

“a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.”

Basic Structure of ANNs

The idea of ANNs is based on the belief that working of human brain by making the right connections, can be imitated using silicon and wires as living neurons and dendrites.

The human brain is composed of 86 billion nerve cells called neurons. They are connected to other thousand cells by Axons. Stimuli from external environment or inputs from sensory organs are accepted by dendrites. These inputs create electric impulses, which quickly travel through the neural network. A neuron can then send the message to other neuron to handle the issue or does not send it forward.

ANNs are composed of multiple nodes, which imitate biological neurons of human brain. The neurons are connected by links and they interact with each other. The nodes can take input data and perform simple operations on the data. The result of these operations is passed to other neurons. The output at each node is called its activation or node value.

Each link is associated with weight. ANNs are capable of learning, which takes place by altering weight values. The following illustration shows a simple ANN.

Types of Artificial Neural Networks

There are two Artificial Neural Network topologies − FeedForward and Feedback.

FeedForward ANN

In this ANN, the information flow is unidirectional. A unit sends information to other unit from which it does not receive any information. There are no feedback loops. They are used in pattern generation/recognition/classification. They have fixed inputs and outputs.

FeedBack ANN

Here, feedback loops are allowed. They are used in content addressable memories.

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