Along with the proliferation of personalized learning platforms also comes the rise of the presence of artificial intelligence in educational technology. Before going further, we must first acknowledge the truth about the term artificial intelligence — it is a buzzword that connotes many different applications. It holds different meaning in different contexts and, much of the time, is used indiscriminately without clear boundaries or specifications.
To try and avoid just adding to the noise, we will focus on three main topics: (1) deconstructing the buzzword into its component parts, (2) characterizing current use cases and predicting future ones, and (3) exploring risks and trade-offs. First, let us define, as clearly as possible, what all is under consideration when we mention the term.
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Deconstructing the Term “Artificial Intelligence”
We’ll begin by separating artificial intelligence into two large, admittedly broad buckets. The first bucket is machine learning.
A.I. Bucket #1: Machine Learning. In a simple sense, machine learning is when large sets of data are used to build and train a “model” (read: algorithm, code, program) that learns on its own, identifies patterns, and makes decisions automatically without intervention. The following terms are either other applications of machine learning or enhancements in the analysis process of machine learning.
Process Enhancements within Machine Learning
Deep Learning. Typically, machine learning goes from large data sets to building models manually to insights. Deep learning is the use of “neural networks” to further automate and facilitate the model building/testing/training part.
- Components of Deep Learning: Neural Networks. Neural networks are layers of algorithms that are designed to automatically recognize patterns in data, rather than be manually trained.
Applications of Machine Learning
Pattern Recognition. Sometimes called data mining or knowledge discovery in databases (KDD), pattern recognition is self-explanatory. Many in industry today mentioning “machine learning” are referring to this application of it.
Natural Language Processing. Natural Language Processing (NLP) is a machine learning application in which the “data” being used to train a model is typically human language in the form of text and/or voice in raw form. This allows data scientists and engineers to train a computer to understand human language in many contexts.
Most of today’s practical applications of A.I. are more specifically machine learning applications. We are already seeing many of these applications of artificial intelligence in the education technology sector as well. However, at the other end of the spectrum of we might call artificial intelligence are a lot of fantastical ideas that lead to even more fantastical futurist implications. We’ll call this second bucket “Robotics and Beyond”.
A.I. Bucket #2: Robotics and Beyond. These terms most likely incorporate some time of machine learning components in their data and coding structures but are much more tangential applications, incorporating the fields of robotics, biology, and physiology, to name a few. For each of the below applications, machine learning is just one input among a broader set used to implement the technology.
Applications of Robotics
Virtual Companions / Chatbots / Robotic Personal Assistants. Artificial human companions are basically any hardware or software that is designed to serve as a companion to others. Naturally, it incorporates machine learning, but the broader implications are in recognizing empathy and human emotion.
Thought-Controlled Gaming. Several advances in neuroscience and virtual reality have made it possible to move objects in a simulation based on information that brain sensors pick up from your mind.
Real-Time Emotion Analytics. Using physiological signals and computer algorithms to detect human emotion in real-time.
…and many more. The above list is by no-means exhaustive.