What is AI & Why it Matters

Updated: Jul 25

Artificial intelligence (AI) will be instrumental in shaping the future of how children interact with each other, their play, the way they experience their education and the societies in which they live. It is imperative to ensure that everything is done to support a responsible use of AI and address risks and challenges which can negatively affect the futures of the education ecosystem.

Artificial intelligence is the creation and application of algorithms to mimic the process of human thought. — Kelly Shiohira

As AI increasingly becomes a part of services and products we use daily, there is a complex convergence impacting the field of education. This applies to educators and the students they serve as well as those vendors they purchase the technologies from and policymakers trying to both protect users and support innovation.

Educators are not only teaching about AI as a subject area of interest, but are employing evolving technologies to improve teaching and learning, support the non-academic needs of students, and operate schools and districts more efficiently for the entire learning community.

The algorithms being employed to make increasingly accurate decisions are only becoming more powerful as datasets expand and complex technologies merge. We can decisively predict that AI in education will escalate as both a topic impacting our work in the profession as well as an area of study for our students.

Educators aren’t waiting for permission from their leaders to do either. With or without official support, teachers and administrators alike are finding tools to help them do their jobs, whether it’s directly tied to student learning or makes processes more efficient and precise such as supporting planning decisions through data analysis. The implementation of AI in education)holds great promise and is already delivering in diverse ways.

There are different and interrelated technologies supporting the development and implementation of AI. Here are several common terms in AI and what they mean:

Machine Learning

Deep Learning

Neural Networks

Natural Language Processing

Computer Vision

Machine Learning (ML) is a general term for technologies that use inputs and/or sensors in devices to “observe” and gather data and respond according to a decision tree called an algorithm. Then to autonomously use new data to train an AI model that continually refines the algorithm’s accuracy in pattern recognition and predictions.

A form of Machine Learning that uses increasingly complex layers of data from artificial Neural Networks to specifically mimic the thought processes of the human mind.

Artificial Neural Networks (ANN) are the means for which Deep Learning evolves. These digital pathways of even seemingly disparate datasets are linked and processed by powerful computers reprocessing new outputs to make new connections.

Originating decades ago in the field of linguistics, Natural Language Processing (NLP) starts with sensors in devices that record human speech and convert it into digital data from existing samples for a variety of outcomes. It also includes machine interpretation of typed or handwritten text to build increasingly complex datasets for training AI models.

If NLP is a machine’s ears, Computer Vision (CV) provides eyesight with optical sensors that can recognize objects, faces, and other common patterns as inputs to an algorithm that translates to a machine’s behavior. And like the other technologies here, informs the model with increasing datasets which continually improve the algorithm’s abilities.

Learning About AI

Though each of the above AI technologies has been evolving for years (and even decades), the mainstream exposure to their capabilities is fairly recent—and even more so in edtech. The inclusion of AI in education services and products can be small, supporting one interaction or situation, or large with algorithms running an entire product. Well known education products already use different components of AI to support their processes in various ways.

Becoming familiar with the vernacular and use cases is something The Alliance is invested in supporting by bringing together all the influencers and their work in one cohesive network. If there are particular sub-topics or examples of AI in the field that you would enjoy learning more about, please notify us at info@edsafeai.org

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