Your Practical Guide To 2019 AI In Teaching And Learning [Updated]

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AI Competency Roadmap

This is the first of our “A Competency Roadmap to AI” series.

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Let 2019 be the year of Artificial Intelligence for your current workplace. This upcoming series will deal with the practical issues you must consider during the successful introduction of these potentially groundbreaking technologies.

The last thing we want is to fill you with lingo and workload that most likely will not get done. Instead, we want you to see the imminent arrival of AI into your professional live as a new co-worker. She’s a little quirky, but once you get to know her and her language, hopefully she would prove one of the best teammates you’ve had. And when she grows and move to higher places, which she unmistakably will, you will be glad to be acquainted with her.

We intend to make of this a living, breathing series. We want to infuse as many parts of it with real examples of how you, individually, on a team or within your organization, solve problems related to the use of AI in your workflow. Ultimately, we have only one goal: To articulate contributions of the global learning community into a practical and cohesive learning and development roadmap.

To get us started, The following is a quick overview of the issues that will be covered throughout the series. We would love to get your feedback, which will influence the direction the series can take. Do you want supporting materials, templates or videos? Let us know!

1. AI-ssumptions

We will move forward under two basic premises:

  • All learning is digital: Every learning setting, physical or virtual, has access to digital technologies through with participants can take advantage of AI. Learning delivery takes place at least partially online.
  • Complex does not mean inscrutable: We will strive to make every concept or step understandable for people at any stage of their AI learning curve. Worst case scenario, we will refer you to Simple Wikipedia or a similar resource.

2. AI-ngredients

Accross most of the present literature on AI, there seems to be a common “Kleenex Syndrome” for the tools used in AI development and introduction. They jump too quickly into the specific details of a specific technology, rather than explain its purpose and use. Furthermore, they often fall into the trap of being limited to what the specific technology can do, if not what is available in the tutorials. This time, we will focus on the problems, generic names for the tools available, and specific examples only from experiences provided from our own readers.

3. mAIndset

It is likely that many, if not most of the roadblocks for a better implementation of AI across classrooms have to do with the mentalities, and the expectations about it.

Solutions abound, and so do results. In this case, the path is always one: Value to the learning experience. Our series will seek to portray AI as a valuable tool, as quick as possible in the process. We will rely on you to tell us your roadblocks, and we will strive to find real examples in which this was solved. Perhaps possibly working with you on making them true.

Ready to get started? Share a comment or discuss in social media. Stay tuned!


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