Although the title may lead some to believe I'm straying off topic here, this is an excellent U of A resource that seep deep connections to education. The PowerPoint document by Dr. Osmar Zaine (The Promise and Perils of Artificial Intelligence - Part 1) traces succinctly the development of AI in the scientific community.
Pupils are working on their own. The second and third grade reading class of 63 pupils … is using a learning center and two adjoining rooms. Two teachers and the school librarian act as coor...
Sandra Lyn's insight:
Blog post comparing MOOCs to Individually Prescribed Instruction (IPI) systems of the 1960’s. Author says that pedagogical reasons for computerized instructional programs remain the same, and although he doesn’t see teachers completely removed from the classroom, he supports student centred approaches to learning.
It's interesting to see things that seem so current having originated before widespread access to computing.
This is a foundational article in the field of AI written in 1986 by William J. Clancey, a computer scientist from Stanford.
What Clancey is proposing here is a model of AI that still does not exist today: the ability of a computer program to reason it’s way through a problem inferentially, creating what he calls a "qualitative student model". This is different from a classification model (the identification of ordered steps) as the computer is meant to ‘learn’ during the process. From this learned knowledge, the computer (or program) will then be able to instruct and diagnose a student’s learning, by:
1. referencing a general model of knowledge and then,
2. comparing the students progress to that model with it’s own qualitative (or learned) model
3. and responsively recommending what the student do next (in terms of learning objectives)
What is required for this type of AI is a deep understanding of the nature of knowledge and problem solving; deep enough that it can be programmable. He suggests that it is possible through the overlapping (but distinct) use of qualitative models.
Sebastian Thrun, winner of the Smithsonian American Ingenuity Award for education takes is redefining the modern classroom
Sandra Lyn's insight:
I scooped this article based on the title alone, and after reading it I've learned a few important lessons.
**Titles can be misleading
**Artificial Intelligence is widely misunderstood
For an article with a title like "How Artificial Intelligence Can Change Higher Education", the words artificial intelligence appear only three times (including once in the title). That being said, Udacity's Sebastian Thrun, the focus of the article, started his career in machine learning and artificial intelligence.
When I arrived (late) to his public lecture at the University of Alberta, the first thing I heard him say was that the purpose of online course delivery was not to recreate the brick-and-mortar classroom on the internet. He spoke about open access to education and lifelong learning. It was inspiring, and a lot of what he had to say is included in this article.
On to misunderstandings...
Artificial intelligence has been a 'buzzword' since Alan Turing created the Turing test in 1950. Even the ancient Greeks dreamt of robotic or artificial beings--a quick Google search for Talos will confirm this. But while it's easy to imagine the science-ficitional possibilities of androids and cyborgs and robots, it is less easy to articulate what artificial intelligence means today. Artificial intelligence, or AI, as I will henceforth refer to it, is a lot less glamorous in it's current practical applications than it is in the realm of imagination. But it has major implications for the future of online course delivery, especially when it comes to assessment.
Can teachers be replaced by machines? While I can't hope to answer this question anytime soon, I hope to better articulate the meaning of AI in education in this forum.
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