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Education 2.0: Artificial Intelligence and the End of the Test - Bill Cope and Mary Kalantzis

We are on the cusp of a series of socio-technical revolutions. On one count, after the industrial revolutions of steam, electricity, and digitization, the next is Industry 4.0, a revolution in which artificial intelligence will be central (Schwab & Klaus, 2017). On another count, focusing now on internet communications,after the read-only web, then the social web, this is Web 3.0, or what web founder Tim Berners-Lee calls the semantic web.1 In this proposal web data is structured around ontologies and tagged and structured in such a way that supplementary meaning is added to natural language, images and other media (Cope, Kalantzis & Magee. 2011).

Schools have barely been touched by these changes. Even though we now find computers in classrooms, and learners accessing their knowledge and doing their work on digital devices, the social relationships of learning have remained much the same. In this paper, we’re going to look at one critically important aspect of education, the test. We are going to focus on this particularly because the test the primary measure of educational outcomes, learner knowledge and progress, and teacher, school and system effectiveness. Tests also influence curriculum, the tail wagging the proverbial dog.

Students are now doing tests online—but as knowledge artifacts they have changed little...

How might things be different? How might artificial intelligence be part of the change? What might be the shape of Education 2.0?

Education_2.0_Cope_Kalantzis.pdf

Cope, Bill and Mary Kalantzis. 2019. "Education 2.0: Artificial Intelligence and the End of the Test." Beijing International Review of Education 1:528-43.

By William Cope
Views: 3,333

After reading this article, it seems to me that the concept of artificial intelligence in computers was strongly influenced by didactic pedagogy, because machine intelligence is limited to memory retrieval and calculation. Does this thought make sense?
- Vania Castro

The evolution of technology applied to learning delivery methods has made the learning experience far more specific, personalized and measurable than it’s ever been. The exciting thing about this is now learning professionals can make the experience interesting and engaging for the learner, but they can also now understand how to actually achieve what learning was intended to do: get a behavior change and business outcome through growth in knowledge. I have personally seen this in action within new learning systems being developed in my company which will be able to customize, measure and build learning experiences for individuals in ways that are not driven by traditional learning management systems. AI and machine learning will be leveraged to work through problem-solving and build learning solutions with tailored approaches. Using a variety of delivery modalities via software, and being able to measure their effectiveness deeply and granularly, will allow us to optimize the experience for everyone. The ability to create learning that is more physically and mentally immersive, or with more connection to the work environment, or more personalized, will mean that each learner’s attention can be captured more fully, offer learning solutions that are more aligned with real-world work experience, and address each learner’s specific needs. The great thing about AI and machine learning is the ability to leverage data that was previously impossible to get, adjust it automatically through machine learning, and get value for the learner and for the business that is orders of magnitude greater than at any time in history. Gone are the days of boring, one-size-fits all, static and un-measurable investments in learning. There’s a lot of noise and traffic in the learning experience for learner’s today. The hope is that AI can clean that up and make the learning communication channels more effective and efficient.
- Heather Blevins