While “AIED” may seem relatively new, a deeper look shows us it has been developing for over half a century
In the last few years, numerous developments have led to a growing awareness of the maturity of Artificial Intelligence. Self-driving cars and personal assistants like Alexa and Siri are some of the consumer-facing technologies that have helped to fuel this awareness. This knowledge can also bring with it a certain dystopian fear about robots and technology “taking over”.
While we should always strive to be cautious with new technologies, our concerns should also be tempered by understanding the long curve of development that typically precedes these seemingly overnight maturings of technology.
I've been reading Artificial Intelligence in Education, a 2019 publication by Wayne Holmes, Maya Bialik, and Charles Fadel, that explores implications of AI in the realm of teaching and learning. It has been an enlightening read (and I should add, a very affordable one – so many academic publications are exorbitantly priced, but this one was only $14.95 in paperback form on Amazon).
One of the things that really struck me as I read this book is the long history of applications of artificial intelligence in education, stretching all the way back to work by Pressey, Skinner, and others the 1950s.
I do not intended to summarize the entire book here in a blog post, but rather to provide a brief overview of one specific interesting use of AI that is explored therein: the Intelligent Tutoring System.
Intelligent Tutoring System =
Domain Model + Pedagogy Model + Learner Model
One of the first things we learn in the section about Intelligent Tutoring Systems is that such systems require three vital elements. Combining a specific knowledge domain model with a pedagogy model (representing knowledge of teaching and learning) and then integrating a Learner Model differentiates such systems from the Computer Assisted Instruction tools that preceded Intelligent Tutoring Systems. Learner Models can combine things that are known about the student with information or patterns gleaned via machine learning from other students who have used the system.
Before exploring various specific examples of ITS, there is a brief discussion about studies looking at the efficacy of such tools. One meta study notes:
“Developers of ITSs long ago set out to improve on the success of CAI [Computer Assisted Instruction] tutoring and to match the success of human tutoring. Our results suggest that ITS Developers have already met both of these goals”1
The book goes on to note that when pooling the results of numerous meta studies, ITS appears to have bested the results of whole-classroom teaching, but not (yet) the results of one-to-one teaching. This is impressive and important to be aware of.
Examples of ITS
There are dozens of examples of Intelligent Tutoring Systems provided in the book. Here are a handful of them:
Mathia: Developed at Carnegie Mellon University, Mathia was designed by cognitive scientists to help deliver a successful math experience to each individual student, while “providing you with all the real-time feedback and assessments you need to understand where your students are at and where they're headed.” (p. 109)
ASSISTments: This free mathematics tutoring tool was developed at Worcester Polytechnic Institute and works with curriculum from 2nd through 12th grade. Teachers can use a variety of resources to create assignments for students. Students receive immediate feedback and guidance on assignments through the software. Teachers can then use assessment and analytics to target class time to help address student needs. (Sounds like a great addition to the flipped math classroom). Learn more here: new.assistments.org.
alta: This fully integrated, adaptive learning courseware is a product from Knewton and is aimed at higher education (knewton.com/what-is-alta/). It is intended to work with several different subjects in the domains of math, economics, and chemistry. Alta's pedagogy is based on item response theory and it utilizes a mastery learning approach.
Area9 Lyceum (area9lyceum.com): This unique approach recreates learning content and delivers it on their platform, which uses “continuous self-assessment” (the user's confidence in their responses are used as part of the adaptive process), in addition to other adaptive factors. The solution appears to have become popular in professional/corporate training.
Toppr: This application is based out of India and offers personalized learning across a wide range of grades and subjects. Toppr uses machine learning based on student responses, personalizing questions and adjusts the speed of presentation to optimize the experience for each student.
These look like powerful tools. Where do we go from here?
With the many offerings available, some of which have been around for a decade or more, I am surprised by how rare the use of these tools still seems to be. I have not come across much in the way of discussions or written dialogue about these applications. There appears to be an imbalance between the power and potential of intelligent tutoring systems (i.e. adaptive learning) and the adoption of the same.
This doesn't seem to be a cost issue as some of these platforms are relatively inexpensive (alta is $39 for college students) or even free (ASSISTments).
Is the problem lack of awareness? Reluctance to change? I ask the reader – are you aware of these types of platforms? Do you have experience with them? Do you know of reasons educators and schools have not more aggressively adopted them? (Thanks for any feedback you care to offer!)
1Kulik, J.A. & Fletcher J.D. (2015). “Effectiveness of intelligent tutoring systems a meta-analytic review.” Review of Educational Research, 0034654315581420. https://doi.org/10.3102/0034654315581420