Artificial Intelligence and Learning

With the growth of tools like the Amazon Echo, IBM’s Watson, or Apple’s Siri there’s been a renewed interest in artificial intelligence and learning.  In this blog article I’ll showcase just a few directions that are part of the contemporary landscape: adapted learning; personalized learning; chatbots and online learning; and new ways to access personal information.

Adapted Learning

According to Wikipedia, adaptive learning dates back to the 1970s. The idea was to create software that could emulate the human ability to adapt to individual learner’s needs creating a more effective learning experience. Adaptive learning usually has four components or “models:”

  • Expert model – The model with the information which is to be taught
  • Student model – The model which tracks and learns about the student
  • Instructional model – The model which actually conveys the information
  • Instructional environment – The user interface for interacting with the system

Much of the initial research came from work at Stanford University and Carnegie Mellon University. Today products like ALEKS, Knewton, and MATHia demonstrate the power of adaptive learning. It’s interesting to see that most of the work has been done in the area of mathematical learning. While there have been attempts to move beyond mathematics, adaptive learning works best in a very predefined world.

Personalizing Learning

There are many approaches to personalizing learning, but based on my experiences working with teachers, it’s next to impossible to do without digital technology. There have been some recent attempts by Alt Schools and Summit Learning to develop software to make the task easier for the learner and the facilitator. Key to both of these approaches is the use of a variety of data that pinpoints where the learner is on some continuum of skills, the learner’s goals, and resources to help the learner master a set of skills, competencies, or objectives.

Chatbots and Online Tools

The first chatter boxes were based on keywords. Today AI plays a new role opening up more sophisticated ways to engage a user in an online conversation. According to Wikipedia, “Today, chatbots are part of virtual assistants such as Google Assistant, and are accessed via many organizations’ apps, websites, and on instant messaging platforms such as Facebook Messenger.” Today, chatbots are often used as part of homework tutorial programs like Nerdy Bot. Chatbots also play a role in grading essays. Hewlett Packard sponsored a competition in 2012 and the winner had 0.81 correlation with human graders.

At Georgia Tech, students were charmed by the teaching assistant, Jill Watson. What they didn’t realize was the fact that their online teaching assistant was actually a chatbot based on IBM’s Watson technology. The Coppell Independent School District (ISD) in Texas is the first school district to use the IBM Watson app to provide deeper levels of personal interactions and learning experiences for its students.

New Tools to Access Information

The Amazon Dot is a new tool for education that uses the technology behind Alexa, Amazon’s tool for the consumer market. Here are some ways Dr. Bruce Ellis suggests to take advantage of Alexa in the classroom:

  • Use Alexa in your classroom to support literacy by having students ask her how to spell a specific word, suggest a synonym, or provide a definition.
  • Social studies students can skip an internet search by asking Alexa simple geography and civics questions.
  • Math students can use Alexa to check their work when they’ve finished an assignment, and science students will find Alexa is great at converting units of measurements.

Arizona State University is one of the first educational institutions to test out the Amazon Dot as a learning tool. Starting the fall of 2017 1,600 engineering students are using the Amazon Dot. The university intends to evaluate the Amazon Dot as a tool that “combines sensing, connectivity and data analytics to inform decision making, optimize operations and energy efficiency, and create a highly personalized campus experience for every student, professor, staff member and alumnus.”

 

 

 

 

Preview of 2017 K-12 Horizon Report

[This year I was part of the team selecting the Emerging Technologies, Trends, and Challenges for the global K-12 world. The team had members around the world. I served on the team representing the Consortium for School Networking (CoSN). When I served as the Coordinator of Educational Technology for the Fox Chapel Area School District, I used the report annually to benchmark the district goals for integrating technology into the learning process. In my teaching at Carnegie Mellon University I always shared the document with my students who worked on their technology plans or other planning document. ]

Photo by Norton Gusky CC BY 4.0

The expert panel has completed voting and the topics for the NMC/CoSN Horizon Report > 2017 K-12 Edition have been selected — below. The K12 Project as whole is led by the New Media Consortium, in collaboration with the Consortium for School Networking (CoSN) and made possible by mindSpark Learning (formerly known as Share Fair Nation). The report is set to be released in August, 2017. We’re now looking for any projects, programs, policies, or leadership initiatives that fit any of the below chosen areas, to be submitted here. Download the official NMC/CoSN Horizon Report Preview > 2017 K-12 Edition to view definitions of the topics below or check out the related discussions of all of the final topics in the 2017 Horizon.k12 Workspace.

I. Key Trends Accelerating K-12 Tech Adoption

 Long-Term Trends: Driving edtech adoption in K-12 education for five or more years

  • Advancing Cultures of Innovation
  • Deeper Learning Approaches

Mid-Term Trends: Driving edtech adoption in K-12 education for the next three to five years

  • Growing Focus on Measuring Learning
  • Redesigning Learning Spaces

Short-Term Trends: Driving edtech adoption in K-12 education for the next one to two years

  • Coding as a Literacy
  • Rise of STEAM Learning

II. Significant Challenges Impeding K-12 Tech Adoption

Solvable Challenges: Those which we both understand and know how to solve

  • Authentic Learning Experiences
  • Improving Digital Literacy

Difficult Challenges: Those we understand but for which solutions are elusive

  • Rethinking the Roles of Teachers
  • Teaching Complex Thinking

Wicked Challenges: Those that are complex to even define, much less address

  • The Achievement Gap
  • Sustaining Innovation through Leadership Changes

III.  Important Developments in Educational Technology for K-12

Time-to-Adoption Horizon: One Year or Less

  • Makerspaces
  • Robotics

Time-to-Adoption Horizon: Two to Three Years

  • Analytics Technologies
  • Virtual Reality

Time-to-Adoption Horizon: Four to Five Years

  • Artificial Intelligence
  • Internet of Things