Ambitious Mashups: Artificial Intelligence and Learning

Authors: Jeremy Roschelle & Patricia Schank; Contributors: Judi Fusco & Wendy Martin

Definition

Cyberlearning projects advance state of the art technologies and techniques from computer science, data science, robotics, and other areas. In conjunction with learning scientists and experts in equity and in particular subject matter or educational contexts, cyberlearning projects seek to advance our understanding of these technologies and techniques in the context of human learning challenges. In reviewing the portfolio, the most common types of technologies and techniques are:

  • Intelligent Tutoring Systems, in which a computational agent supports and guides students as they work to learn challenging subject matter.
  • Machine Learning, where statistical techniques are applied to infer patterns in large data sets about learner behavior and outcomes. Insights and techniques for guiding learner’s behavior to desired outcomes are sought.
  • Speech, Vision, and Natural Interaction, where machine learning and other techniques are specifically applied to analyze human speech, visual scenes in which learners participate, eye-gaze, etc. and (in cases) to synthesize naturalistic interactions with learners in the same modalities.
  • Social Robotics and Avatars, where above techniques are applied to provide an artificial learning partner to a student or group of students.

The Artificial Intelligence & Education (AI&Ed) community is long-standing and accomplished, with its own society, journals, and multiple funding streams. As participants in the cyberlearning community, experts in the above topics were somewhat less present in the first 3-4 years, but participation increased strongly thereafter. Rather than simply advancing Intelligent Tutoring Systems (ITS) technology with AI, cyberlearning projects combine AI with learning theories to create “ambitious mashups.” Examples include exploring hybrid systems in which people and AI agents work together, focusing on social learning (classic AI & Ed research was somewhat more individual-oriented). Cyberlearning AI projects also support teachers and expand to applications like social robots for learning Chinese, AI supports for workforce learning, and an invention coach (whereas early ITS applications were often oriented to more bounded problem solving in math and science). Thus, the portfolio of cyberlearning projects push beyond AI beyond the boundaries of a conventional ITS.

Please see the full report for additional information on how this theme changed over time in cyberlearning research and for some questions that arose as we investigated the theme.

Project Examples and Resources


Intelligent Tutors

  • 36 Projects
  • 16 sessions at cyberlearning convenings

Stimulating Quotes and Snippets

  • “The time for real time feedback is in the moment, while the student is learning. It doesn’t make sense to allow students to go through all the inquiry phases and then a week or two from then get back to them. It’s so important that students get the feedback in real-time and the teacher can stop the class in real-time. It affects a teacher’s instruction in a way that supports inquiry learning as it needs to be supported, in the moment. It’s like apprenticeship learning where you provide situated experiences with feedback in real time, with the apprentice working side by side with the mentor; that’s the big benefit we see from Inq-ITS & Inq-Blotter.” – Janice Gobert
  • “Intelligent tutoring systems are efficient for some procedural skills, freeing teachers to spend time on other needs or with other students to provide more individualized support––or giving them time to plan the next lesson.” – Claudia Mazziotti

Example Project Abstracts:

Related Primers/Spotlights/Reports:

Spotlight: The Invention Coach

Showcase Videos and/or Gallery Posters:

Cross Connections:

  • Assessment
  • Learning Theories: Cognitive
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