Effective studying is a skill, but itβs one students sometimes donβt learn despite years of schooling.
A ΒιΆΉΣ³»΄«Γ½ researcher has developed a computer-based, artificial intelligence tutoring tool β MetaTutor β that monitors studentsβ learning activities, facial expressions, eye movements, and interactions with avatars, and adapts its instruction delivery to help students learn more effectively.
The tool is featured in the U.S. Department of Educationβs What Works Clearinghouse report .
βHumans struggle and face serious challenges when learning, reasoning and problem solving,β says Roger Azevedo, a professor in ΒιΆΉΣ³»΄«Γ½βs College of Community Innovation and Education who developed the tool. βSo, we as psychologists are finding ways of understanding what those challenges or limitations are so we can help people overcome them.β
MetaTutor works by placing students inside an AI-supported learning environment they access through their computer. As they navigate the content they are studying, the tool monitors the information they are accessing and for how long. If students start going down the wrong track, the computerβs AI will recognize this and will begin to help them readjust.
βFor example, what if the student selects content that is part of the topic but isnβt relevant to the current learning goal?β Azevedo says. βIf a student persists with that content, then after a certain amount of time, one of the toolβs four avatars, Mary the Monitor, pops up on the screen.β
βMary starts a dialogue,β Azevedo says. βShe says, βHey, do you think this content is relevant to your current learning goal?β If the student says βyes,β then they have to explain to Mary why itβs relevant. Depending on their response, Mary provides individualized instructional techniques and feedback.β
These prompts help students stay focused and augment their ability to discern the material most relevant to their planned and current learning goals. This helps improve their metacognition, or ability to be aware of what they are learning.
Azevedo and his interdisciplinary research team are focused on improving student learning outcomes, which is why they are also continuously collecting data about what people are doing and what is happening to them during the learning process.
This includes tracking their eye movements across the screen, monitoring their facial expressions of emotions and interactions and dialogue with the four avatars. The team also monitors the physiological responses of each participant during complex learning.
These data about how students examine and react to content can then be entered back into the system to improve the toolβs educational effectiveness.
For example, the data can predict and select what material is more likely to be associated with studentsβ current learning goals or offer a better way to examine and learn the material based on how students used their eyes to scan multimedia instructional materials.
In the future, the data being collected will allow the AI-based learning and training system to provide real-time, individualized feedback and support to meet each studentβs learning needs, Azevedo says.
The tool is currently designed to teach college students about human body systems, but knowledge gleaned from researching it as a learning tool could be applied to educational material for other fields as well. Research has focused on college students but could eventually include testing the system with high school and middle school students as well as medical professionals.
Work on MetaTutor began in 2010, and the research has been supported with National Science Foundation grants through the years that total $4 million, as well as funding from the Social Sciences and Humanities Research Council of Canada.
Azevedoβs research has shown that 73 to 86 percent of people who use the AI-based version of MetaTutor outperform people who use the non-AI based version that does not adapt to the user.
A version of MetaTutor is currently being used in Spain to teach college students with learning disabilities, and Azevedo says there are plans to expand its reach, including designing a version for high school and middle school students to fit their curriculum needs.
Azevedo received his doctorate in educational psychology from McGill University and his postdoctoral training in cognitive psychology at Carnegie Mellon University. He received his masterβs in educational technology and bachelorβs in psychology from Concordia University. Azevedo is the lead scientist in ΒιΆΉΣ³»΄«Γ½βs Learning Sciences Cluster and has joint appointments in ΒιΆΉΣ³»΄«Γ½βs . He joined ΒιΆΉΣ³»΄«Γ½ in 2018.
