Effective studying is a skill, but it鈥檚 one students sometimes don鈥檛 learn despite years of schooling.
A 麻豆原创 researcher has developed a computer-based, artificial intelligence tutoring tool 鈥� MetaTutor 鈥� that monitors students鈥� 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 Education鈥檚 What Works Clearinghouse report .
鈥淗umans struggle and face serious challenges when learning, reasoning and problem solving,鈥� says Roger Azevedo, a professor in 麻豆原创鈥檚 College of Community Innovation and Education who developed the tool. 鈥淪o, we as psychologists are finding ways of understanding what those challenges or limitations are so we can help people overcome them.鈥�
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 computer鈥檚 AI will recognize this and will begin to help them readjust.
鈥淔or example, what if the student selects content that is part of the topic but isn鈥檛 relevant to the current learning goal?鈥� Azevedo says. 鈥淚f a student persists with that content, then after a certain amount of time, one of the tool鈥檚 four avatars, Mary the Monitor, pops up on the screen.鈥�
鈥淢ary starts a dialogue,鈥� Azevedo says. 鈥淪he says, 鈥楬ey, do you think this content is relevant to your current learning goal?鈥� If the student says 鈥榶es,鈥� then they have to explain to Mary why it鈥檚 relevant. Depending on their response, Mary provides individualized instructional techniques and feedback.鈥�
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 tool鈥檚 educational effectiveness.
For example, the data can predict and select what material is more likely to be associated with students鈥� 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 student鈥檚 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.
Azevedo鈥檚 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 master鈥檚 in educational technology and bachelor鈥檚 in psychology from Concordia University. Azevedo is the lead scientist in 麻豆原创鈥檚 Learning Sciences Cluster and has joint appointments in 麻豆原创鈥檚 . He joined 麻豆原创 in 2018.
