Intelligence Artificial Intelligence Ian Gent [email protected] Topics in Artificial Intelligence.
Artificial Intelligence in Educationnews.ntu.edu.sg/rc-cradle/Documents/Dr. Mike Timms.pdf · 2020....
Transcript of Artificial Intelligence in Educationnews.ntu.edu.sg/rc-cradle/Documents/Dr. Mike Timms.pdf · 2020....
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Artificial Intelligencein Education
Mike Timms
Director, EdTech Evaluation Consulting
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Artificial Intelligence in Education
1. A brief history of Artificial Intelligence in Education (AIED)
2. How is Artificial Intelligence currently used in Education Technology?
3. What are the future areas of research for AIED?
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Types of Artificial Intelligence(Adapted from Hintz, 2016).
AIED
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1. A brief history of AIED
A look back before we look forward
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TIMELINE of AIEDEARLY YEARS
1960sINFORMATIONPROCESSINGAttempts to map out knowledgedomains so that learning could beprogrammed in an informationprocessing approach.
AIED Early Years
Trying to representknowledge and how the
brain processes itduring learning.
1980sMODELLINGMISCONCEPTIONSThe BUGGY approach. Studentshave 'bugs' in their thinking thatneed to be corrected. Creation of'bug libraries'.
Pedagogy becomesmore important 2000s
DATA MININGAIED uses a multitude of AIapproaches such as ANNs andBayes Nets (BNs). EducationalData Mining field is formed.
Growth of different AIapproaches and learning
analytics
1970sINTELLIGENT TUTORINGSYSTEMS (ITS)Initial approaches are based onstep-by-step programmed learning.Designed by engineers not teachers.
Computer assistedinstruction is born
1990sDYNAMICSTUDENT MODELSMore attention paid to trackIng whatthe student has learned and updating adynamic student model. More attentionto how to provide hints and feedback.
A FIELD EMERGESIn the beginning the field of AI ineducation was dominated by computerscientists. As the field grew they werejoined by cognitive psychologists andeventually by education researchers.
Student modelsbecome adaptive
1960s - 2000sAs Artifiicial Intelligence intelligence advanced, so did the approaches in AIED. Technological advances brought fasterprocessing, which allowed for real-time feedback to learners. At first the field struggled to find ways to represent domainknowledge and had no models of how to provide feedback, but as the field advanced better solutions emerged.
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TIMELINE of AIEDEARLY YEARS
1960sINFORMATIONPROCESSINGAttempts to map out knowledgedomains so that learning could beprogrammed in an informationprocessing approach.
AIED Early Years
Trying to representknowledge and how the
brain processes itduring learning.
1980sMODELLINGMISCONCEPTIONSThe BUGGY approach. Studentshave 'bugs' in their thinking thatneed to be corrected. Creation of'bug libraries'.
Pedagogy becomesmore important 2000s
DATA MININGAIED uses a multitude of AIapproaches such as ANNs andBayes Nets (BNs). EducationalData Mining field is formed.
Growth of different AIapproaches and learning
analytics
1970sINTELLIGENT TUTORINGSYSTEMS (ITS)Initial approaches are based onstep-by-step programmed learning.Designed by engineers not teachers.
Computer assistedinstruction is born
1990sDYNAMICSTUDENT MODELSMore attention paid to trackIng whatthe student has learned and updating adynamic student model. More attentionto how to provide hints and feedback.
A FIELD EMERGESIn the beginning the field of AI ineducation was dominated by computerscientists. As the field grew they werejoined by cognitive psychologists andeventually by education researchers.
Student modelsbecome adaptive
1960s - 2000sAs Artifiicial Intelligence intelligence advanced, so did the approaches in AIED. Technological advances brought fasterprocessing, which allowed for real-time feedback to learners. At first the field struggled to find ways to represent domainknowledge and had no models of how to provide feedback, but as the field advanced better solutions emerged.
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TIMELINE of AIEDEARLY YEARS
1960sINFORMATIONPROCESSINGAttempts to map out knowledgedomains so that learning could beprogrammed in an informationprocessing approach.
AIED Early Years
Trying to representknowledge and how the
brain processes itduring learning.
1980sMODELLINGMISCONCEPTIONSThe BUGGY approach. Studentshave 'bugs' in their thinking thatneed to be corrected. Creation of'bug libraries'.
Pedagogy becomesmore important 2000s
DATA MININGAIED uses a multitude of AIapproaches such as ANNs andBayes Nets (BNs). EducationalData Mining field is formed.
Growth of different AIapproaches and learning
analytics
1970sINTELLIGENT TUTORINGSYSTEMS (ITS)Initial approaches are based onstep-by-step programmed learning.Designed by engineers not teachers.
Computer assistedinstruction is born
1990sDYNAMICSTUDENT MODELSMore attention paid to trackIng whatthe student has learned and updating adynamic student model. More attentionto how to provide hints and feedback.
A FIELD EMERGESIn the beginning the field of AI ineducation was dominated by computerscientists. As the field grew they werejoined by cognitive psychologists andeventually by education researchers.
Student modelsbecome adaptive
1960s - 2000sAs Artifiicial Intelligence intelligence advanced, so did the approaches in AIED. Technological advances brought fasterprocessing, which allowed for real-time feedback to learners. At first the field struggled to find ways to represent domainknowledge and had no models of how to provide feedback, but as the field advanced better solutions emerged.
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TIMELINE of AIEDEARLY YEARS
1960sINFORMATIONPROCESSINGAttempts to map out knowledgedomains so that learning could beprogrammed in an informationprocessing approach.
AIED Early Years
Trying to representknowledge and how the
brain processes itduring learning.
1980sMODELLINGMISCONCEPTIONSThe BUGGY approach. Studentshave 'bugs' in their thinking thatneed to be corrected. Creation of'bug libraries'.
Pedagogy becomesmore important 2000s
DATA MININGAIED uses a multitude of AIapproaches such as ANNs andBayes Nets (BNs). EducationalData Mining field is formed.
Growth of different AIapproaches and learning
analytics
1970sINTELLIGENT TUTORINGSYSTEMS (ITS)Initial approaches are based onstep-by-step programmed learning.Designed by engineers not teachers.
Computer assistedinstruction is born
1990sDYNAMICSTUDENT MODELSMore attention paid to trackIng whatthe student has learned and updating adynamic student model. More attentionto how to provide hints and feedback.
A FIELD EMERGESIn the beginning the field of AI ineducation was dominated by computerscientists. As the field grew they werejoined by cognitive psychologists andeventually by education researchers.
Student modelsbecome adaptive
1960s - 2000sAs Artifiicial Intelligence intelligence advanced, so did the approaches in AIED. Technological advances brought fasterprocessing, which allowed for real-time feedback to learners. At first the field struggled to find ways to represent domainknowledge and had no models of how to provide feedback, but as the field advanced better solutions emerged.
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TIMELINE of AIEDEARLY YEARS
1960sINFORMATIONPROCESSINGAttempts to map out knowledgedomains so that learning could beprogrammed in an informationprocessing approach.
AIED Early Years
Trying to representknowledge and how the
brain processes itduring learning.
1980sMODELLINGMISCONCEPTIONSThe BUGGY approach. Studentshave 'bugs' in their thinking thatneed to be corrected. Creation of'bug libraries'.
Pedagogy becomesmore important 2000s
DATA MININGAIED uses a multitude of AIapproaches such as ANNs andBayes Nets (BNs). EducationalData Mining field is formed.
Growth of different AIapproaches and learning
analytics
1970sINTELLIGENT TUTORINGSYSTEMS (ITS)Initial approaches are based onstep-by-step programmed learning.Designed by engineers not teachers.
Computer assistedinstruction is born
1990sDYNAMICSTUDENT MODELSMore attention paid to trackIng whatthe student has learned and updating adynamic student model. More attentionto how to provide hints and feedback.
A FIELD EMERGESIn the beginning the field of AI ineducation was dominated by computerscientists. As the field grew they werejoined by cognitive psychologists andeventually by education researchers.
Student modelsbecome adaptive
1960s - 2000sAs Artifiicial Intelligence intelligence advanced, so did the approaches in AIED. Technological advances brought fasterprocessing, which allowed for real-time feedback to learners. At first the field struggled to find ways to represent domainknowledge and had no models of how to provide feedback, but as the field advanced better solutions emerged.
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TIMELINE of AIEDEARLY YEARS
1960sINFORMATIONPROCESSINGAttempts to map out knowledgedomains so that learning could beprogrammed in an informationprocessing approach.
AIED Early Years
Trying to representknowledge and how the
brain processes itduring learning.
1980sMODELLINGMISCONCEPTIONSThe BUGGY approach. Studentshave 'bugs' in their thinking thatneed to be corrected. Creation of'bug libraries'.
Pedagogy becomesmore important 2000s
DATA MININGAIED uses a multitude of AIapproaches such as ANNs andBayes Nets (BNs). EducationalData Mining field is formed.
Growth of different AIapproaches and learning
analytics
1970sINTELLIGENT TUTORINGSYSTEMS (ITS)Initial approaches are based onstep-by-step programmed learning.Designed by engineers not teachers.
Computer assistedinstruction is born
1990sDYNAMICSTUDENT MODELSMore attention paid to trackIng whatthe student has learned and updating adynamic student model. More attentionto how to provide hints and feedback.
A FIELD EMERGESIn the beginning the field of AI ineducation was dominated by computerscientists. As the field grew they werejoined by cognitive psychologists andeventually by education researchers.
Student modelsbecome adaptive
1960s - 2000sAs Artifiicial Intelligence intelligence advanced, so did the approaches in AIED. Technological advances brought fasterprocessing, which allowed for real-time feedback to learners. At first the field struggled to find ways to represent domainknowledge and had no models of how to provide feedback, but as the field advanced better solutions emerged.
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AIED to date
• Individual learners and tutoring
• Feedback• when • how • what
• Keyboard and mouse• Subject areas of
computer coding, mathematics and science
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2. How is AI used in EdTech?
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Overview of typical AIED applications and their relationships.
Source: Southgate et al. (2018).
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Intelligent Tutoring Systems
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Traditional ITS Model
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A modern ITS modelEven though the model incorporates affect (emotional state) of the learner, the basic ITS model still underpins it.
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Example of a one-on-one tutoring system
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Providing hints is a common feature of ITS
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Pedagogical Agents
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Virtual Coach
Virtual Role-Play
Good for learning skills
Johnson & Lester, 2018
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Teachable Agent
Good for higher ability learners
Johnson & Lester, 2018
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NRC: Resiliency Game
Produced by Smart Sparrow
A different approach to developing a pedagogical agent
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Virtual Coach – without the expensive animation!
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Adaptive Learning
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DuoLingo
Adapts lessons based on performance
Adapts the Practicesessions
Tracks your errors and goesover them
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Smart Classrooms
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Martinez-Maldonado et al. 2020
Positioning sensors worn by teachers
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Chng et al. 2020
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3. Future applications of AIED?
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Technology
• Extending AIED using advanced technologies
Education• Supporting
teachers in the classroom
Data
• Using big data and learning analytics with AIED
Future pathways for AIED
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Extending AIED using advanced technologies
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Natural Language Processing
… to have unstructured dialogue with learners
Johnson & Lester, 2018
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Chatbots
… to have interactions with learners
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Facial recognition
… to detect emotions during learning
… to identify students during learning
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Blending AIED with new hardware
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Mixed Reality – AR, VR
… to incorporate AIED into simulations
… to incorporate AIED into training
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Multi-Modal Sensors
… to assist in physical skills training
Motion sensors, biometric sensors, eye-tracking etc
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…as pedagogical agent
…learning to program robots
… social emotional learning … helping teachers?Robots
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Supporting teachers in the classroom
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Time-intensive tasks for teachers
Marking student work
Providing meaningful feedback
Developing lessons
Developing assessments
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Challenging tasks for teachers
Working with small groups
Differentiating instruction
Tutoring in collaborative groups
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Blending AIED with big data
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Advances in AIED data
• Blending big data with existing models
• Machine Learning + theories of cognition
• Deep learning
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www.edtechevaluation.com.au
mailto:[email protected]
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References
• Chng, E., Seyam, M. R., Yao, W., & Schneider, B. (2020). Using Motion Sensors to Understand Collaborative Interactions in Digital Fabrication Labs. In I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millán(Eds.), Artificial Intelligence in Education (Vol. 12163, pp. 118–128). Springer International Publishing. https://doi.org/10.1007/978-3-030-52237-7_10
• Johnson, W. L., & Lester, J. C. (2018). Pedagogical Agents: Back to the Future. AI Magazine, Summer 2018, 33–44.
• Martinez-Maldonado, R., Echeverria, V., Schulte, J., Shibani, A., Mangaroska, K., & Buckingham Shum, S. (2020). Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching. In I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millán (Eds.), Artificial Intelligence in Education (Vol. 12163, pp. 360–373). Springer International Publishing. https://doi.org/10.1007/978-3-030-52237-7_29
• Southgate, E., Blackmore, K., Pieschl, S., Grimes, S., McGuire, J., & Smithers, K. (2018). Artificial intelligence and emerging technologies (virtual, augmented and mixed reality) in schools: A research report. University of Newcastle, Australia. https://docs.education.gov.au/system/files/doc/other/aiet_final_report_august_2019.pdf