Artificial Intelligencein Education

Mike Timms

Director, EdTech Evaluation Consulting

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?

Types of Artificial Intelligence(Adapted from Hintz, 2016).

AIED

1. A brief history of AIED

A look back before we look forward

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.

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.

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.

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.

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.

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.

AIED to date

• Individual learners and tutoring

• Feedback• when • how • what

• Keyboard and mouse• Subject areas of

computer coding, mathematics and science

2. How is AI used in EdTech?

Overview of typical AIED applications and their relationships.

Source: Southgate et al. (2018).

Intelligent Tutoring Systems

Traditional ITS Model

A modern ITS modelEven though the model incorporates affect (emotional state) of the learner, the basic ITS model still underpins it.

Example of a one-on-one tutoring system

Providing hints is a common feature of ITS

Pedagogical Agents

Virtual Coach

Virtual Role-Play

Good for learning skills

Johnson & Lester, 2018

Teachable Agent

Good for higher ability learners

Johnson & Lester, 2018

NRC: Resiliency Game

Produced by Smart Sparrow

A different approach to developing a pedagogical agent

Virtual Coach – without the expensive animation!

Adaptive Learning

DuoLingo

Adapts lessons based on performance

Adapts the Practicesessions

Tracks your errors and goesover them

Smart Classrooms

Martinez-Maldonado et al. 2020

Positioning sensors worn by teachers

Chng et al. 2020

3. Future applications of AIED?

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

Extending AIED using advanced technologies

Natural Language Processing

… to have unstructured dialogue with learners

Johnson & Lester, 2018

Chatbots

… to have interactions with learners

Facial recognition

… to detect emotions during learning

… to identify students during learning

Blending AIED with new hardware

Mixed Reality – AR, VR

… to incorporate AIED into simulations

… to incorporate AIED into training

Multi-Modal Sensors

… to assist in physical skills training

Motion sensors, biometric sensors, eye-tracking etc

…as pedagogical agent

…learning to program robots

… social emotional learning … helping teachers?Robots

Supporting teachers in the classroom

Time-intensive tasks for teachers

Marking student work

Providing meaningful feedback

Developing lessons

Developing assessments

Challenging tasks for teachers

Working with small groups

Differentiating instruction

Tutoring in collaborative groups

Blending AIED with big data

Advances in AIED data

• Blending big data with existing models

• Machine Learning + theories of cognition

• Deep learning

Any Questions?miketimms@edtechevaluation.com.au

www.edtechevaluation.com.au

mailto:miketimms@edtechevaluation.com.au

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