Artificial Intelligence in EducationPromise and Implications for Teaching and Learning
Dr Wayne HolmesBA, MA, MSc (Oxon), PhD (Oxon)
Assistant Professor (Learning Sciences and Innovation)
Institute of Educational Technology, The Open University (UK)
Visiting Associate Professor (Artificial Intelligence in Education)
University of São Paulo and Federal University of Alagoas (Brazil)
Visiting Associate Professor (Artificial Intelligence in Education)
Big Data Center for Technology-mediated Education, BNU (China)
Lead author (Artificial Intelligence in Education Policy Guidelines)
UNESCO
Lead (Education Taskforce)
All Party Parliamentary Group on Artificial Intelligence (UK)
Intelligence Unleashed. An Argument for AI in
Education.
Technology-enhanced Personalised Learning.
Untangling the Evidence.
Artificial Intelligence in Education.
Promises and Implications for Teaching and Learning.
1.The promise of AIED
2.The implications of AIED
Artificial Intelligence in EducationPromise and Implications for Teaching and Learning
1. The promise of AIED
2. The implications of AIED
Artificial Intelligence in EducationPromise and Implications for Teaching and Learning
The promise of AIED
• AI technologies to support learning (AIED) have been developed for around 30 years.
• It now involves well-established multimillion dollar-funded companies (such as Knewton and Carnegie Learning).
• Amazon, Google and Facebook have invested millions of dollars to develop AIED products.
• By 2024, AIED is predicted to become a market worth almost $6 billion.
However,
• AI technologies are still not that common in classrooms.
• AIED often replicates existing educational practices and educational myths.
• AIED shines a spotlight on existing educational practices.
The promise of AIED
Artificial Intelligence in education
Learning with AI
System-facing AI
Student-facing AI
Teacher-facing AI
Learning about AI
Teaching young people
about AI
Training AI engineers
Training managers about AI
The promise of AIED
The promise of AIED
System-facing AIED
• AI technologies are being developed to automate many aspects of school systems (administration), including:
• admissions,
• timetabling,
• learning management,
• attendance recording,
• predicting which students are at risk of failure…
• Intelligent Tutoring Systems (ITS) • Alef Education• ALEKS• alta• Area9 Learning• Assistments• Bjyu• Century• CogBooks• Dreambox• Inq-ITS• iReady• Mathia• RealizeIt• Smart Sparrow• Squirrel AI• Summit Learning• Toppr
The promise of AIED
• Intelligent Tutoring Systems (ITS)
• Dialogue-based Tutoring Systems
• AutoTutor• CIRCSIM• SCHOLAR• Watson Tutor
The promise of AIED
• Intelligent Tutoring Systems (ITS)
• Dialogue-based Tutoring Systems
• Exploratory Learning Environments
• Betty’s Brain• Crystal Island• ECHOES• Fractions Lab
The promise of AIED
• Intelligent Tutoring Systems (ITS)
• Dialogue-based Tutoring Systems
• Exploratory Learning Environments
• Automatic Writing Evaluation
• e-Rater• Intelligent Essay Assessor• OpenEssayist• PEG• Revision Assistant• Write to Learn
The promise of AIED
• Intelligent Tutoring Systems (ITS)
• Dialogue-based Tutoring Systems
• Exploratory Learning Environments
• Automatic Writing Evaluation
• Learning Network Orchestrators
• Smart Learning Partner• Third Space Learning
The promise of AIED
• Intelligent Tutoring Systems (ITS)
• Dialogue-based Tutoring Systems
• Exploratory Learning Environments
• Automatic Writing Evaluation
• Learning Network Orchestrators
• Language Learning
• Babbel• Duolingoand many others...
The promise of AIED
Future student-facing AIED possibilities
• Collaborative Learning
• Adaptive group formation.
• Expert facilitation.
• Intelligent virtual agents.
• Intelligent moderation:
The promise of AIED
Future student-facing AIED possibilities
• Collaborative Learning
• Continuous assessment
• Replacing stop and test exams (which do not assess effectively).
• Allowing more in-depth and nuanced assessment.
• Supported by blockchain technologies.
The promise of AIED
Future student-facing AIED possibilities
• Collaborative Learning
• Continuous assessment
• AI Learning Companions
• Could accompany and support individual learners throughout their lifelong studies.
• Could help learners to work out what to learn.
• Could offer individualised examples, feedback and guidance.
The promise of AIED
Future student-facing AIED possibilities
• Collaborative Learning
• Continuous assessment
• AI Learning Companions
• Student forum monitoring
• Responding automatically and instantly to standard student questions.
• Referring more complex questions to human tutors.
• Helping to make connections between students.
• Automatically identifying orphan posts.
The promise of AIED
Future teacher-facing AIED possibilities
• Student forum monitoring
• Responding automatically and instantly to standard student questions, allowing the teachers to concentrate on the human aspects of teaching.
The promise of AIED
Future teacher-facing AIED possibilities
• Student forum monitoring
• AI Teaching Assistants
• AI as a research tool to further the learning sciences
• Could enable teachers to monitor student performance while they learn.
• Could enable teachers to instantly access targeted information and materials.
• Could turn teachers into super-teachers.
The promise of AIED
Future teacher-facing AIED possibilities
• Student forum monitoring
• AI Teaching Assistants
• AI as a research tool to further the learning sciences
• Helping to open the ‘black box’ of learning.
• Helping to confirm and augment learning theories.
The promise of AIED
1. The promise of AIED
2. The implications of AIED
Artificial Intelligence in EducationPromise and Implications for Teaching and Learning
Data
What data is collected (is the data private, consent given?)
What proxies are being used/misused?
How is the data processed (there’s no such thing as raw data)?
What are the implications?
Introducing AIED: debunking some myths
Introducing AIED: debunking some mythsAlgorithms and
computation
How do we prevent algorithmic biases?
How do we guard against mistakes?
How do we protect against unintended consequences?
How do we stay in control?
Introducing AIED: debunking some myths
Pedagogical approaches?
Resource allocations (including teacher expertise)?
Gender and ethnic biases?
Behaviour and discipline?
Accuracy and validity of assessments?
Education
Introducing AIED: debunking some myths
Algorithms & computation
Education
Data
Ethics of algorithms in
education
Ethics of data
used in AI
Ethics of learning analytics
?
• Do no harm.
• Ethical frame of reference.
• Transparency.
• Avoiding bias.
• Unintended consequences.
• Autonomy.
• Addressing the full diversity of learners.
• Opening up the AIED black box.
• Misuse of personal information.
• Maintaining trust.
• Equity of access.
• Implications of predictions.
• Sharing information.
• Marginalization of vulnerable populations.
Introducing AIED: debunking some myths
Intelligent Tutoring Systems
privacy
transparency
unintended consequences
biasesinformed consent
ethics?
ethics?
ethics?
ethics?
ethics?ethics?
Introducing AIED: debunking some myths
• Intelligent Tutoring Systems aim to “personalise” learning.
• However, personalisation in learning has many dimensions(“Technology-enhanced Personalised Learning”, Holmes et al., 2018).
• ITS focus on personalising pathways to learning particular pre-specified content…
• …rather than enabling people to personalising what they learn, in order to maximise student agency and prepare them for lifelong learning.
Introducing AIED: debunking some myths
• Intelligent Tutoring Systems adopt an instructionist/didactic/knowledge transfer approach to teaching and learning.
• This is retrogressive, and contested by the learning sciences. It under-values:
• knowledge construction,
• teacher/student engagement,
• student/student engagement,
• challenge and productive failure.
Introducing AIED: debunking some myths
“Unfortunately we didn’t have a good experience using the program, which
requires hours of classroom time sitting in front of computers... The assignments
are boring, and students feel as if they are not learning anything.... It’s severely
damaged our education, and that’s why we walked out in protest.”
Washington Post newspaper, November 2018
Introducing AIED: debunking some myths
• Automatic Writing Summative Evaluation aims to reduce teacher workload.
• We all know that marking assignments can be time-consuming (and sometimes boring) for teachers.
• But, this is also an opportunity for teachers to learn about their students’ individual capabilities.
• What will be the next teacher job to be taken over by AI?
Introducing AIED: debunking some myths
The promise and implications of AIED
In summary...
• AI is likely to have a major impact in education.
• AI has the potential to transform education –for good, but also for bad.
• It is important that educators, learning scientists and policymakers engage with computer scientists and AI developers, to ensure that AIED meets real needs.
The promise and implications of AIED
In summary...
• We should be bold and not limit our imagination to existing practices.
• But, we must not be seduced by the lure of exciting technologies.
• Instead, we should always start with the learning.
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