artificial intelligence in education promise and...

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


1. The promise of AIED

2. The implications of AIED


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…

Student-facing AIED

The promise of AIED

• 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)

• Intelligent Tutoring Systems (ITS)

• Dialogue-based Tutoring Systems

• AutoTutor• CIRCSIM• SCHOLAR• Watson Tutor

The promise of AIED

Dialogue-based Tutoring Systems



• Exploratory Learning Environments

• Betty’s Brain• Crystal Island• ECHOES• Fractions Lab

The promise of AIED

Exploratory Learning Environments




• Automatic Writing Evaluation

• e-Rater• Intelligent Essay Assessor• OpenEssayist• PEG• Revision Assistant• Write to Learn

The promise of AIED

Automatic Writing Evaluation





• Learning Network Orchestrators

• Smart Learning Partner• Third Space Learning

The promise of AIED

Learning Network Orchestrators





• Learning Network Orchestrators

• Language Learning

• Babbel• Duolingoand many others...

The promise of AIED

Language Learning

Future student-facing AIED possibilities

• Collaborative Learning

• Adaptive group formation.

• Expert facilitation.

• Intelligent virtual agents.

• Intelligent moderation:

The promise of AIED



• 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




• 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




• 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

Teacher-facing AIED

The promise of AIED

?

• ?

Future teacher-facing AIED possibilities


• Responding automatically and instantly to standard student questions, allowing the teachers to concentrate on the human aspects of teaching.

The promise of AIED



• 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



• 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


4. The ethics of AIED is all about data

Introducing AIED: debunking some myths

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 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?


Pedagogical approaches?

Resource allocations (including teacher expertise)?

Gender and ethnic biases?

Behaviour and discipline?

Accuracy and validity of assessments?

Education


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.


5. Personalised learning is the best approach to AIED


Intelligent Tutoring Systems

privacy

transparency

unintended consequences

biasesinformed consent

ethics?

ethics?

ethics?

ethics?

ethics?ethics?


• 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.


• 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.


“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


6. Tools that reduce teacher workload is the next best AIED


• 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?


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.

Thank you for listening.

Dr Wayne [email protected]

artificial intelligence in education promise and...

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