EU report on the impact of AI on Learning Teaching and Education #AI #education #EU #policy

The resently published report on the impact of artificial intelligence (AI) on learning, teaching and education gives a great outline on the realities of AI, the state of the art, and the challenges as well as opportunities for those of us with an expertise in learning in general, or learning in terms of learning theory. The report is part of the JRC Science for Policy documents, and it is very well written by Ilkka Tuomi (who is renowned for his expertise in Internet, data, AI and computer science). Ilkka recorded a brief overview of the report, which can be seen below. In the report-related video, he refers to current machine learning systems as datavors, he defines (and right fully so) the term of machine learning as an oxymoron and he puts current AI in very accessible parallel, namely the Artificial Instict (as current AI is mainly about behaviourist approaches and patterns).

A very interesting perspective is that Ilkka and the report stress the importance of having someone on board of AI for learning/teaching/education on board, who has expertise in learning and learning theory.

The policy challenges mentioned at the end of the report are:

• A continuous dialogue on the appropriate and responsible uses of AI in education is therefore needed.
• In the domain of educational policy, it is important for educators and policymakers to understand AI in the broader context of the future of learning. As AI will be used to automate productive processes, we may need to reinvent current educational institutions.
• In general, the balance may thus shift from the instrumental role of education towards its more developmental role.
• A general policy challenge, thus, is to increase among educators and policymakers awareness of AI technologies and their potential impact.
• Learning sciences could have much to offer to research on AI, and such mutual interaction would enable better understanding about how to use AI for learning and in educational settings, as well as in other domains of application.
• As there may be fundamental theoretical and practical limits in designing AI systems that can explain their behaviour and decisions, it is important to keep humans in the decision-making loop.
• The ethics of AI is a generic challenge, but it has specific relevance for educational policies.
• Human agency means that we can make choices about future acts, and thus become responsible for them.  AI can also limit the domain where humans can express their agency.
• An important policy challenge is how such large datasets that are needed for the development and use of AI-based systems could be made more widely available.

This 47 page report offers the following topics:

Introduction ...................................................................................................... 5
2 What is Artificial Intelligence? ............................................................................. 7
2.1 A three-level model of action for analysing AI and its impact ............................. 7
2.2 Three types of AI ....................................................................................... 10
2.2.1 Data-based neural AI ......................................................................... 10
2.2.2 Logic- and knowledge-based AI ........................................................... 12
2.3 Recent and future developments in AI .......................................................... 13
2.3.1 Models of learning in data-based AI ..................................................... 15
2.3.2 Towards the future............................................................................. 16
2.4 AI impact on skill and competence demand ................................................... 17
2.4.1 Skills in economic studies of AI impact ................................................. 18
2.4.2 Skill-biased and task-biased models of technology impact ....................... 20
2.4.3 AI capabilities and task substitution in the three-level model ................... 21
2.4.4 Trends and transitions ........................................................................ 22
2.4.5 Neural AI as data-biased technological change ...................................... 23
2.4.6 Education as a creator of capability platforms ........................................ 23
2.4.7 Direct AI impact on advanced digital skills demand ................................ 25
3 Impact on learning, teaching, and education ....................................................... 27
3.1 Current developments ................................................................................ 27
3.1.1 “No AI without UI” ............................................................................. 28
3.2 The impact of AI on learning ....................................................................... 28
3.2.1 Impact on cognitive development ........................................................ 30
3.3 The impact of AI on teaching ....................................................................... 31
3.3.1 AI-generated student models and new pedagogical opportunities............. 31
3.3.2 The need for future-oriented vision regarding AI .................................... 32
3.4 Re-thinking the role of education in society ................................................... 32
4 Policy challenges ............................................................................................. 34

Below is the 20 minute video of Ilkka Tuomi which explains the report in easy terms.

Session on #AI, #machineLearning and #learninganalytics #AIED #OEB18

This was a wonderful AI session, with knowledgeable speakers, which is always a pleasure. Some of the speakers showed their AI solutions, and described their process; others focused on the opportunities and challenges. Some great links as well.

Squirrel AI, the machine that regularly outperforms human teachers and redefines education by Wei Zhou

Squirrel AI is an AI to respond to the need for teachers in China. Based on knowledge diagnosis, looking for educational gaps. A bit like an intake at the beginning of a master education for adults.

Human versus machine competition for scoring education, and tailored learning content offerings. (collaborates with Stanford Uni). Also recognized by Unesco. (sidenote: it is clearly oriented at 'measurable, class and curriculum related content testing). 

 The ideas behind AI: adaptive learning is a booming market.

Knowledge graph + knowledge space theory: monitoring students real-time learning progress to evaluate student knowledge mastery and predict future learning skills. based on Bayesian network plus Bayesian inference and knowledge tracing and Item Response Theory. The system identifies the knowledge of the student based on the their intake or tests. Based on big data analysis the students get a tailored learning path. (personalised content recommendation using fuzzy logic, classification tree, and personalized based on logistic regression, graph theory, and genetic algorithm.). Adaptive learning based on Bayesian network, plus Bayesian inference, plus Bayesian knowledge tracing, plus IRT to precisely determine students current knowledge state and needs.

Nanoscale Knowledge Points: granularity is six time’s deeper.  Used in medical field.

Some experiments and results: the forth Human versus AI competition, which resulted in AI being quicker and more adapt to score tests of students.  Artificial Intelligence in Education conference (AIED18 conference link, look up video youtube.com, call for papers deadline 8 February 2019 for AIED19 here).

Claus Biermann on Approaches to the Use of AI in Learning

Artificial Intelligence and Learning: myths, limits and the real opportunities.  

Area9 lyceum: also adaptive  learning long-term company with new investments.

Referring to Blooms 2sigma problem.

Deep, personalized learning, biologically enabled data modeling, four-dimensional teaching approach.

How we differ: adaptive learning adapts to the individual, only shows content when it is necessary, takes into consideration what the student already knows, follows up on what the student is having trouble with.  This reduces the time of learning, and increases motivation. Impact from adaptive learning, almost 50% reduction of learning time.

Supports conscious competence concept.

AI is 60% of the platform, but the most important part is the human being, learning engineers, the team of humans who work together makes it possible.

Marie-Lou Papasian from Armenia (Jerevan).

Tumo is a learning platform where students direct their own development. After school program, 2 hours twice a week, and thousands of students come to the centre of TUMO. Armenia and Paris, and Beirut.

14 learning targets ranging from animation, to writing, to robotics, game development…

Main education is based on self learning, workshops and learning labs.

Coaches support the students and they are in all the workshops and learning labs.

Personalisation: each students choose their learning plan, their topics, their speed. That happens through the ‘Tumo path’, which is an interface which enables a personalised learning path (cfr LMS learning paths, but personalized in terms of speed and choices of the students). After the self-paced parts, the students can go to a workshop to reach their maximum potential, to learn and know they can explore and learn. These are advanced students (12 – 18 years, free of charge).

Harnessing the power of AI: the AI solves a lot of problems, as well as provide freedom to personalise the students learning experience. A virtual assistant will be written to help the coaches to help the student guided through the system.

AI guided dog: a mascot to help the students.

The coaches, assistants… are their to learn the students to take up more responsibility.

For those learners who are not that quick, a dynamic content aspect is planned to support their learning.

Wayne Holmes from the OU, UK and center for curriculum redesign, US

A report commissioned about personalized learning and digital ... (free German version here , English version might follow, will ask Wayne HOlmes).

Looking at the ways AI can impact education

A taxonomy of AI in education

Intelligent Tutoring System (as examples mentioned earlier in the panel talk)

Dialogue-based tutoring system (Pearson and Watson tutor example)

Exploratory Learning Environments (the biggest difference with the above, is that this is more based on diversification of solving a specific problem by the student)

Automatic writing evaluation (tools that will mark assignments for the teachers, also tools that will automatically give feedback to the students to improve their assignments).

Learning network orchestrators (tools that put people in contact with people, e.g. smart learning partner, third space learner, the system allows the student to connect with the expert).

Language learning (the system can identify languages and support conversation)

ITS+ (eg.. ALP, Alt school, Lumilo. The teacher wears google glasses, and the students activity comes as a bubble visualizing what the student is doing).

So there is a lot of stuff already out there.

We assume that personalized learning will be wonderful, but what about participative or collaborative learning

Things in development

Collaborative learning (what one person is talking about might be of interest to what another person is talking about).

Student forum monitoring

Continuous assessment (supported by AI)

AI learning companions (e.g. mobile phones supporting the learning, makes connections)

AI teaching assistants (data of students sent to teachers)

AI as a research tool to further the learning sciences

The ethics of AIED

A lot of work has been done round ethics in data. But there are also the algorithms that tweak the data outcomes, how do we prevent biases, guard against mistakes, protect against unintended consequences….

But what about education: self-fulfilling teacher wishes…

So how do we merge algorithms and big data and education?

With great power comes great responsibility (Spiderman, 1962, or French revolution national convention, 1793)

ATS tool built by Facebook, but the students went on strike (look this up).

Gunay Kazimzade Future of Learning, biases, myths, etcetera (Azerbaijan / Germany)

Digitalization and its ethical impact on society.

Six interdisciplines overlap.

Criticality of AI-biased systems.

(look up papers, starting to get tired, although the presentation is really interesting)

What is the impact of AI on our children is her main research considerations. How is the interaction between children and the smart agents. And what do we have to do, to avoid biases while children are using AI agents.

At present the AI biases infiltrate our world as we know, but can we transform this towards less biases?