Thursday, 6 December 2018

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, 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?