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?
