GEF Madrid 2024: AI and Personalised Learning
Summary
TLDRIn this AI and personalized learning session, a panel of experts discusses the potential and challenges of tailoring education to individual needs. They explore the role of generative AI in creating adaptive learning experiences, the importance of understanding learners' profiles, and the balance between technology and human interaction in education. The discussion highlights the possibility of AI-driven feedback mechanisms, the need for a holistic understanding of learners, and the importance of human elements in teaching that AI cannot replace.
Takeaways
- 🧠 Personalized learning through technology aims to provide each learner with a tailored experience that considers their needs, pace, and learning style.
- ⏱ The moderator emphasizes the time constraint, highlighting that AI, despite its capabilities, must adhere to the laws of mathematics and time management during the panel discussion.
- 📚 George discusses the historical aspect of personalized learning and how generative AI presents a new approach to achieving the 'Holy Grail' of education, which is personalized learning at scale.
- 🕵️♂️ Alvin envisions a future where AI and wearable devices offer personalized learning experiences, potentially creating an 'apprenticeship' model that adapts to the individual learner in real-time.
- 🛠 Rafael suggests that AI can serve as the 'perfect 50% teacher', providing knowledge and answering questions but acknowledging that human teachers still play an irreplaceable role, especially in emotional and social aspects of learning.
- 🤖 Maria Isabel expresses both excitement and concern about AI's role in education, emphasizing the importance of maintaining human connection and the holistic development of students.
- 🔧 John describes a practical application of AI in providing feedback for hard-to-evaluate skills, suggesting that AI can offload cognitive load from teachers, allowing them to focus more on individual student needs.
- 🛡 Unice discusses the development of tools that work in conjunction with AI to enhance personalized learning, such as visual aids and multiple-choice question generation, catering to different learning styles.
- 🔄 George mentions the importance of student profile data in personalizing the learning experience, suggesting that integrating this data with AI can help in creating a more nuanced and effective learning environment.
- 🤝 The panelists agree that while AI has the potential to revolutionize personalized learning, it's crucial to balance technological advancements with a deep understanding of human needs and educational goals.
- 🚫 The discussion concludes with a reminder of the limitations of AI, particularly the importance of not losing sight of the human element in learning and the need for further exploration and professional development in AI for educators.
Q & A
What is the main topic of the session described in the transcript?
-The main topic of the session is AI and personalized learning, discussing its current state, future potential, and the role of generative AI in education.
What is personalized learning as defined by George in the transcript?
-Personalized learning, as defined by George, is the intention of providing each learner with a learning experience that reflects their needs, learning pace, and best approaches to be an effective learner, often through technology.
What is the 'Bloom's 2 Sigma difference' mentioned by George, and why is it significant in education?
-Bloom's 2 Sigma difference refers to the significant educational benefit achieved through one-on-one tutoring. It signifies the goal in education to replicate the personalized attention and effectiveness of a tutor in a scalable way using technology.
How does Alvin envision the future of personalized learning with AI and wearable devices?
-Alvin envisions a future where AI and wearable devices provide personalized learning experiences tailored to each individual's needs, potentially including multi-dimensional, multimodal presentations of information and real-time feedback based on the learner's responses and understanding.
What is the role of a teacher according to Maria Isabel, and how does AI impact this role?
-Maria Isabel believes the role of a teacher is to build connections with students, develop curiosity, agency, and a sense of belonging. AI impacts this role by potentially taking over some aspects of personalized learning and feedback, prompting reflection on what makes a good teacher and the human element in education.
What is the potential issue with students relying on AI for learning, as highlighted by Rafael?
-Rafael highlights the potential issue of conflict between what an AI tutor says and what a human professor teaches. It raises concerns about the credibility and consistency of information and the challenges of managing such conflicts in educational settings.
What is the current limitation of AI in providing personalized learning, as discussed by John?
-John points out that while AI can provide initial feedback and offload cognitive load from teachers, it currently lacks the ability to nurture creativity and respond to the individual student's needs beyond knowledge dissemination.
What is the importance of understanding the human element in AI and personalized learning, as emphasized by Rafael?
-Rafael emphasizes the importance of understanding what makes us human to ensure that AI does not get out of control and to identify the real benefits and problems. He suggests that we need to study humans holistically to better integrate AI into learning.
How does the panel view the potential of AI to scale personalized learning, as discussed by Unice?
-Unice discusses the potential of AI to scale personalized learning by creating tools that can cater to a large number of learners, providing immediate feedback, and using visual aids to cater to different learning styles, thus overcoming the limitations of traditional classroom settings.
What is the significance of student profile data in personalized learning, and how can it be integrated with AI, as mentioned by George?
-Student profile data is significant in personalized learning as it allows the system to understand the learner's needs, strengths, and weaknesses. George mentions that integrating this data with AI can help tailor responses and interventions to better suit the individual learner, enhancing the effectiveness of personalized learning.
What is the challenge of implementing AI in education, and how can it be addressed, as suggested by the panel?
-The panel suggests that challenges include technical limitations, resistance to change, and the need for professional development. Addressing these challenges involves continuous exploration and adaptation, fostering a mindset open to change, and providing educators with the necessary training to effectively use AI tools.
Outlines
😀 Introduction to AI and Personalized Learning
The session begins with an introduction to the topic of AI and personalized learning, emphasizing the importance of managing time given the number of panelists. The moderator highlights the potential of AI in education but also reminds participants of its limitations, particularly in relation to the laws of mathematics. The session aims to define personalized learning and explore how generative AI presents new opportunities in this field. George is invited to provide his perspective on personalized learning, its history, and the current impact of AI on its practice.
🔮 The Future of Personalized Learning with AI
Alvin discusses the future vision of personalized learning, likening it to an apprenticeship model that has been refined over time with the help of AI and wearable technology. He envisions a future where devices can perceive and adapt to an individual's learning needs, potentially using neural signals to gauge understanding and adjust the learning experience accordingly. The conversation touches on the idea of a 'perfect teacher' that is always available, leveraging immersive technology and AI to provide lifelong learning opportunities.
🤔 The Role of Teachers in an AI-Enhanced Classroom
The panelists explore the evolving role of teachers in the context of AI integration in classrooms. Rafael suggests that AI could serve as a '50% teacher,' handling knowledge dissemination while human teachers focus on emotional and social aspects of learning. Maria Isabel reflects on the importance of human connection in teaching and the potential loss of that with AI, highlighting the need for a balanced approach that utilizes AI as a tool without replacing the human element in education.
🛠 Practical Implementations of Personalized Learning
John shares his experience with implementing AI in education, specifically mentioning a pilot project for the World Bank where AI was used to provide personalized feedback on student assignments. He discusses the potential of AI to offload the cognitive load of teachers, particularly in evaluating complex skills, and to provide initial feedback that can later be refined by human teachers. The conversation also touches on the challenges of integrating AI into existing educational structures and the need for professional development in this area.
📚 Enhancing AI with Student Profile Data
Unice discusses the development of tools that work in conjunction with large language models to enhance personalized learning. These tools can cater to different learning styles, such as visual learners, by generating graphs or diagrams. They also support the learning process by providing a chain of thought reasoning and adapting to the learner's profile, which is maintained and updated separately from the AI's context window. The goal is to create a more nuanced and personalized learning experience that can scale to large numbers of students.
🧠 The Importance of Understanding Human Beings in AI Education
Rafael raises a philosophical question about the understanding of human beings in the context of AI and education. He emphasizes the need to study humans holistically to ensure that the benefits of AI in education are real and not just assumed. The conversation suggests that there is a risk of losing control of the AI revolution if we do not understand the human element it is intended to serve. The panelists agree on the importance of balancing technological advancements with a deep understanding of human nature.
🏫 Personalized Learning in Online vs. In-Person Settings
Maria Isabel and John reflect on their experiences with personalized learning in both online and in-person settings. They discuss the benefits of online learning in reaching a large number of students with limited resources and the challenges of maintaining personal connections in a physical classroom setting. The conversation highlights the potential of AI to enhance the learning experience in both environments and the importance of exploring its use in education.
🤖 The Potential of AI in Enhancing Classroom Interaction
The moderator proposes a hypothetical scenario where AI-enhanced glasses could provide real-time insights into students' learning states, allowing for more personalized classroom interactions. Maria Isabel expresses openness to exploring such technology, while acknowledging the resistance that might come from traditional teaching methods. The conversation suggests that while technology can offer new possibilities, changing mindsets and embracing these changes is also crucial in education.
📝 The Difference Between Information and Learning
In the final moments of the session, the panelists address the distinction between access to information and the process of learning. John emphasizes that information is just data until it is combined with personal experiences and reflection to form knowledge. Ricardo adds that much of the valuable knowledge in organizations is tacit and resides in people's minds, suggesting that collaborative intelligence is key to effective decision-making and learning.
Mindmap
Keywords
💡Personalized Learning
💡AI (Artificial Intelligence)
💡Generative AI
💡Learning Experience
💡Educational Technology
💡Immersive Technology
💡Knowledge
💡Learning Analytics
💡Curriculum
💡Teaching Assistant
💡Context Window
Highlights
The session emphasizes the importance of time management for panelists, highlighting the constraint of 45 minutes for nine people, equating to 5 minutes per person.
Personalized learning is defined as providing a learning experience tailored to an individual's needs, pace, and learning style, aiming to replicate the effectiveness of one-on-one tutoring.
Generative AI is presented as a new approach to personalized learning, with the potential to overcome scalability issues faced by previous technologies.
The discussion acknowledges the challenge of creating data models and interventions for personalized learning, given the difficulty in assessing student capabilities.
Alvin envisions a future where wearable devices and AI provide personalized learning experiences, adjusting content delivery based on real-time learner feedback.
Rafael suggests that AI could serve as a 'perfect 50% teacher', providing knowledge but acknowledging the limitations in understanding student emotions and reinforcement needs.
Maria Isabel raises concerns about the emotional connection in teaching, questioning how AI can replicate the human element of building relationships with students.
John shares his experience using AI to provide personalized feedback on student assignments, demonstrating a practical application of AI in education.
Unice discusses the development of tools that augment AI with additional software to cater to different learning styles, such as visual learners.
George emphasizes the importance of student profile data in personalizing the learning experience and the challenges of maintaining stateless interactions with AI.
The debate on the effectiveness of context windows in AI for personalization is highlighted, with differing opinions on their utility in learning.
Rafael stresses the importance of understanding human beings as the raw material for AI, advocating for a holistic study of humanity to ensure ethical AI development.
The potential of AI to democratize education by impacting millions of learners with minimal resources is underscored, especially in areas with limited educational infrastructure.
Maria Isabel discusses the ease of personalizing learning in a physical classroom setting due to the ability to build relationships and understand student interests over time.
The session concludes with a call to action for educational institutions to prepare for and adapt to the future of AI in personalized learning.
A provocative question is raised about the difference between access to information and actual learning, prompting a discussion on the role of AI in knowledge creation.
The importance of 'know-how' as a key source of knowledge that is difficult to codify in AI is mentioned, suggesting limitations in AI's ability to capture tacit knowledge.
Transcripts
Ai and personalized learning welcome
everyone to this session of AI and
personalized learning uh so we have a
distinguished set of panelists uh here
uh but it is also true that we have many
panelists so if they introduce
themselves we will eat into uh the time
that we have to learn from their wisdom
so uh I'm going to get right into it uh
I do want to remind uh the panelist that
AI is extraordinary but it hasn't defied
and eliminated the laws of mathematics
so 45 minutes nine people means 5
minutes per person so please keep that
in mind as we uh proceed so I want to
start with defining terms uh so I want
to uh ask uh what is uh personalized uh
learning uh and maybe George since you
have studied this for many many years uh
we know that it isn't new uh but maybe
the vehicle or at least the way in which
uh generative AI Works uh presents a new
uh way of doing it so if you could start
don't give us a whole history but if you
could start telling us what personalized
learning is and then uh why why is it
different now sure um so I'd say first
of all uh personalized learning through
technology is the intention of providing
each learner with a learning experience
that reflects her needs her pace of
learning uh her
best approaches to being an impactful
and effective learner and that typically
is a function of how quickly content is
presented the format in which content is
presented the depth at which content is
presented and so the intent and this has
long been a Holy Grail in the education
sector is personalized learning that
allows the a technology system to
achieve what's often referenced as
blooms 2 Sigma difference which is the
benefit of onetoone relationship between
a qualified capable tutor and between
the individual learner themselves so
that's a quick overview the definition
it's had a long history in computer
science as a domain CMU has been one of
the more active places everything from
intelligent tutoring systems to a range
of different models around how we might
guide and support students unfortunately
it typically doesn't scale it's very
intensive in terms of time and effort to
create the right First Data models and
then the right approach to uh trigger
interventions that the skill set of an
individual student and very hard to
assess the capability of a student so I
think that's where generative AI starts
to come in with some promise because
with the right uh prompting context
window and I'd argue data architecture
of a learner profile fed into those llm
interactions uh there's a prospect that
we can maybe get closer than we have
been in the past all right and just so
that I understand well the previous
Technologies were trying to personalize
learning by mapping a whole tree of if
you do this you do that and the promise
of generative AI is that you don't have
to specify all the paths that the
technology can help you with that it
roughly that's a so it's it's in some
ways a bit similar to uh if you're
familiar with the uh argument that Gary
Marcus has made for a while you know the
idea of uh symbolic AI which requires
heavy processing heavy articulation and
definition in advance versus neural
networks which or more closely mimic a
non-rule based human neural art
great thank you thank you so much George
all right so U now let's try to picture
what personalized learning looks like
I'm looking at you Alvin you you have a
you might have a vision of how this
might look like in five or 10 years uh
can you tell us um what what does that
experience actually looks like yeah so I
think beyond what Bloom did which was
essentially a one toone tutor uh I think
that concept isn't even you know that 10
20 years ago what we're talking about is
actually apprenticeship is something
that's been around for thousands of
years that's actually how we've been
learning long before we had universities
and that's what essentially one toone
learning is that that's what
personalized learning is you spend not a
class but years with somebody and to
understand the the the learning model
the habits the the the the skill sets of
that individual and then to change the
way you teach with it and I think that's
what we're coming to today is that with
with AI with with wearable devices we
will have an ability for the for there
not just to be neural network training
but for it to be personalized to every
individual because like the glasses I'm
wearing today it has microphone it has
CL uh cameras it can actually perceive
everything that I perceive and take that
information to help change the way uh
that the information presented to me in
fact um if you know in another year or
so this kind of a glasses there an
immersive uh device can can get to this
type of glasses it will also be able to
present information to me in a
multi-dimensional multimodal way and
that way it can actually change the way
that the materials presented to give me
the the most uh immersive way to get
that point across and it in fact if if
at some point we may even get some
neural signals from it to know am I
confused am I learning am I actually
understanding it and to adjust the
difficulty uh of that content so you
know all of this is coming so the the
the combination of immersive technology
with AI with sensing information is
going to allow us to have the perfect
teacher that is with you 24/7 not just
for kids but actually for all ages so
you have lifelong learning and and I
think that's what we really need to get
to is to allow every single individual
in the world to have a perfect tutor um
that is all knowing and takes into that
perspective that what do you need to
know at the time I don't know how many
people have read the book um Diamond age
U young ladies primer Neil Stevenson
anyway and there there the idea is that
Nell the the protagonist has a a tablet
that goes with her as she grows up from
a child to an adult and it it it keeps
teaching her lessons as she goes through
the story and I think that's what we are
going to have is because you know these
kind of glasses you're wearing
essentially all your waking moments and
it will be able to sense all the
information you have and it will be able
to give you realtime feedback in terms
of teaching you based on your
experiences and and that's what I think
the the the kind of ultimate potential
of personalized learning is all right
I'm going to do a followup in 10 seconds
you need to answer yes does that mean no
teacher human teacher in the
classroom um I I think that changes the
role of the teacher the role of the
teacher today is to go and give you a
bunch of facts put it on to board and
say okay 10 seconds are done all right
we're moving all right Rafael what's
your vision for how personalized
learning looks like in an actual setting
building on what you said for me is
going to be first of all for let's think
about what AI can do in the next two or
four months because in six months we
don't know okay so for me what they can
provide us of today is the perfect 50%
teacher okay I think there are plenty of
sin that never are going to be able to
do it Ai No AI is never going to be able
to understand if he needs a a positive
or enthusiastic reinforcement feedback
we're very far of that but in terms of
knowledge okay what for me is incredible
and it's in a fantastic opportunity is
to have an unlimited teacher for you
take economy no because you move to
depends of the of the subject the the
topic no but you take economies you can
put as of today everything that we know
about economy and have unlimited teacher
for each one of the students okay that
will create a lot of problems because
imagine when you have your professor and
when the and the student says no no my
my my p is saying another thing you know
so can create a lot of conflict but the
potential for 50% is immediate and is
there and this opportunity to have
unlimited interaction is fantastic
people from my experience learn when
they are searching not what they are
listening okay so and knowledge calls
for knowledge yeah so you can ask
question and questions and question and
you get the people to enter on that
Dynamic I think this is unmatchable but
again we going to have to manage the
confli and the yeah that's a powerful
Place CU When you're learning with an AI
tool you're asking questions which is
kind of much better than the alternative
and you have to be I mean from what we
have learned in the history in science
everything that can be done is done okay
yes here I think that the challenge for
educational institution is at what level
they able to control you know the
implementation or adoption of these type
of Technologies no I mean if you provide
for example a CH specialized on economy
you must be very confident that very
well curated information of course you
can even trust him on top of the
professor and you say that uh when you
say 50% you you're you're essentially
describing that chpt or similar
Technologies nowaday is equivalent to
the median teacher in terms of uh skill
and knowledge is that what you're
describing you can create the super
teacher because you can have I mean
there is a limit in the amount
understanding that you can have okay and
also you can create a teacher that has
no
ideological beliefs biases which is very
important but this is for 50% there is
another 50% that has to do with
emotional oh so 50% of the job of the
job of the teacher I'm don't it so I
won't lose my job immediately is what
you're saying okay as you as you have
noticed I'm worried about that a little
bit all right Maria Isabel I want to ask
you so you're an expert in education
and I want to ask you um I mean George
described personalized learning as a
Holy Grail what why is this so important
what why do you think we should we it it
seems like every time we talk about the
benefits of AI personalized learning
comes up uh even George's presentation
this morning towards the top of the list
why is this so important well first of
all I would like
to compliment you know your words I mean
this is is super exciting I mean I think
about the possibilities and and and it
makes me happy right as an educator I
see the possibilities in the classroom
on the other hand it makes me a little
bit sad right because I wonder like why
do I wake up every morning right why do
I show up in the classroom right why do
I build that connection with my students
if you know artificial intelligence can
do that for me right so we can't forget
that piece right like of course you know
like it's all about a balance right like
we can use it as a tool but we can't
forget you know like what makes us like
good teachers and my mission right like
I get up every day in the you know every
day I go to school um and I just want
to share the possibilities with my
students at the University I mean I want
for them to be to be the best teachers
ever right I want for them to be in the
classroom to listen to the students to
develop curiosity to develop agency to
develop a sense of belonging um to
develop joy in in the students so I
wonder like are those Mach machines
going to do that for for us right so so
yeah so it's exciting but at the same
time it has to make us reflect about the
kind of curriculum that we build right
and how can we develop the best skills
for these you know kind of jobs that the
future requires great great so Rafael is
saying don't worry because for that 50%
of the job you will still wake up every
day to do it okay okay great thank you
all right so I want to maybe move to how
examples of specific ways in which
teachers can or professors can use
personalized learning I think Alvin went
to the this is coming we're all going to
wear glasses and um in a classroom of
the future I think Raphael approximated
a little bit more like what we could do
today but I want to I want to ask
specific ways in which Educators can can
do this so uh let me start with John
John you you want to describe uh so is
this L on yeah so I I think that for me
I think when we talk about personalized
learning um it's it's s of this false
promise you know this unicorn that's
always out there it's because our
schools and universities just aren't
built for it these are these are
factories and it's kind of like walking
into a Starbucks You' be like oh my gosh
I anything I want in a Starbucks right
you have how many thousands of
combinations of stuff you can get out
there but at the end of the day you
still have Starbucks right and that's
the same thing that we have within
school so we've got fixed we've got
fixed curricula fixed bodies of
knowledge fixed expertise and now I
think George was touching on this that
we're about halfway there with uh
personalized learning because we can
provide feedback and this is where I
think that that things get really
interesting because we can offload much
of the cognitive load that teachers have
in providing feedback especially for
hard measure skills and competencies
like uh are you a creative uh Visionary
can you express um can you express
entrepreneurship can you can you express
some computational thinking now for a
teacher to evaluate these things these
These are really hard to evaluate and
take a whole lot of thinking a whole lot
of cognitive load with AI we can offload
a lot of that and provides at least some
initial feedback and then ease the role
of the teacher to actually focus on more
of the individual students needs um
based on that let me push a little bit
more can you do that today we can do it
today so I did uh when you say we you
mean like you and two other people or we
like everyone in this room okay so
anybody else can do this so I did a a
quick pilot for the World Bank uh for
their evoke project uh where I I don't
know really how to program with this
stuff I'm using PHP that's not my
language but it's used in mood right uh
so I asked chat GPT I said well here's a
problem I want to connect API to API
immediate between them and all has to be
in PHP which I really don't know so
using using a chat GPT I actually built
you know 50 50 lines of code that pulled
student assignments out of Moodle
portfolio posts or written essays and
then fed them through chat GPT uh
through a query to provide uh person
personalized feedback and then it spits
it back to mood as as a is an automated
response and the results were actually
really good this s this first take um
does it does it answer everything does
it s thing no but at least students get
that that initial feedback so if you're
doing a first draft or something they're
able to work on this stuff and bring to
a teacher later on for for Fuller
evaluation and I was really impressed
but I think that's just half that's half
the way because it doesn't really
respond to students creativity so what
do you want to learn where do you want
to go right it can provide feedback on
how you're doing but in in terms of
nurturing your curiosity it's not quite
there yet we have a ways to go I'm going
to push you a little bit more all right
so um you describe a process and I'm
sure people here are wondering why
didn't the students submit their thing
to chbt and get that feedback why do you
need to write 50 lines of code and do
all of that give us the answer to that
you know they absolutely could okay but
um this is I think this is very new for
students and I think that access to
technology is not quite equally
distributed uh but I think that it's
going to get there and so it's going to
be common place uh heard many times
already today people are concerned about
students uh writing through this stuff
but I think it's be common place to use
this stuff to augment students work uh
to maybe I think we have to get away
from creating the correct response to
creating your response and I think
that's the important parts of this
especially we put in the context of
personalized learning perfect thank you
thank you so much John okay I'm going to
move move to Faron I'm going to ask the
question in Spanish but it's the same
question that I asked
John for
vide
m
on
PR
okay so now I'm going to move um to
Unice so Unice the way that uh George
described for us personalized learning
at the beginning there are many aspects
of it but uh some of the aspects have to
do with uh being adapted to the the pace
that a student wants to use in their
learning their particular interests uh
their background on the topic and so on
so nowadays with uh chat gbt or similar
models a student can learn by
personalizing to themselves by simply
typing into the chat I'm this kind of
learner I have this background on the
topic and so on you seem to have spent
some time
creating
tools that are on top I imagine of some
of these llms or maybe independent of
them um could you tell us about the
tools that you have developed and how
they play into this yeah definitely
thank you so I'll share with you a
little personal Journey how I came into
Ai and personalized learning so we
started I started teaching on campus and
when you have you know 20 students 30
students you can give them personalized
feedback and it's very manageable even
in a class of 100 students or 200
students you have you know Tas and uh
you can give them personalized feedback
So eventually we started building muks
massive open online courses and when we
had like 1,000 2,000 students it was
still you know difficult but you can
still reply you can still give each one
you know where they're stuck uh fast
forward we ended up having around 1.4
million Learners and then it becomes
extremely difficult to cater to every
individual and that's where we started
building these tools and these tools uh
they end up helping the learner first of
all uh these tools they're not simple
chat GPT they're they tend to be
augmented with tools like you can
augment them with wallframe alpha or
calculators or different software and
you can also help with Chain of Thought
reasoning so you walk them how uh the
problems are solved the other problem
we've seen that also comes uh into play
when talking about Ai and personalized
learning is 65% of Learners are visual
Learners and what that entails it means
when you use a large language model it's
very difficult to uh create graphs or
plots or diagrams that are relevant to
that question so again with these tools
you can actually cater to the other 65%
of personalized Learners doing multiple
choice generation for example uh when
you have four potential answers each
answer is usually a different track or
you know a different like wrong lead
that's identifies a different concept so
with one multiple choice uh question you
could Target you know several skill sets
and quickly Target what the next
question will be so there are a lot of
different tools and strategies when it
comes to uh implementing these AI in
personalized learning great great great
thank you thank you okay I want to come
back to George George you said uh maybe
in passing but I think I think you meant
it more than in passing that it's not
just adapting to the PACE and background
of the students and so on but you
mention um student profile data as being
an important component can you describe
for us and and uh we have reached the
stage that I was hoping we reached which
is every one of you has spoken at least
once so now I want more interaction uh
so if you want to respond to George just
raise your hand and I'll call on you but
could you describe um is that possible
today and uh if it is how to do it and
if it's not what other barriers to get
there sure so uh one of the challenges
with uh llms is they're largely
stateless and uh the difficulty is if
you want a persistent profile of a
learner that you want to attend to
specific needs you need some indication
of who that learner is because you know
if you go to chat GPT ask it a question
go back same browser two seconds later
you'll get it sometimes reasonable
variations in your responses so that
means it doesn't care about you and it
doesn't know you and the whole point of
it education is for us to be known by
the system and a teacher to personalize
or you know as Alan was saying for for a
a mentor or an apprentice to be guided
in effective way so the way we've
approached it is we've said look we're
we're going to at least short term I I
don't believe that larger context
Windows like Gemini has is the solution
in the long run so my background I've
been active in development of learning
analytics as an academic discipline and
so we spent a lot of time understanding
which sequences of learner generated
data are indicative of
attention um wandering are indicative of
learner confusion disengagement and so
we developed a model where we're looking
at six attributes of a profile so
cognitive metacognitive affect social
emotional well-being and skill sets and
so what we want to do is say each of
those levels we've identified sort of
key variables that we want to work with
and identify that says you know George
understands this concept or George
manages time well and if I don't then
we're going to through an integration of
we're largely just passing it in context
windows to the llm that says this is
George George's profile Georgia's
current state uh this gets fed here at
responds the outcome of that we're
pulling it back as a text file into an
S3 bucket it gets analyzed fed back to
the profile and so that's the model
we're saying look llms have a lot of
potential to appear to be intelligent
but when we're teaching there's much
more Nuance about a profile so that's
the mechanism we're utilizing to help
integrate and accelerate the
capabilities of the llm in that approach
great so your your platform passes on in
essence the profile information to the
llm so that the response is tailored to
that profile great thank you can I
respond a little bit um I I think it's
that essentially what you're doing is
extending the context window right
because even even with even with Gemini
totally different well yes and no I mean
if the context Windows 100,000 tokens
then you're right it's very different
but if you get to like's say long net
which is a billion tokens right then
then essentially you have a a long-term
memory of that conversation in fact if
you look what it's not the memory it's
the ability to create attributes of
profiles from data that are disconnected
from Context it's actually saying this
sequence of pattern says you are an
individual who learns in this way you've
shown cognitive deficiency in
understanding a key topic for this
reason yeah but if you have enough of a
context then you essentially it it
should be able to to grasp that type of
understanding if if it's if you're doing
uh you know interactive discussions with
the learner then it it should be able to
know whether are right or wrong or
you're doing you know quizzes in between
so I'll just make quick it should is a
very big future word I can tell you
right now I can build a profile of your
interaction data while you're engaging
in a learning process that indicates
your mind is wandering or you understood
a concept I don't need a large context
window in the big context window there's
a fair bit of controversy on whether a
context window solves that even with the
1.5 million you know size token context
window still the benefit of rag or graph
rag Technologies to make sense a local
Iz data makes a lot of sense so I think
a context window as a personalization
mechanism that's disconnected from a
profile I'm not convinced of if it
happens I'll happily change my mind but
I know what we can do today yeah all
right wait wait moderator has it right
here it's getting a little bit technical
you two go and have a cup of coffee uh
John you want to you want a rejoinder
here I was just going to say I just want
to break up that fight there say it's a
no no no no it's not a fight but they
the disagreement is at a level where 80%
of people in the room might not be
following so I just exactly but but
here's here's the thing though for all
us who don't follow right because we
talk about whether context windows work
or not what you need Etc all right
here's the thing if it doesn't work now
it will work in the near future and the
important thing for for schools
universities is that finally we have a
heads up on what the future is going to
be like we have we have a chance to
prepare we have a chance to act now
right so whether or not we can debate
whether or not what what Works what
doesn't work but we have a vision for
the future we have a vision for the
technologies that that we are developing
and we're going to get there that's it
you know every time you say we I'm like
am I part of this we or not who is we
yeah yeah you have many sleepless nights
now okay okay all right all right so uh
Rafael you want
to there's something that does it sounds
there I mean we're talking all the time
about the potential benefits and the
potential risk about artificial
intelligence in human no but I have a
something that distracts me like a major
question what do we know really about
the human beings okay because I found
interesting what the Anthropologist
start saying this morning
but where do we know where can we
find a deep
understanding of what as of today is
known not known what is debate and what
is fake about the human beings because
we're talking about the raw material
that's going to use it yeah and I my my
concern is to make sure that this
doesn't get out of control we are going
to have to come back to understanding
what human what what what makes us human
we had a talk about that this morning
and you have a startup to figure this
out I think this is a challenge we going
to have to face in the world okay I
think since and nit kill the human being
at the end of the you know 18th
century uh we have a stopped to study
human being as a whole in holistic way
okay so I cannot spend 60 years going
through the 40 degrees in Humanities and
still on top of that we'll have to study
biology Neuroscience we have to come
back a better understand the human
beings because if not we are going to
jump to the potential
benefits and we're not going to
understand what are the real benefits
and the real problems no so not
everything to make sure that this which
is absolute the revolution
doesn't get out of control going to have
to in parallel try to much better
understand human being and to be able to
convey the knowledge in a very
synthetized clear actionable way which
is something that we're not looking for
maybe because it's a huge task but I
don't think it's Huger that we have we
have done with the with the Homa no or
with other similar technology okay thank
you thank you right I want to come to
the two of you now so um you have done
both teaching and person in online but
I'm going to I'm going to assign you as
a online role and you care about this
little kids as human
beings uh and teach in person so I want
to understand the potential of
personalized learning online relative to
in person so can you can you reflect
since youve done both and then I want
you to come back marel with how do you
personalize learning with the help of AI
or maybe not uh thank you definitely so
online uh one of the you know Main
benefits uh of personalized learning is
that you don't need to have an army of T
who would go and help each indivual
teaching assistant yes a teaching
assistant thank you uh who are going to
go and you know answer each individual
learner's question and you can with one
person you can impact you know millions
of Learners and this is exactly what
we're seeing today in government that we
work with that from a very centralized
governments within the Ministry of
Education for example they could build a
simple software and then suddenly impact
7 million Learners especially where they
don't have you know uh capabilities to
improve the education system classes are
crowded uh you know student to teacher
ratios aren't as as good and this is one
of the most beautiful things about Ai
and personalized learning how you need
you know maybe a team of two or three
people at most and you can have a
massive impact and that's exactly what
we've seen online with massive open
online courses and completion rates
thank you thank you very much all right
yeah well can you can you do that in a
physical classroom yes I can I mean I
had to say that you know being in this
conversation is making me think about
how little I know about artificial
intelligence right I I haven't gotten
any kind of training and we need more
professional development around this
topic right and in person it's a little
bit easier right I have been teaching
online as well and I know the challenges
is super hard for me in person is easier
right because I have the the the the the
ability to I mean I have the possibility
to connect with my student right during
you know a whole semester I can get to
know them I can um I I learn from them
right I I know what they are interested
in right I know their passions so I can
build my curriculum around them right so
it's easier in that how many students do
you have well I have um a group of 30
people so e year exactly so we you know
we do like um large discussions you know
like a small group discussions um so
that give us the opportunity to know to
to get to know each other yeah um
artificial intelligence I have been
using it a little bit right I use it for
um the development of my own syllabus um
and also I let them mus it right right
we have to explore it um but I don't use
it in the sense that okay um use it yes
to get the result use it for the process
right of that investigation we are you
know doing an inquiry so yeah let's
explore it let's use it right let's
learn uh but um but yeah for for me you
know like I'm not focused on the result
because you know I don't care I'm in the
machine can do it for us right so I'm
focusing more on developing the skills
during that process of learning let me
ask you one question suppose yeah I'll
I'll go to you in a second so suppose
that we could transport his glasses to
you
in the glasses have more capabilities
can you imagine going from a classroom
of 30 to a classroom of 60 where maybe
it's a little bit harder to get to know
your students but when you are teaching
and you look at a student all of a
sudden there's something that tells you
something about that student that would
allow you to interact with them is that
like no way I can't handle too much
technology or how are we think about I'm
happy to explore it like everything
right like yeah the glasses you want to
give it to her okay all right yeah happy
to learn yeah for sure okay you would
explore it yeah I would explore it yeah
it's like you know would your colleagues
do it would your colleagues do it m
I don't think so I mean resistance is
there right like even to change people
how to teach right like some people
teach more in a traditional way right so
to change that is even hard right
because that's how they learned um but
uh yeah so then if you add technology it
might be a little bit more difficult but
uh who knows right like we have to
believe that people can change their
mindset right that's why we are in
education
so yeah great thank you techology is
available today that technology is
available today all right we you we're
going to we're going to match you up you
have coffee with him for your Technical
and then with her for for the classroom
person
okay um we have 90 seconds left if you
have something on your chest you want to
get
out all right so I have one more
question maybe you can so um this
session was about personalized learning
but we didn't personalize learning to
the people who were here
so what what could we have done with the
technology of today and with the
constraints that we had in this room to
personalize the session to the people in
this room them to ask a question maybe
uh allow them to ask a question is a
good idea but we only have a minute uh
so I'm I'm uh first person I ask a
question wins all right there's a
question there I'll be a little
provocative I touched it You' talked
generally about access to information is
access to information learning is could
be interpreted as like a super a super
elaborate Google right with a very
elaborate very great how is that
connected to learning okay John you seem
you seem ready for this one no just
simply having access to I'm going to try
to be really quick Simply Having access
to information isn't isn't learning
because it doesn't mean anything mean
information is built from data but to
create learning you have to build
knowledge which is incorporating you
know the tcid experiences you get from
day-to-day life plus this explicit stuff
that you get from from tapping into
information smartly you combine these
two things together they got personal
learning they got some learning but
otherwise information is just you know
it's it's chewed up
data I think you ra a very important
issue which is we are assuming all
information is written yeah so AI is
learn to access this information no and
have the problems how we going to De it
with it but we have analyzed 4,000
decision that were made by 60 large
organizations in the last 5 years we
found 80% of the knowledge was on the
minds people yeah and WID spread on
average in group of 20 to 25 people okay
so this is very important because we're
assuming you know that everything is
written and it's not okay when you come
to close decision a specific decision a
specific situation the knowledge is
still a lot of the knowledge in the
people's minds so we find new ways
to have collaborative intelligence which
is something we are very bad on it
because teams or this is impossible to
interact with people and they have the
knowledge thank you the Ricardo hman and
my colleague at the Harvard Kennedy
School uh speaks of knowhow as being
kind of the key um source of uh
knowledge that helps country uh
countries develop so to the extent that
know how is hard to codify in in AI uh
that certainly limitation all right uh
speaking of limitations it's 5:00 we
need to close this session but please H
give a big round of applause to our
marvelous panelist thank
you all
righty if you very thank you so thank
you thank you thank you
تصفح المزيد من مقاطع الفيديو ذات الصلة
GenAI + Education Welcome and Fireside Chat
Why AI Won't Replace Teachers
Interview with Dr. Paul Kim, CTO Stanford University Graduate School of Education
GEF Madrid 2024: Globalising Education with AI
Panel: The Importance of AI-Literacy for AI in Education
GEF Madrid 2024: Financing Education in an AI era
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