Summit George Siemens Becoming an AI University

Summary

George Simmons discusses the transformative potential of AI in higher education, emphasizing the need for universities to adapt and integrate AI to remain relevant. He outlines key areas where AI can enhance learning, such as personalized education and administrative efficiency, while also highlighting challenges like ethical considerations and curriculum development. Simmons advocates for an 'AI-first' university approach, where AI is embedded in all organizational aspects, from infrastructure to research, to better prepare students for a rapidly evolving world.

Takeaways

• 🧠 George Simmons discusses the intersection of human and artificial cognition in knowledge processes and the role of AI in education.
• 🎓 Simmons is a co-founder, chief scientist, and architect of SNH use human system, which focuses on the impact of AI on learning and wellness.
• 🤖 He critiques the higher education sector's response to AI, suggesting a misjudgment and a need for more proactive engagement with AI technologies.
• 📚 Simmons highlights four main topic areas: literature on AI and learning, the intersection of human and machine cognition, current AI trends, and the implications for universities.
• 🌐 He emphasizes the importance of networks in human cognition and how AI is becoming a node within our cognitive networks, affecting how we learn and process information.
• 🔬 Research suggests AI's role in learning is not just as a tool but as a transformative agent that requires a re-evaluation of human learning theories.
• 🏫 The practical applications of AI in classrooms are primarily adaptive systems, personalization, profiling, prediction, and assessment, with tutoring emerging as a significant area.
• 🛠 Challenges introduced by AI in education include ethical concerns, curriculum development, and infrastructure questions.
• 🔑 The benefits of AI in education include personalized learning, improved administrative processes, and more effective assessment methods.
• 🔮 Looking forward, Simmons envisions an 'AI-first' university where AI is integrated into all organizational aspects, from infrastructure to research.
• 🌟 The future of education with AI involves addressing the ethical dynamic, building institutional AI capabilities, and fostering a multi-institutional collaboration for data sharing and learning.

Q & A

What is the main topic of George Simmons' discussion?

The main topic of George Simmons' discussion is the significant misjudgment by higher education in utilizing AI for innovation and change within the university sector.

What are the four distinct topic areas Simmons plans to address in his talk?

Simmons plans to address literature around AI and learning, the intersection between human and machine cognition, current trends in AI, and the implications of AI for universities, including potential changes and areas of priorities.

How does Simmons describe the impact of new technology on society's emotional and mental health?

Simmons describes the impact as a 'spacing effect,' where new technology like social media initially connects people but can lead to disconnection and negative mental health effects due to algorithmic distortion and propaganda.

What is the role of AI in the cognitive network according to Simmons?

According to Simmons, AI is not just a tool or resource but a node within our cognitive network, which has significant implications for education and how we understand and interact with knowledge.

What is the importance of considering AI as a mediating and transforming agent in human learning?

Considering AI as a mediating and transforming agent in human learning is important because it necessitates a re-evaluation of our understanding of human learning and knowledge growth, taking into account AI's potential to change how we engage with and process information.

What are some of the practical applications of AI in classrooms as discussed by Simmons?

Some practical applications of AI in classrooms include adaptive systems and personalization, profiling and prediction of student outcomes, assessment and evaluation, and tutoring.

What are the challenges introduced by AI in the educational landscape?

Challenges introduced by AI in the educational landscape include ethical dynamics, ensuring the integrity and security of student data, curriculum development to incorporate AI effectively, and infrastructure questions related to AI deployment.

What does Simmons suggest is the key to successfully integrating AI into the university system?

Simmons suggests that getting the data and related architecture right is key to successfully integrating AI into the university system, emphasizing the importance of institutional capability, leadership, policy, governance, and adaptive teaching methods.

How does Simmons view the future of AI in relation to human interaction?

Simmons views the future of AI as one where AI will impact every aspect of university operations, potentially leading to an 'AI first' university that integrates AI into all organizational aspects, from infrastructure to research.

What are some of the emerging trends in AI that Simmons highlights?

Some emerging trends in AI that Simmons highlights include the prevalence of AI in various technologies, the growth of open-source LLMs, the integration of AI with traditional robotics, the rise of wearable devices with AI capabilities, and the development of platform technologies that simplify AI development.

What is the significance of the term 'learner co-pilot' mentioned by Simmons?

The term 'learner co-pilot' signifies the idea of AI providing personalized support to learners, acting as a guide or co-pilot in their educational journey, enhancing the learning experience by meeting individual needs beyond just cognitive aspects.

Outlines

00:00

🤖 AI's Role in Higher Education Transformation

George Simmons discusses the underutilization of AI in universities and its potential for innovation. He emphasizes the need for higher education to adapt and embrace AI to ensure student success and institutional relevance in a rapidly changing world. Simmons outlines four key topic areas for discussion: literature on AI and learning, the intersection of human and machine cognition, current AI trends, and the implications for universities. He also addresses societal challenges, such as emotional and mental health, and the impact of technology on social connection and well-being.

05:00

🧠 Rethinking Human and Artificial Cognition

The speaker explores the concept of cognition beyond the brain, highlighting the role of networks and AI systems in our cognitive processes. He argues that AI should be viewed as a node within our cognitive network rather than a separate tool or resource. Simmons discusses the importance of understanding AI's impact on learning, particularly in areas of metacognition, emotion, trust, and social practices. He also touches on the need to re-evaluate our understanding of human learning and knowledge growth in the context of AI integration.

10:02

📚 Practical Applications and Challenges of AI in Education

Simmons reviews the practical applications of AI in educational settings, such as adaptive systems and personalization, profiling and prediction, assessment and evaluation, and the emerging role of AI tutors. He acknowledges the benefits of AI in enhancing personalized learning and administrative efficiency but also warns of the significant ethical and methodological challenges that accompany AI integration. The speaker identifies research gaps and the need for a more holistic understanding of AI's impact on student learning and engagement.

15:04

🛠️ AI's Impact on University Operations and Infrastructure

The speaker discusses the transformative potential of AI across all university operations, from knowledge generation to communication. He identifies trends in AI development, such as the rise of generative AI, multimedia integration, and the growth of open-source AI tools. Simmons also addresses the implications of AI for university infrastructure, emphasizing the importance of data architecture and the need for universities to develop a robust AI strategy that includes ethical considerations and curriculum development.

20:05

🌐 The Future of AI in University Systems

Simmons envisions an AI-first university where AI is integrated into all organizational aspects, from infrastructure to admissions, teaching, assessment, curriculum, and research. He outlines six areas of focus for universities to consider when adopting AI, including building institutional AI capability, leadership and governance, adaptive and responsive teaching methods, and accelerating research through AI utilization. The speaker stresses the importance of getting data architecture right and the potential for multi-institution collaboration in AI development.

25:07

🏛️ Redefining Education in the Age of AI

In the concluding remarks, Simmons reflects on the broader implications of AI for education, suggesting a shift from teaching knowledge to developing human beings who can navigate complexity and engage with non-human forms of intelligence. He calls for proactive measures to anticipate and mitigate potential harmful effects of AI while leveraging its benefits to enhance learning and societal engagement.

Keywords

💡Artificial Cognition

Artificial cognition refers to the simulation of human cognitive processes by artificial systems, such as AI. In the video, it is discussed as intersecting with human cognition in knowledge processes, emphasizing the integration of AI in learning and educational innovation.

💡AI in Education

AI in Education denotes the use of artificial intelligence technologies within educational settings to enhance learning experiences. The speaker criticizes the slow adoption of AI in universities and argues for its potential to innovate and personalize learning.

💡Misjudgment

In the context of the video, misjudgment refers to the underestimation or incorrect assessment of AI's impact on higher education. The speaker believes that universities have not fully embraced AI's potential for educational transformation.

💡Adaptive Systems

Adaptive systems in education are AI-driven tools that adjust to individual learners' needs, providing personalized learning experiences. The script mentions these systems as a key application of AI in classrooms.

💡Metacognition

Metacognition is the awareness and understanding of one's own thought processes. The video discusses metacognition in the context of AI, exploring how AI can interact with human self-regulation and learning management.

💡Ethical Dynamics

Ethical dynamics pertain to the moral implications and challenges introduced by AI, such as ensuring that AI applications do not harm people and maintaining data security and integrity. The speaker identifies ethics as a significant challenge in AI integration.

💡Generative AI

Generative AI refers to AI systems capable of creating new content, such as text, images, or music. The video mentions the growth of generative AI and its practical groundings in various applications, including tutoring and adaptive systems.

💡Personalized Learning

Personalized learning is an educational approach tailored to individual students' needs, abilities, and interests. The script highlights the benefits of AI in facilitating personalized learning experiences.

💡AI-First University

An AI-First University is a concept where AI is integrated into all aspects of the university's operations, from infrastructure to research. The speaker suggests that universities should adopt an AI-first approach to remain relevant and innovative.

💡Digital Ecosystem

A digital ecosystem in education refers to the interconnected digital tools and platforms that facilitate learning and research. The video emphasizes the importance of considering the entire classroom as a learning ecosystem for effective AI integration.

💡Pedagogical Approach

Pedagogical approach refers to the methods and strategies used in teaching and learning. The script discusses how AI can be integrated into various pedagogical models to enhance student engagement and learning outcomes.

Highlights

George Simmons is a co-founder, chief scientist, and architect of SNH use human system, focusing on the intersection of human and artificial cognition in knowledge processes.

Higher education has shown a fatigued response to AI's potential for innovation in the university sector over the last decade.

AI should be seen as a mechanism to ensure student success and keep universities relevant in a rapidly changing world.

Literature on AI and learning suggests that AI can enhance learning processes through personalized systems and adaptive learning.

The intersection between human and machine cognition is a co-creation process, not an antagonistic one.

AI's role in education is not as a tool but as a node within our cognitive network, affecting how we understand and engage with knowledge.

Research indicates that AI can help in complex problem-solving by integrating human and artificial cognition effectively.

Adaptive systems and personalization are the primary applications of AI in current educational settings.

Ethical dynamics, curriculum development, and infrastructure questions are significant challenges introduced by AI in education.

AI's impact on student focus and engagement shows that students rely on AI rather than learning from it, indicating a need for pedagogical adaptation.

The prevalence of AI in various sectors suggests that it will impact all aspects of a university's operations.

AI tools are beginning to unbundle the university system, offering knowledge generation and communication through various providers.

The concept of an 'AI first University' involves AI in all organizational aspects, from infrastructure to research.

Building institutional capability with AI is crucial, including understanding AI's functions and having the technical capacity to utilize it.

Adaptive and responsive teaching methods are becoming prevalent, with AI providing personalized support for learners.

The acceleration of research through AI utilization is changing the landscape of academic inquiry.

Data architecture and institutional infrastructure are critical for successful AI deployment in universities.

Multi-institution collaboration and data sharing are recommended for universities to learn from each other in the AI space.

Education is moving from teaching knowledge to developing human beings and helping them navigate complexity and engage with AI.

Transcripts

[Music]

he's George CS um I think they are

getting ready with all the last details

before taking the stage so let me

briefly introduce him uh he researches

how human and artificial cognition

intersect in knowledge processes he's

also a co-founder a chief scientist and

architect of SNH use human system that

is an organization building resources to

to respond to systems impact on AI on

learning and also Wellness I think we're

ready here now are you jge okay please

come to the stage welcome him George

Simmons thanks so much for joining

us uh good morning and uh appreciate the

opportunity to spend some time talking

about what I think is a significant

misjudgment on the part of higher

education over the last certainly

several years but likely going back well

over a decade and that is a somewhat

fatigued and even baguer response to AI

as a mechanism for changing and

innovating the university sector as a

whole so I'm going to talk through what

I think is happening and what I think we

need to do as universities to be more

responsive and more capable to utilize

AI as again a mechanism M for ensuring

our students are successful but also

ensuring that universities continue to

remain relevant in a pretty quickly

changing world I'm going to talk about

four distinct topic areas the bulk of

the talk I'm going to look at some of

the literature around Ai and learning

and this is just to give you a bit of a

sense on what do we know from literature

that works well in learning and learning

related processes I'm going to build a

little bit on what Charles was just

talking about which is the intersection

between human and machine it's a

co-creation process not necessarily

antagonistic process I'm going to talk

very briefly two slides worth about AI

specifically and I'm just going to

detail what it is that AI does and what

some of the current trends are that

we're seeing in AI I assume everyone in

the audience doesn't need the 500th what

is AI primer so I'm just going to talk

about what's happening right now

specifically around llms that have an

educational implication I'm going to

from there go a little bit about what

does this mean specifically from a

university lens and how universities

might change and then finally I'll

present sort of a six area of priorities

that universities need to pay attention

to if they want to start getting more

actively involved in the AI conversation

so to get

started we're at an interesting time in

history in that we've are sort of at the

tail end of an extended period of

emotional turmoil as a society uh we

have seen a significant increase in

escalation in areas of emotional need or

in areas of loneliness and mental health

impacts are certainly growing not only

limited to the effects of uh the

pandemic but just stats and indicators

prior to the pandemic that said hey as

people were not doing okay emotionally

and mentally some of the systems that

Society has created for us aren't

serving all of us equitably and that's a

significant Challenge and so there's

ways that we need to be better in how we

support and engage with Society r large

not just with individual learners but

what happens is each time we have a new

technology we introduce a bit of a

spacing effect and that spacing effect

means social media as an illustration

initially came on and it allowed us to

connect with people from around the

world but nowadays that connection is

actually producing disconnection and so

with initially gave us the opportunity

to do new things with new groups of

people suddenly became become at odds

and in conflict with new groups of

people so the way social media has been

deployed by itself was naive and

effective but once you make it available

for algorithmic Distortion and for

propaganda suddenly it becomes harmful

and actually disruptive to the system as

a whole and so we need to keep that in

the back of our minds because the

lessons of social media on mental health

and on society Wellness will be almost

insignificant compared to the threat and

the risk that AI will pose into the

public conversational sphere so each new

wave of Technology forces us to evaluate

the spaces that we occupy and how we

remain human in those environments and

that's one of the reasons I particularly

appreciate the uh the theme of the

education Forum here around that human

component in AI settings so when you

look at traditional learning literature

there's been a long period of

acknowledging that thinking and learning

doesn't just happen in our brains right

there's a range of theorists that from

embodied cognition to distributed

cognition to some externalization of

Concepts and ideas we're constantly

putting human knowledge into physical

things or objects or concepts in the

world and most established theorists and

philosophers would argue that you are

intelligent as a function of the

networks that you exist within and those

networks traditionally have been tools

and resources we've created such as

books and related artifacts but

increasingly now they're starting to

become systems that are AI compliant or

AI enabled so when I think of artificial

intelligence to me it's not a tool it's

not a resource that we use it is a a

node within our cognitive Network and

that has significant implications

educationally and that's because as a

species we don't exist in these systems

as isolated entities the best way to

describe it is we and not just as humans

but species all of life all of society

coexists and exists fundamentally as a

function of networks the idea of

individual is actually antithetical in

terms of growth opportunities in the

advancement of society all of our

capabilities are a byproduct of how

we're networked and connected so we did

a paper a while ago where we wanted to

understand if we bring AI into these

learning processes such as complex

problem solving what are the effects of

that you know what are the critical

components that are involved assuming

that you agree with me that networks are

the foundational underpinnings and so we

looked at essentially when you have

human and artificial cognition

intersecting in areas of metacogn

such as regulation and learning

management in affect related to things

such as emotion and trust and confidence

and the way that we connect with one

another with a sense of security and

confidence what does that look like or

if you then take and look at the

cognitive practices things like

remembering what's the importance of

memory when AI is at your fingertips or

which parts of memory remain relevant

when AI is at your fingertips because

one of the things that AI does in this

conversation uh is move capability

questions to a new plane it's not that

it makes those things irrelevant it

means that we are related to some of

those Core Concepts differently than we

perhaps have been in the past and

similarly with social practices and

collaboration and engagement and working

together so when you bring AI into this

process one argument that I've been

making to colleagues for years is that

every single thing that we know and

understand understand about human

learning and human knowledge growth

needs to be

re-evaluated with an understanding of AI

as a potential mediating and

transforming agent within that

ecosystem and so we looked at if you

take these two pieces and you bring them

together because that's what we

essentially see happening it's not that

we're saying AI is a tool off to the

side I'm arguing that AI is the first

injection of intelligence

in the human System since our neocortex

came online so it is an alien

intelligence it's not exactly like us

but it does certain things that can make

some stuff easier for all its criticisms

for its hallucinations for its biases AI

is a type of an intelligence that we can

co-thinkers a period of these little

blips of sudden crashes U there's a

research uh report that was put out by

Johnson where he said these systems

where AI is starting to make decisions

they're moving so fast that we are at a

point where there is an inability for

humans to intervene in real time meaning

its machines have taken over large swads

of those kinds of processes and what

it's done for us we can't participate in

real time so the human cognitive

function is to escalate which means we

move to a higher plane because we can't

do the granular performance at the same

level that AI meaningfully can and

that's produced work such as this paper

by rwan and all where they said we need

to start thinking about theories of

learning that don't just integrate human

to machine interactions it's machine to

machine interactions that we need to

think about because there are sads of

decisions in some high-risk areas

including medical and Military where AI

is making decisions often without a

human input layer uh brought in and so

to start thinking about complex problem

solving and the integration of human and

artificial cognition into this kind of a

landscape is critical so paper we did a

few years ago we looked at exactly this

question is what happens when you have

two types of intelligence that maybe

don't quite understand each other but we

know that meaningful integration between

the two is going to be important for

solving all the problems that Humanity

faces from homelessness to inequality to

climate change um how do we begin to

make those two play together and what is

that intersecting space where learning

and sense making and meaning making

happen meaningfully at that level so we

did a paper um in just last year

actually where we looked at the

literature that to date has looked at Ai

and uh its impact on the education

setting specifically what are people

doing with AI in classrooms in a

practical way not in a high flut and

future way that says oh we'll all have a

personal agent and we'll all be happy

and have a robot in our home but in a

practical way what's actually happening

in classrooms and so the number one set

of applications are ones that still

remain prominent which is adaptive

systems and

personalization that's been a holy grail

of education for decades but it says

rather than one student or one teacher

teaching 30 students everyone has a

onetoone relationship like was mentioned

previously this is the idea of blooms 2

Sigma where the inclusion of a tutor can

move a c student to an a student with

the right level of support and guidance

profiling and prediction was an

important one that came up as well a big

part of what universities haven't done

historically is to understand their

students you know what are their skill

sets what are their capabilities outside

of a grade and so it's this idea of how

can we better profile and then if we

profile predict which students will

succeed which students are at risk of

potentially dropping out assessment and

evaluation is another important one and

then interestingly uh tutors were right

at the bottom at least of this cluster

it wasn't a huge area of use this data

would obviously be very different if we

were to do this report again in a year's

time because one of the top adaptations

of the a growth of gener of AI has been

tutoring and adaptive systems of that

type so the benefits then are straight

forward personalized learning positive

influence on the education process um

better administrative activity from a

university level as well helping get

insight into how students are learning

and then also as a way of doing more

effective assessment but that doesn't

mean everything is all delightful

because there's some significant

challenges that are introduced with AI

in this landcape one probably top

Remains the ethical Dynamic how do we

ensure that AI helps not harms people

how do we preserve the Integrity how do

we preserve the uh the security of the

student student in this area of growing

Automation and increased technology a

lot of attention to curriculum

development how do we use AI well to

create courses and then a range of in

infrastructure questions that I'll

address uh once once I get a little

further toward the end the big research

gaps in the literature um are what you

would expect ethics keeps coming up top

of the list because that remains one of

the bigger unspoken challenges in the

University sector as a whole and not

just University across all of society a

lot more questions about methodology uh

this is a conversation I was having with

my wife on this as well recently which

is in education we've typically done we

take a concept and we develop a theory

around it Theory sometimes is the

byproduct of extensive research and then

we use that to guide and shape decisions

going forward but now we're at a

slightly different landscape in that we

can use large sads of data and rapidly

move that forward to try and gain

insight into students and student

performance when we started to look at

this more from an llm side there was

acceleration on a number of fronts but

the same questions remain profiling

prediction feedback remained key

concerns uh in the educational landscape

whether we're looking traditional AI or

emerging

AI we did a paper uh actually I think it

was this year um where one of the

outcomes was we looked at student focus

and student engagement when you bring AI

into the classroom setting and the

interesting thing we found was that

students don't necessarily learn from AI

they instead rely on AI which is an

interesting distinction uh it doesn't

have the same learning capability in all

settings as always it's a function of

pedagogical approach and pedagogical

models one of the big papers though that

I always refer to and this is an

important concept when we talk

methodology is that a lot of the

activity that happens in a classroom is

based on uh or that happens in research

is based on a setting that's

disconnected from reality and an Brown

did a fantastic paper uh you know was it

40 plus years ago where she looked at

this design experiment that the entirety

of a classroom is a learning ecosystem

for learning research rather than these

one-off experimental design settings and

that's exactly the kind of activity that

we try to do in digital spaces now

through the use of data and data

collection which we get from a range of

sources student Information Systems uh

instruments or survey instruments we

deploy Learning Management Systems we

can get a fairly holistic assessment or

lens of what a student is doing and

where she is in her overall learning

process so with that as a backdrop I'll

take the last 10 minutes to talk through

these final sections so if we look at

technology over the last few decades we

can say the open education movement

fundamentally taught us that we can

scale content with minimal cost

additions each new duplication of a web

page or a PDF is really insignificant

compared to the cost of duplicating a

new textbook a second thing that we

learn through open online courses or

mukes in some cases is that we can scale

teaching we can have 100,000 or 500,000

students take a course and it's much

less expensive from a lecture lens if

that's the primary pedagogy in that kind

of a setting an AI is at the early cusp

I believe of teaching us that we can

accelerate and scale interaction so the

connections that we have on sort of a

onetoone basis from a tutoring

perspective the significant Trends I

want to identify here though relate to

where is the current state of AI after

chat GPT and the growth of generative AI

the hype that we had in 2022 and early

last year we're starting to see some

very practical groundings of these

Technologies not least of which is the

prevalence of AI in everything from our

cars Toof software to the platforms we

use growing multimedia and multimodal

and also a lot of attention being paid

to open- Source llms or open source

software a lot of that's driven by meta

interestingly enough and a growing group

of uh organizations notably stuff like

misil and others that are really

promoting open llms there's also

attention being paid to very small llms

which you're going to see more and more

on your Android or on your iPhone

devices uh feed2 Fe three actually just

came out uh at the end of April as well

so we're starting to see them

accelerating similarly AI pairings

meaning AI with traditional Robotics are

starting to come together and I think

most of us in this room will have a an

aid driven robot in our homes within the

next 5 years doing routine related house

tasks a lot of attention now this is

maybe a little more relevant to some of

you who are running technical teams

there's been a significant acceleration

of platform technologies that make AI

development easy if you were to do

something with an llm 16 months ago or

12 months ago you needed a fairly High

technical capability but now in

environments like AWS or vertex you can

quickly run up a series of models test

and deploy uh with a team of one who has

fairly fundamental understandings of the

process um we're also seeing a lot of

I'll skip that one uh more and more

wearables wearable devices uh rayb bands

is an interesting one again meta driving

uh which is the ability to have your

glasses as you're walking see a scene in

front of you you in llama 3 which is

Meadows open llm uh you can ask it what

am I looking at what's this picture and

it will search and provide an answer

back to you uh audibly on your on your

uh glasses as well and then a lot of

tooling things which is a little Beyond

where we are today but tools like dspi

and laying chain that make this process

of managing multiple llm Integrations

much more effective so what are some of

the inte

implications of this well first of all I

think AI will impact roughly everything

that a university does there's no sector

that's not going to be challenged by it

and I do think it represents a systems

level challenge for the sector and I

don't think universities see that and I

don't think many of them are responding

as urgently as they should because if

you look at one of the main things we do

is we generate knowledge and we

communicate knowledge that's our role as

a university and AI plays in all of

those territories I mean here's just a

range of tools that are knowledge

adjacent generating Technologies some of

them have been deprecated you know like

Galactica was briefly put out but then

paused but there's a lot that you can do

with this growing Suite of AI tools that

intersect with human creativity and

human knowledge

capability the system itself as an

Enterprise is already in a process of

unbundling it's no longer a

self-contained system a lot of what we

offer is increasingly being done by a

range of providers and we're going to

start to see exactly the same effect

happening in AI tools if you're a leader

in a university you're going to get a

range of providers and technology

companies coming up to you selling you

AI technologies that do everything from

uh tutoring to content creation to

assessment to student recruiting to

chatbot engagement and so on so it's a

constant influx of new technologies and

new approaches and so the way that we're

going to adopt as a sector is really

going to be one of three a direct

response to a simple problem a platform

based response or as a transformational

angle or transformational opportunity

from a system preserving lens the first

or the second one it's about just taking

Ai and helping it solve a problem like

advising or providing better student

support or the idea of a learner

co-pilot you know Microsoft co-pilot and

others are already making that available

or doing things like adaptive feedback

um that's what universities such as ASU

and what you're seeing with University

of Florida they're taking this kind of

an approach where they're largely going

out and just finding a problem and

solving it with some function of AI if

you want system changing approaches

though you need to start thinking very

differently about your literacies about

developing personal learning graphs and

personal models of a learner that

transcends a course even transcends

their lives computed curriculum not

pre-structured textbooks but curriculum

that's generated based on what a learner

knows and integration of Labor Market

needs into that educational process as

well so we're talking about not doing

education as usual but doing education a

fundamentally different

way so the idea then is this

articulation of an AI first University

and an AI first University is one where

AI is involved in all aspects of the

organization from the infrastructure

through to admissions teaching

assessment curriculum and the research

process and I'll run through six of

those very quickly but you know one is

the infrastructure the pipeline the data

l so any AI deployment is fundamentally

a data challenge secondly it's about

building institutional capability with

AI like do does the organization know

what AI is and how AI performs and what

it does um thirdly there's a range of

questions that relate to the leadership

and policy and governance how does the

University enable AI experimentation how

does it protect University reputation

through effective AI

engagement adaptive and responsive

teaching methods as noted this is

already prevalent in the literature but

how do we begin to use AI in such a way

that it is focused onetoone support for

Learners how do we improve the

personalized experience so that each

individual is met at her needs not just

cognitively but metacognitively

affectively socially and so on so it's a

very nuanced uh response to individual

learner needs and then also the

acceleration

of research through the utilization of

AI doing a simple literature review uh

is now dramatically different through

the inclusion of tools like elicit

consensus or Iris

AI so I think at the end of the the

final several slides one of the critical

challenges I want to emphasize for

anyone that's initiating the AI

conversation is get the data and the

related architecture right more than

almost anything else this is a critical

challenge there are needs of building

capabilities institutionally what I mean

by that as being AI capable as an

organization and having the technical

capacity to train fine-tune models build

your own Bots those are expected but at

the in institution-wide concern of

infrastructure is critical um we just

released a paper for discussion uh

yesterday actually on a global data

Consortium where we tried to lay out how

should Universities at scale begin

collaborating and sharing data so that

you can learn from your your peers

rather than try and do everything on

your own so the university AI response

should be through multi-institution

collaboration and sharing across

operational data analysis data science

planes and then ultimately addressing

and driving impact so it's a critical

outcome uh we have a white paper that's

now out for review uh from the American

Council on education if anyone's

interested on

that um final points we're really

getting at this idea where most of

education has been about teaching people

knowledge related things you know the

epistemology question and I think we're

now moving to the ontology question like

who are we as human beings how do we

develop human beings how do we help

people become more engaged more

productive and more effective members of

society you know any of the kinds of

things that are here like what is it

that we should be teaching how should we

be teaching people and Learners places

of being in the world how should be

driving their capability to navigate

complexity to engage with nonhuman forms

of intelligence and then as a byproduct

of that to be sort of proactive engaged

and anticipating potential harmful

effects of AI as we go

forward thank you

[Applause]

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