GEF Madrid 2024: Conversation: Becoming an AI University / GEF AI Platform

Summary

El discurso destaca cómo la inteligencia artificial (IA) intersecciona con el conocimiento humano y cómo las universidades pueden aprovechar la IA para mejorar la educación y mantenerse relevantes en un mundo en rápida evolución. Se discuten áreas clave como la adaptabilidad en el aprendizaje, la intersección humano-máquina, y la importancia de una infraestructura sólida para el uso de la IA en la enseñanza y la investigación universitaria. La charla enfatiza la necesidad de reevaluar las metodologías de aprendizaje tradicionales y abordar los desafíos éticos y tecnológicos que presenta la IA en el sector educativo.

Takeaways

• 🧠 La charla de George Simons enfatiza la intersección entre la cognición humana y artificial en procesos de conocimiento.
• 🤖 George es cofundador y científico jefe de SNH use human system, una organización que construye recursos para abordar el impacto del sistema en el aprendizaje y la bienestar.
• 🏛 Simons critica la respuesta de la educación superior al uso de la inteligencia artificial (AI), sugiriendo que ha habido un juicio erróneo y una falta de innovación.
• 📚 Se discute la importancia de la AI en el aprendizaje, destacando áreas como la adaptación del sistema, la predicción y el perfilamiento de estudiantes, y la evaluación.
• 🔄 La AI se presenta como un nodo en la red cognitiva, no simplemente como una herramienta, sino como una inteligencia que puede transformar la forma en que se enseña y se aprende.
• 🤝 La integración de la cognición humana y artificial es vista como un proceso de co-creación, no un proceso antagonístico.
• 🔑 Se enfatiza la necesidad de reevaluar todo lo que se conoce sobre el aprendizaje humano y el crecimiento del conocimiento con la AI como agente transformador.
• 🔮 Se mencionan desafíos éticos y metodológicos en el uso de la AI en educación, como la preservación de la integridad y la seguridad de los estudiantes.
• 🛠️ Se sugiere que la AI impactará en todos los aspectos de las universidades, desde la infraestructura hasta la enseñanza, la investigación y la gobernanza.
• 🌐 Se destaca la importancia de la colaboración multi-institucional y el intercambio de datos entre universidades para aprovechar al máximo el potencial de la AI.
• 🌟 Se presenta la idea de una 'Universidad AI-first', donde la AI está involucrada en todos los aspectos de la organización, transformando la educación tradicional.

Q & A

¿Qué es lo que George Simons considera una mala valoración por parte de la educación superior en relación con la inteligencia artificial?

George Simons considera que la educación superior ha subestimado el papel de la IA como mecanismo de cambio e innovación en el sector universitario, lo que ha llevado a una respuesta cansada y desinteresada por parte de las instituciones educativas.

¿Cuáles son los cuatro temas clave que Simons va a discutir en su charla?

Simons abordará la literatura sobre IA y el aprendizaje, la intersección entre la cognición humana y la máquina, las tendencias actuales en IA y su implicación educacional, y finalmente, las seis áreas de prioridad que las universidades deben considerar para involucrarse en la conversación sobre IA.

¿Cómo describe Simons la relación entre la cognición humana y la cognición artificial?

Simons ve la relación entre la cognición humana y la artificial como un proceso de co-creación y no necesariamente como un proceso antagonista, donde la IA actúa como un nodo dentro del sistema cognitivo de la red humana.

¿Qué papel desempeñan los sistemas de adaptación y personalización en la educación según la literatura revisada por Simons?

Los sistemas de adaptación y personalización son una de las principales aplicaciones de la IA en la educación, permitiendo una relación uno a uno entre el estudiante y el proceso de enseñanza, que es una meta buscada desde hace décadas en la educación.

¿Qué desafíos éticos presenta la integración de la IA en el aprendizaje según lo discutido por Simons?

Los desafíos éticos incluyen asegurar que la IA ayude y no dañe a las personas, preservar la integridad y la seguridad del estudiante en un entorno de creciente automatización y tecnología.

¿Cuáles son las áreas de prioridad que las universidades deben considerar para involucrarse en la conversación de IA según Simons?

Las áreas de prioridad incluyen la infraestructura y arquitectura de datos, la capacidad institucional en IA, liderazgo y políticas de gobernanza, métodos de enseñanza adaptables y respuesta, y la aceleración de la investigación a través del uso de IA.

¿Qué cambios significativos en la tecnología han permitido que la IA esté a punto de enseñar a escala?

Los cambios incluyen la escalada de contenido con costos mínimos, la enseñanza a gran escala a través de cursos en línea masivos, y la reciente capacidad de la IA para acelerar e interactuar de manera personalizada y adaptativa.

¿Qué es una 'Universidad centrada en la IA' y cómo se ve la participación de la IA en todos los aspectos de la organización?

Una 'Universidad centrada en la IA' es aquella donde la IA está involucrada en todos los aspectos de la organización, desde la infraestructura hasta los procesos de admisión, enseñanza, evaluación, currículo y la investigación.

¿Qué papel desempeñan las herramientas de IA en la generación y comunicación del conocimiento en las universidades?

Las herramientas de IA intersectan con la creatividad humana y la capacidad de conocimiento, desempeñando un papel en la generación de conocimiento, la comunicación y la transformación de los procesos educativos.

¿Cómo Simons sugiere que las universidades deben abordar la implementación de la IA para maximizar su impacto y solucionar problemas?

Simons sugiere que las universidades deben considerar la implementación de la IA desde tres enfoques: como una respuesta directa a un problema simple, como una plataforma basada de respuesta, o como una oportunidad transformacional para el sistema.

¿Qué es una 'Red de datos global' y cómo podría ayudar a las universidades a colaborar y compartir datos?

Una 'Red de datos global' es una propuesta para que las universidades colaboren y compartan datos a gran escala, lo que les permitiría aprender de sus pares y no tratar de hacer todo por sí solos, mejorando la capacidad de respuesta a la IA en la educación.

Outlines

00:00

🤖 La intersección de la cognición humana y artificial en la educación

El primer párrafo presenta a George Simons, quien explora cómo la cognición humana y la cognición artificial se intersectan en procesos de conocimiento. Simons es cofundador y científico jefe de SNH use human system, una organización dedicada a construir recursos para entender el impacto del sistema en el aprendizaje y el bienestar. Discute la falta de adaptación de la educación superior a la innovación por AI y enfatiza la importancia de utilizar AI para garantizar el éxito de los estudiantes y mantener la relevancia de las universidades en un mundo en rápida evolución. Aborda cuatro áreas temáticas principales, incluyendo la literatura sobre AI y el aprendizaje, la intersección humano-máquina, las tendencias actuales en AI y su implicación educacional, y las prioridades que las universidades deben considerar para involucrarse en la conversación de AI.

05:01

🧠 La cognición distribuida y el impacto de AI en el aprendizaje

Este párrafo se centra en la teoría de la cognición distribuida y cómo el conocimiento humano se extiende más allá del cerebro, utilizando herramientas y recursos. Simons argumenta que la inteligencia artificial no es simplemente una herramienta, sino un nodo en nuestra red cognitiva, lo que tiene implicaciones educativas significativas. Se discuten los efectos de la intersección de cognición humana y artificial en áreas como la metacognición, la regulación del aprendizaje, las emociones y la confianza. La investigación de Simons sugiere que el aprendizaje y el crecimiento del conocimiento humano deben ser reevaluados en la luz de la AI como agente transformador en el ecosistema educativo.

10:02

🔬 Investigación sobre la integración de inteligencias artificial y humana en el aula

El tercer párrafo detalla la investigación de Simons sobre cómo la inteligencia artificial y la humana pueden integrarse en el aprendizaje, con un enfoque en la problemática de la falta de comprensión mutua y la importancia de una integración significativa. Se revisan aplicaciones prácticas de AI en el aula, como sistemas adaptativos y personalización, profiling y predicción, evaluación y tutorías. Se discuten los beneficios, como el aprendizaje personalizado y la mejora en la administración universitaria, así como los desafíos éticos y de infraestructura que surgen con la implementación de AI en el entorno educativo.

15:03

📚 La evolución de la educación abierta y su relación con AI

Simons examina cómo el movimiento de educación abierta ha demostrado la capacidad de escalar contenido y enseñanza a bajo costo, y cómo AI está a punto de transformar la interacción y la personalización en el aprendizaje. Se destaca la importancia de la gestión de datos y la arquitectura relacionada, y se sugiere que la respuesta de las universidades a AI debería ser a través de la colaboración multi-institucional y el intercambio de datos para aprender de los pares y no tratar de hacer todo por sí solos.

20:04

🏛 La universidad AI-first y su impacto en todos los aspectos de la organización

Este párrafo describe la visión de una universidad AI-first, donde la inteligencia artificial está involucrada en todos los aspectos de la organización, desde la infraestructura hasta la admisión, enseñanza, evaluación, currículo y proceso de investigación. Se discuten seis áreas clave, incluyendo la infraestructura de datos, la capacidad institucional con AI, liderazgo y política, métodos de enseñanza adaptables y respuestas, y la aceleración de la investigación a través de AI. Simons enfatiza la importancia de la colaboración y el intercambio de datos entre instituciones para abordar los desafíos de la implementación de AI en la educación.

25:08

🌐 La transformación de la educación y la anticipación de los efectos de AI en la sociedad

El sexto y último párrafo concluye con la idea de que la educación se está moviendo hacia una comprensión más profunda de quiénes somos como seres humanos y cómo desarrollarnos. Simons propone que la educación debe enfocarse en la enseñanza del ser y no solo del saber, y en cómo ayudar a las personas a navegar la complejidad y a interactuar con formas de inteligencia no humana. Se enfatiza la necesidad de ser proactivos y anticipar los efectos potencialmente dañinos de la AI en la sociedad.

Keywords

💡Cognición artificial

La cognición artificial se refiere a la simulación por computadoras de procesos cognitivos humanos, como el aprendizaje, la toma de decisiones y la problemática. En el video, se discute cómo la cognición artificial intersecta con la cognición humana en procesos de conocimiento, destacando su importancia en el aprendizaje y la innovación en el sector universitario.

💡Integración de la cognición humana y artificial

Este concepto describe cómo la cognición humana y la cognición artificial pueden trabajar juntas en un proceso de co-creación, en lugar de verse como procesos antagonistas. En el video, se argumenta que la AI actúa como un nodo dentro del sistema cognitivo de la humanidad, transformando la forma en que entendemos y gestionamos el conocimiento.

💡Aprendizaje adaptativo

El aprendizaje adaptativo es un enfoque educativo que utiliza tecnología para personalizar el contenido y el ritmo de aprendizaje de acuerdo con las necesidades individuales de los estudiantes. En el script, se menciona cómo los sistemas adaptativos y la personalización son aplicaciones prominentes de la AI en el aula.

💡Profiling y predicción

El profiling (perfiles) y la predicción se refieren a la creación de perfiles de estudiantes basados en sus habilidades y a la predicción de su éxito o riesgo de abandono. En el video, se destaca la importancia de estas herramientas para mejorar la comprensión institucional de los estudiantes y para apoyar decisiones educativas.

💡Evaluación y retroalimentación

La evaluación y la retroalimentación son procesos clave en la educación que permiten medir y comentar el progreso de los estudiantes. En el contexto del video, se discute cómo la AI puede mejorar estas prácticas, ofreciendo retroalimentación más rápida y precisa.

💡Etica en la IA

La ética en la inteligencia artificial abarca cuestiones como la integridad, la seguridad y el bienestar de las personas en el uso de la tecnología. El video resalta la necesidad de abordar desafíos éticos en el uso de la AI en el ámbito educativo, asegurando que la tecnología ayude y no daña a las personas.

💡Desarrollo de currículos

El desarrollo de currículos es el proceso de crear y actualizar programas de estudio. En el video, se sugiere que la IA puede ser una herramienta valiosa para el desarrollo de currículos, permitiendo la creación de contenido educativo más dinámico y alineado con las necesidades del mercado laboral.

💡Infraestructura tecnológica

La infraestructura tecnológica se refiere a los sistemas y recursos tecnológicos necesarios para soportar la operación de una organización. El video enfatiza la importancia de una sólida infraestructura para el éxito de la implementación de la AI en las universidades, incluyendo la gestión de datos y la capacidad técnica.

💡AI First University

Una 'AI First University' es una institución educativa que integra la IA en todos los aspectos de su organización, desde la infraestructura hasta la enseñanza, la evaluación y el proceso de investigación. El concepto se introduce en el video como un modelo para universidades que buscan aprovechar al máximo el potencial transformador de la IA.

💡Colaboración multi-institucional

La colaboración multi-institucional implica la cooperación entre diferentes universidades o instituciones para compartir datos y aprendizajes. En el video, se sugiere que esta colaboración es esencial para el éxito a gran escala de la implementación de la IA en el sector educativo.

Highlights

George Simons es co-fundador, jefe científico y arquitecto de SNH use human system, una organización que construye recursos para responder al impacto del sistema en el aprendizaje y el bienestar.

Simons critica la respuesta de la educación superior al uso de la IA, sugiriendo que ha habido un juicio erróneo y una falta de innovación en el sector universitario.

Se discute la importancia de la IA en el proceso de aprendizaje, destacando la necesidad de adaptarse y utilizar la IA para garantizar el éxito de los estudiantes y la relevancia de las universidades.

Simons enfatiza la intersección de la cognición humana y artificial, argumentando que es un proceso de co-creación y no un proceso antagonista.

Se presenta una visión de la IA como un nodo en la red cognitiva, en lugar de una herramienta o recurso, lo que tiene implicaciones significativas para la educación.

Se explora la idea de que la IA puede transformar la forma en que se gestiona el aprendizaje y se conecta con uno mismo y con otros, en el contexto de la regulación emocional y la confianza.

Simons argumenta que la integración de la IA en el aprendizaje requiere una reevaluación de todo lo que se conoce sobre el crecimiento del conocimiento humano.

Se discuten los efectos de la IA en el procesamiento de problemas complejos y cómo la inteligencia artificial y humana pueden colaborar para resolverlos.

Se destaca la importancia de la adaptabilidad y la personalización en el aprendizaje con IA, con el uso de sistemas adaptativos y de personalización como la búsqueda de la educación personalizada.

Simons señala los desafíos éticos y de seguridad que presenta la IA en el entorno educativo, y la necesidad de abordar estos problemas para proteger a los estudiantes.

Se mencionan las brechas de investigación en la literatura sobre IA y educación, con un enfoque en ética y metodología para mejorar la comprensión y el uso de la IA en el aula.

Se destaca la dependencia de los estudiantes en la IA en lugar de aprender de ella, lo que sugiere una distinción importante en la interacción humano-máquina.

Simons habla sobre las tendencias actuales en la IA, incluyendo el uso de tecnologías generativas, el aumento de la atención a las LLMs de código abierto y la integración de IA con robots.

Se discuten las implicaciones de la IA para la universidad, sugiriendo que la IA impactará todos los aspectos de la organización universitaria y representa un desafío a nivel de sistema.

Se presenta la idea de una universidad centrada en la IA, donde la IA está involucrada en todos los aspectos de la organización, desde la infraestructura hasta la investigación.

Simons enfatiza la importancia de la colaboración multi-institucional y el intercambio de datos entre universidades para aprender de los pares y avanzar en el uso de la IA.

Se cuestiona la tradición de enseñar conocimientos en la educación y se sugiere un enfoque en la ontología, enfocándose en el desarrollo humano y la capacidad de los individuos para navegar la complejidad.

Transcripts

thing he is George cens um I think

they're 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

state welcome him George

Simons 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 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 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 L 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 writ

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

what 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 can compare 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 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 and the advancement of

society all of our capabilities are a

byproduct of how we're Network 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 metacognition 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 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

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 on line

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 uh 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

it's 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 granual level

performance at the same level that AI

meaningfully can and that's produced

work such as 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 machino 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 a 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 one

toone 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

straightforward 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 landscape 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 in this area of growing

Automation and increased technology a

lot of attention to curriculum devel

velopment 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 was having with

uh 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 swads 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 a

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 that happens in research is

based on a setting that's disconnected

from reality and an Brown did a

fantastic IC 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

oneoff 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 a 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 to software to the platforms we use

growing multimedia and multimodal and

also a lot of atten 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 fe2 fe3 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 scen 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 Lang chain

that make this process of managing

multiple llm Integrations much more

effective so what are some of the inte

inte implications of this well first of

all I think AI will impact roughly

everything that 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 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

leg so any AI employment 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 uh 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 is 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 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 on ology

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 we be

driving their capability to navigate

complexity to engage with non-human

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

[Applause]

you thank you very much George wonderful

intervention yes for the next guest

thank you thank you very much again um

now we are moving forward H and I'm

going to switch switch again into

Spanish

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[Applause]