Panel: The Importance of AI-Literacy for AI in Education

Summary

The panel discussion delves into the critical importance of AI literacy in education, emphasizing the need to understand AI's role in daily life and its impact on learning. Experts from diverse backgrounds stress the significance of teaching AI responsibly, including its ethical considerations, and the necessity for students to be AI literate. They explore ways to integrate AI literacy into curricula, the importance of family engagement, and the challenges of keeping pace with rapidly evolving technology.

Takeaways

• 📚 The panel discussion emphasized the importance of AI literacy in education and the need to understand AI's role in learning and everyday life.
• 🌟 Laura Allen highlighted the comparison between traditional literacy and AI literacy, stressing the need to understand AI's terminology, concepts, and the implications of data usage.
• 👨‍🏫 Stephanie Adragna discussed the significance of family involvement in fostering AI literacy, suggesting that joint engagement can facilitate a better understanding of AI technologies.
• 🔍 Dru Long emphasized the unique aspects of AI, such as its active decision-making processes, which differentiate it from other technologies and necessitate a certain level of AI literacy.
• 🤖 Tyron Young, from a practitioner's perspective, focused on the ethical considerations of AI and the importance of having a checklist for evaluating new AI technologies.
• 👨‍🎓 The conversation underscored the responsibility educators have in teaching AI literacy and the need to integrate it with critical thinking and ethical considerations.
• 👥 There was a consensus on the importance of interdisciplinary approaches to AI literacy, combining technical knowledge with social, ethical, and creative perspectives.
• 👶 The panelists agreed that AI literacy should start from an early age, with discussions around AI ethics and understanding integrated into curricula at all levels.
• 🏫 There was a call for more transparency from AI developers and for educational institutions to provide resources that help students understand the implications of AI technologies.
• 💡 The discussion highlighted the need for hands-on, embodied learning experiences with AI to help demystify the technology and make it more accessible for learners.
• 🔗 The panelists provided various resources and publications for further exploration of AI literacy, emphasizing continuous learning and adaptation to the rapidly evolving field of AI.

Q & A

What is the main focus of the panel discussion in the provided transcript?

The main focus of the panel discussion is AI literacy, specifically how AI is implemented in education and the importance of understanding AI as educators, researchers, and students.

What is the role of Laura Allen in the context of this panel?

Laura Allen is an assistant professor at the University of Minnesota, and she contributes to the panel with her research background at the intersection of AI literacy, focusing on text comprehension, text production, and developing adaptive strategy instruction and feedback for students.

What does Stephanie Adragna bring to the panel discussion?

Stephanie Adragna, a PhD candidate at the University of Washington, brings her research on AI literacy education to the panel, emphasizing the importance of guiding young people's interaction with AI and ensuring they are not only consumers but also creators of technology.

What is Drury Long's perspective on AI literacy?

Drury Long, an assistant professor at Northwestern, discusses the importance of AI literacy as a set of competencies that enable individuals to critically evaluate AI technologies, communicate and collaborate effectively with AI, and use AI as a tool in various settings.

What is Tyron Young's view on AI literacy in the context of education?

Tyron Young, a senior manager for research and development at Digital Promise, emphasizes the need for a checklist to evaluate the ethical considerations and potential risks and rewards of AI technologies, especially in educational settings.

How does the panel view the importance of AI literacy in everyday life?

The panel views AI literacy as crucial in everyday life due to AI's active role in decision-making processes, necessitating a basic understanding of how AI works to make informed decisions about its use and the data shared with AI systems.

What is the significance of the panelists' diverse backgrounds in the discussion?

The diverse backgrounds of the panelists, including academia and industry, provide a multifaceted perspective on AI literacy, covering aspects from theoretical understanding to practical implementation and ethical considerations in various contexts.

What is the panel's stance on the integration of AI in educational technologies?

The panel acknowledges the potential benefits of AI in educational technologies but stresses the importance of transparency, understanding the implications of AI use, and maintaining spaces for socialization and collaboration among students.

How does the panel address the ethical considerations of AI in the classroom?

The panel suggests that ethics should not be an afterthought but an integral part of AI literacy, starting with questions about the implications and potential consequences of using AI in educational settings.

What resources or activities does the panel recommend to promote AI literacy?

The panel recommends various resources and activities, such as the book 'Critically Conscious Computing', which provides teaching units and activities to foster AI literacy, including critical thinking about new technologies.

What is the panel's opinion on the need for AI literacy in university computer science programs?

The panel believes that AI literacy, including understanding machine learning and its ethical implications, should be a core part of computer science education, not just an elective or add-on course.

Outlines

00:00

📚 Introduction to AI Literacy Panel

The panel, consisting of experts from diverse backgrounds, is introduced by the moderator to discuss the importance of AI literacy in education. The moderator emphasizes the ubiquity of AI and the need for researchers, teachers, and students to understand its implementation in learning. The discussion aims to explore the responsibility of making students AI literate, including understanding AI's role in teaching and how their data is utilized. The panelists are given flexibility to share their perspectives, and the audience is encouraged to submit questions during the talks.

05:00

🌐 AI Literacy as a Multi-Level Understanding

Laura Allen, an assistant professor at the University of Minnesota, draws parallels between traditional literacy and AI literacy. She discusses the necessity of understanding terminology, concepts, and the multi-level components of AI, similar to language comprehension. Laura emphasizes the importance of not just knowing the individual parts of AI but also interpreting them within one's own experiences and beliefs. Her talk underlines the significance of critical understanding and the interactive nature of language and AI systems.

10:02

👪 Engaging Families in AI Literacy Co-Design

Stephanie Adraga, a PhD candidate at the University of Washington, presents her research on involving families in co-designing AI literacy activities. She discusses the transition from digital natives to an AI generation and the importance of guiding interactions with AI. Stephanie's work focuses on family joint engagement, which facilitates access to AI technologies' language and power, fostering critical engagement for designing social futures. Her studies explore various AI literacy activities that families can engage in together, promoting creativity and self-expression with technology.

15:02

🔍 Analyzing AI Education Resources and Family Engagement

Stephanie continues her discussion by sharing her findings on the analysis of AI education resources and the roles parents play in AI literacy activities. She highlights the challenges in finding instructions for using AI resources and the sparse coverage of AI concepts, often ignoring the social impact. Stephanie's work includes developing platforms like CopyMates, which allows children and parents to train custom AI models, fostering skepticism and understanding of AI's limitations. Her research underscores the need for interdisciplinary approaches and the inclusion of social impact topics in AI literacy curriculums.

20:03

🤖 The Importance of AI Literacy in Everyday Decision Making

Drury Long, an assistant professor at Northwestern, discusses the unique aspects of AI compared to other technologies due to its active role in decision-making processes. He emphasizes the importance of understanding AI at a high level to make critical decisions about its use, especially given the black box nature of AI decision-making. Drury advocates for public AI literacy to empower individuals to engage with AI systems knowledgeably and critically in various aspects of life, including education.

25:04

🏫 Integrating AI Literacy in Educational and Informal Learning Spaces

Drury Long elaborates on his definition of AI literacy as a set of competencies that enable individuals to critically evaluate AI technologies, communicate effectively with AI, and use AI as a tool. He shares his work on designing learning interventions to foster AI literacy, particularly focusing on practical understanding rather than programming skills. Drury also discusses the importance of considering AI literacy in relation to other literacies like data literacy and computational literacy.

30:06

👨‍🏫 Perspective of an Educator on AI and Ethics

Tyron Young, a senior manager for research and development, brings an educator's perspective to the discussion. He reflects on the evolution of digital literacy and its intertwining with AI literacy, emphasizing the cultural context of students and the importance of ethical considerations. Tyron discusses the need for a checklist to evaluate the risks and rewards of engaging with AI technologies, especially in educational settings where students must navigate the rapidly advancing technology landscape.

35:06

📘 Final Thoughts on AI Literacy and Moving Forward

The moderator wraps up the panel discussion by thanking the participants for their insights and opening the floor for audience questions. The panelists have shared diverse perspectives on AI literacy, emphasizing the need for understanding, critical evaluation, and ethical considerations in engaging with AI technologies. The conversation highlights the importance of AI literacy in educational and informal learning spaces, as well as the responsibility of educators and developers in fostering this literacy.

Keywords

💡AI Literacy

AI Literacy refers to the ability to understand, evaluate, and engage with artificial intelligence technologies. In the video, it is a central theme, with panelists discussing the importance of AI literacy in education and everyday life. For instance, Laura Allen emphasizes the need for students to be literate in AI to understand how it impacts their lives and to critically evaluate AI technologies.

💡Adaptive Learning

Adaptive learning is a method of education where the learning process adapts to the needs of the student. In the context of the video, it is mentioned as a way AI can improve learning experiences by providing personalized education. However, there is also a discussion on the responsibility to teach students about AI, ensuring they are not just passive recipients of adaptive technologies.

💡Critical Thinking

Critical thinking is the ability to analyze and evaluate information objectively. The panelists in the video stress the importance of fostering critical thinking skills in relation to AI. For example, Stephanie Adragna discusses the need for young people to be skeptical of AI technologies and to understand their underlying mechanisms to make informed decisions.

💡Data Literacy

Data literacy is the ability to read, work with, analyze, and argue with data. In the video, it is closely tied to AI literacy, as understanding AI often involves comprehending how data is used and interpreted by algorithms. Drury's research highlights the importance of recognizing that computers learn from data, including personal data.

💡Machine Learning

Machine learning is a subset of AI that allows computers to learn and improve from experience without being explicitly programmed. The video discusses machine learning in the context of education, where AI systems can adapt to students' learning needs. Drury's example of creating data sets to teach an AI to recognize birds illustrates this concept.

💡Algorithmic Bias

Algorithmic bias refers to the prejudice in algorithms that can arise from the data they are trained on. The panelists discuss the importance of understanding and addressing these biases. For instance, Stephanie Adragna mentions the need to protect families from harmful biases in AI technologies.

💡Ethics

Ethics in the context of AI involves considering the moral implications of AI technologies and their applications. Tyron Young discusses the ethical considerations of AI in the classroom and the need for transparency from companies providing AI-based educational tools. Ethics is a recurring theme, emphasizing the responsibility to question and critique AI applications.

💡Human-AI Interaction

Human-AI interaction refers to how humans engage with AI systems. Panelist Drury's work focuses on designing AI that can better understand people and interact in social and creative environments. The video emphasizes the need for AI literacy to improve these interactions and ensure AI serves human needs effectively.

💡Educational Technology

Educational technology encompasses the use of technology to facilitate learning and teaching. The video discusses the integration of AI into educational technology, with a focus on the potential benefits and the need for transparency and ethical considerations. Tyron Young raises concerns about automated scoring in AI-based educational tools and the need for understanding these systems.

💡Interdisciplinary Approach

An interdisciplinary approach combines knowledge and methods from multiple disciplines to address a topic or problem. In the video, Stephanie Adragna suggests taking an interdisciplinary approach to AI literacy, focusing on social impact topics relevant to both kids and parents, such as climate change, and integrating AI literacy into various subjects.

💡Generative AI

Generative AI refers to AI systems that can create new content, such as images, text, or music. The video discusses the rapid development of generative AI and its implications for AI literacy. Stephanie Adragna mentions the potential of generative AI to stimulate creativity and the need to understand the technology behind it.

Highlights

The panelists discussed the importance of AI literacy in education and its implementation, emphasizing the need for students, researchers, and teachers to understand AI.

Laura Allen compared AI literacy to traditional literacy, highlighting the need for understanding terminology, concepts, and the ability to interpret AI within one's own experiences.

Stephanie Adragna emphasized the role of families in AI literacy, suggesting that joint family engagement can foster critical conversations and understanding of AI technologies.

Drury Long discussed the unique aspects of AI literacy, noting AI's active decision-making processes and the importance of understanding its workings to make informed decisions.

Tyron Young shared insights from an educator's perspective, stressing the importance of ethical considerations and the need for transparency in AI applications used by students.

The panel explored the idea of AI literacy as an interdisciplinary field, intersecting with data literacy, computational literacy, and the ability to critically evaluate AI technologies.

Adragna shared findings from her research, indicating that children can become more skeptical and aware of AI's limitations through hands-on activities involving AI model training.

Long introduced the concept of 'critically conscious computing', providing a framework for evaluating new AI technologies with a focus on ethics and accountability.

The panelists agreed on the necessity of fostering AI literacy that encompasses understanding AI's social impact, ethical considerations, and the ability to critically engage with AI systems.

Young raised concerns about the rapid advancement of AI and the lack of clear guidelines for evaluating new AI applications, suggesting the need for a checklist to assess AI technologies.

The discussion highlighted the importance of including AI literacy in the curriculum, not as an afterthought but as a core component, starting with ethical considerations.

Adragna and Long both shared resources and activities designed to promote AI literacy in both educational and familial contexts, emphasizing hands-on and interactive learning experiences.

The panelists stressed the need for transparency from AI developers, particularly regarding data collection, algorithmic processes, and the potential implications of AI technologies.

Long and Adragna discussed the importance of machine learning literacy, suggesting that it should be a required part of computer science education to ensure a well-rounded understanding of AI.

The conversation concluded with a call to action for the development of AI literacy programs that are interdisciplinary, ethically grounded, and focused on empowering learners to critically engage with AI.

Transcripts

so thanks everybody for joining today

super excited to have a really great

group of panelists here

um we are going to be talking a little

bit about uh AI literacy uh we have uh

four great panelists from all different

backgrounds we have Laura Allen who's an

assistant professor at the University of

Minnesota

um Stephanie adruga a PhD candidate at

the University of Washington uh Drury

long who's assistant professor at

Northwestern and Tyron young who is a

senior manager for research and

development at digital promise so super

excited to hear from everybody I'll just

sort of frame the discussion a little

bit uh before we get started and kind of

let everyone know how it's going to work

um this panel sort of came out of uh

increasing uh need for thinking about

how AI is implemented in education and

AI sort of as an ubiquitous

concept all around us

um a lot you know this conference is

themed uh empowering learning Learners

in the age of AI so we also kind of need

to think about how we're teaching AI as

well and hey how AI literate we are as

as researchers teachers and students

um and so you know a lot of the talks

we've seen so far in the conference and

the panels and so forth have really

focused on uh the use of AI to improve

um improve learning like so how can we

build Ai and Technologies you know to

make it more adaptive for students and

so forth

um to prove collaboration

um sorry

um my dog needs to be part of the panel

as well

um

but you know I think one of the equally

important questions to ask is what is

our responsibility

um to sort of make you know our students

literate in AI as we you know try to use

it to improve learning

um so on the one hand

this relates to how we we teach students

about AI

um and and how you know it's ubiquity in

their everyday lives but on other it

really relates to kind of empowering

them to understand how AI itself may be

used to teach them and how their data is

being used

um so uh with that I want to invite all

the panelists so everybody's going to

have about seven to nine minutes but

pretty flexible to kind of give their

take on AI we've left it super open so

I'm hoping to get some variety of of

perspectives here and then uh we'll open

it up for questions at the end I will

encourage everybody who all the

attendees to go ahead and type your

questions in the um there's a q a

section here and it would be great if

you typed your questions in as the

speakers are talking so we can go ahead

and kind of start thinking about them

before we sort of reach that q a part uh

after everyone's done talking so with

that I'll we're just going to go in

alphabetical order so I'll hand it over

to Laura to go ahead and speak first and

then

um we'll go Laura Stephanie Ed uh durian

then Tyron so Laura whenever you're

ready

all right

um let me just share my screen

can you uh oh no

my mouse has disappeared

someone can y'all just see the panelist

list yeah probably we can see a black

background

it would give you popped up so there we

go oh yeah can you we're good now

alrighty

um so hi everyone

um thank you Caitlin for inviting me to

be on this panel

um so for those of you who don't know me

probably everyone uh I um am an

assistant professor at University of

Minnesota and most of my research has

been sort of at this weird intersection

between AI literacy

um where I both study literacy so I've

spent a lot of time studying text

comprehension and text production how

people

um we can best support students in

learning how to read and write

um and then I've also worked on

developing technologies that can produce

provide adaptive strategy instruction

and feedback for students

um where I haven't done as much work is

actually on AI literacy itself um so

um hopefully my take on this is at least

a little bit useful

um so when I was thinking about AI

literacy I thought about again my

backgrounds in literacy so I thought

about you know what we think about in

terms of regular literacy or you know

the traditional view of literacy

um there's if you if you look up the

definition of literacy online you will

find many of them and they're they're

quite different

um but generally right when we think

about literacy we think about some sort

of ability to read write speak listen so

using language

in a way that allows us to communicate

with others and to make sense of the

world right and so when I was thinking I

was thinking about how we can apply that

same sort of perspective and what we

know from teaching students in that

realm of literacy to this sort of new

way of thinking about literacy

so

um so when we think about literacy from

a text perspective we think about the

fact that we have some sort of language

that is generally you know connected in

some sort of way right so we have

individual words that make up the text

and then they're combined you know in

special ways to make sentences and then

we can sort of connect those

um in broader ways to make some sort of

document and if we want students to

really understand

um you know sort of how to make meaning

out of text then they really need to be

able to have knowledge at multiple

different levels so first they need to

understand those individual words right

so all those different words and what

they mean they don't need to know every

single one of them but they generally

need to know most of the words in a text

they're reading they need to know what

they mean and what they might be

associated with in the world

they also need to know the rules for how

those words can be combined right so we

have a lot of different you know words

um but there's a very finite set of

rules that we sort of operate under in

order to combine those words so not

every single word can sort of just be

combined with itself right we need to

know the ways in which they move

together and interact with each other

and then once we have these sentences

right we also need to be able to combine

them with our knowledge of the world in

order to make sense of that language

right so sort of a Hallmark property of

language is that it's sort of

it's all symbolic and so it doesn't

really mean anything in the world

without us interacting with those

symbols in the world right so there's no

reason that the word chair means chair

it just does and we sort of associate it

with certain things to write our

interactions with the world in order to

make sense

so if we try to think about those sort

of same principles and we're thinking

about AI literacy

um I think at least we can draw some

insights from that

so it similarly is going to require us

to sort of break down what these what

this form of literacy means into

multiple different components right and

and this mapping is not perfect so bear

with me but but generally we need to be

able to First think about something like

the words right in the in the analogy

that I'm giving so we need to know what

the terminologies are what are the

concepts right what are the things that

are representing whatever we're using so

they need to know

um what types of data is available to

them and how they can sort of use that

data what are the different ways that

data can be stored or could be modeled

they need to know how that data can then

be used in some sort of model in order

to do something functional right so what

are the ways in which I can take data

from language data from you know click

stream data data from facial recognition

and sort of combine those in different

sorts of ways

um to develop some sort of model and

then finally students need to not only

understand and all of those different

sorts of pieces which I think is a big

Focus right in digital literacy and AI

literacy and they need to know those

things but they also need to know how to

interpret those things within the

context of their own lived experiences

and their own beliefs and their own

knowledge right and I think this piece

is really really critical and I think

something that I is really important

that doesn't get missed

um so similar to research and literacy a

lot for a long time people sort of

focused on students knowing vocabulary

and knowing grammatical rules and

probably you were a kid in school at

some point and you were taught a lot of

vocabulary and a lot of grammar rules

and we don't we do that often while

sacrificing teaching students how to

construct meaning right and how to

interpret things through their own lens

and through their own knowledge and I

think that we need to sort of foster

that in a similar way within AI literacy

um so just to wrap up

um I think that sort of the big ways

that we should support students to

become more literate is both teaching

them the meaning of those different sort

of component pieces so where the data is

you know what types of data they have

where it's getting collected what type

of models there are and what are the

rules for how they work and then also

how to integrate that with their own

understanding

um and so oh this is coming out weird

sorry uh and so like when we think about

language we think about the fact that I

am a user of language and I'm producing

language right now right but critical to

my the success of me producing language

is that you understand what I'm saying

and that we have some sort of shared

understanding right so if my goal is

misaligned or you don't interpret my

goal in the same way then I have failed

as a speaker right and so I think we

should think about literacy from this

interactive way between in students and

AI systems so that they both are aware

of how they work but also aware of how

different sorts of goals whether those

be explicitly embedded within the system

are sort of more implicit in terms of

biases and the data and things like that

can all sort of govern how those things

work and how students can leverage those

in order to make decisions and to

function in society so

I will end there

thanks Laura

um super interesting uh I think Connie

mentioned that your screen might have

been black at the end but I don't think

we missed anything uh on your last slide

I think it's okay we got the got the uh

we understood your language

um so with that I'll go ahead and turn

it over to uh Steph who who do you have

slides to share okay

cool

can you hear me

yes hi uh let me try to get this on the

big screen

hopefully you can also see my slides now

great uh I'm Steph uh I'm a PhD

candidate at the University of

Washington in Seattle I've been doing

research in the space of AI literacy

education since 2016 until now and I

started doing this kind of research

because I noticed that uh young people

are now growing up with AI in 2016

things like Syria Alexa voice assistants

were just starting to enter kids uh

homes and kids lives we've seen like a

rapid expansion of these Technologies

and the ways in which they're being

exposed to young people ever since so I

realized that we're going from the

digital natives or you know a digital

generation to an AI generation where

young people will probably learn how to

interact with a voice assistant before

they even read to learn how to read and

write and via voice assistant they can

search the web they can have access to

any type of information so

for me it was important to to figure out

like how do we guide this interaction

with AI how do we make sure that

um young people and families are not

only consumers but also creators of

technologies that are being developed in

the space and applications

um and more importantly to make sure

that families are being protected by

from our great mean bias or all sorts of

other issues that are being introduced

with these Technologies becoming

commonplace so what we know until now is

that in general like when parents are

involved in kids learning and exposure

to technology it has beneficial impact

um that family joined media engagement

was also something that was studied over

the years when they were looking at how

kids and parents consume like TV

together or interact with mobile apps um

it has beneficial impacts in terms of

developing and fostering critical

conversations in the family

um and in our in my own work and also in

the work of from other people here in

the panel from Durie and others we've

seen that there are many different ways

in which we could introduce young people

to to Ai literacies and all the

competencies that are part of that so

what I wanted to learn was how could I

actually involve not only kids but also

their families in co-design co-designing

AI literacy activities

um how do kids and parents learn

together about Ai and what are all the

types of Designing that we could do to

support family literacy and you heard me

use this term a lot so I wanted to also

kind of find like how I'm thinking about

it my most also disappears so I cannot

uh minimize the video but um for me I

actually really include the ability to

read work with analyze and author with

AI and they Foster a critical

understanding of this technology so the

critical part is quite important and

what I found throughout the studies I've

been doing

is that family joint engagement in AI

literacy is really facilitates access to

an evolving language of AI Technologies

it also facilitates access to the power

in the community this technology can

bring

um that family joint engagement in AI

literacy Fosters critical engagement

necessary to design social Futures that

are imagining meaningful uses of AI in

the home and we also discovered that

kids and parents like to learn together

with AI how to engage in Creative coding

and imagine like how to

well basically we imagine Computing

Norms at home and enable much more

self-expression family expression with

these Technologies

um so I've done several studies that

were formative like analyzing the

existing Ai curricula and see what works

and what doesn't analyzing how AI coding

impacts and understanding uh analyzing

what are all sorts of different family

co-design activities for AI literacy

that

um both parents and kids like doing

engage in jointly and last but not least

a longitudinal longitudinal study

looking at how families develop AI

literacies over time

and

um the tldr I'm not going to have the

time to go into detail uh for all of

these studies I'll share a link with all

the Publications so you if you're

interested you can learn more later

um right now like there are

um uh growing Corpus of resources for AI

education and literacy

um we found together with my co-authors

um a corpus of 51 resources that are

still active and available online and we

analyze them these include like

activities demos curriculum

um lots of different types of resources

and we analyze them

um to really see like to which extent

like what AI Concepts they

um Talk they

um demonstrate uh what big ideas for a

literacy they cover what age groups are

suitable for and so so on and so forth

um and what we found actually is that

there's really the instructions for how

to use this uh both for families or in a

classroom or for educators or informal

learning spaces are quite uh hard to

find a news that there's a sparse

coverage of AI concept and primarily

ignoring social impact that AI has that

very often for some of these like demos

and tools and activities like their

prohibitive costs or Hardware

requirements and there's

um General lack of consideration of

prior knowledge like what do people

know when they're coming to these

activities to this curriculum to these

demos

um how could we could we support them to

to engage in these activities

um and also limited opportunities for

self-assessment and reflection basically

it's the wild west uh very early stages

there's a lot to do and there's a lot to

opportunities for growth uh in in the

space of AI education resources that are

available online and recommended for

teachers for example

uh in in the second study I mentioned

um where I wanted to try to address some

of these issues that I found

um I use the platform that I built

called copymates which is free it's open

source it expands scratch to actually

allow kids and their parents to train

their custom models with images with

text with sounds and once they have

their custom models they can build

scratch like like games

and what we found in this study we had

57 children ages 8 to 12 from public

private schools after school programs

and we got them to engage in three

different

um activities where they could do a text

training activity an image training

activity and a smart home activity and

after they engaged in this activities

what we found is that they became more

skeptical of the way they were

describing the intelligence of smart

assistant they're already having the

home like Alexa or Google home or smart

robots so it really allowed them to not

only develop ways to test like what

these Technologies can do but also have

uh I realized that this intelligence is

not magical it doesn't come you know

like from The Ether that is actually

done by people who record the data who

train an algorithm

um that there's like

people Behind These Technologies and

that there are limitations that come

with this technology so it allowed them

to become much more skeptical of these

Technologies

um so like I mentioned that yeah

oh yeah no I just wanted to make sure

your slides weren't progressing I just

wanted maybe if you unshare and re-share

that would that would help sorry to

interrupt

no worries uh let me try again

are they progressing now okay we're good

yeah okay

um yeah so the platform is available

online feel free to to try it uh it's

translated in lots of different

languages and it's being used worldwide

um uh so moving forward

um

I uh I mentioned that we did this

longitudinal studies with families at

home especially during covet because it

was very hard to do this in person

um what was interesting there for for

the long-term study with families we

actually developed lots of different

activities around image classification

machine learning

um games with voice assistance design

and analyze AI these sessions and what

we found here uh was that parents

actually played lots of different roles

when engaging in AI literacy activities

learning activities with their kids

the the range the roles range from being

a cheerleader to being a mediator a

mentor student a teacher an observer

um and the activities that supported the

most The Joint roles where the parents

would Tinker together with their kids

and would collaborate with their kids

where activities that involved like a

physical component like a Hands-On

component a crafty component so

for example like the teachable machine

activities worked very well uh and um

the game for like testing the voice

assistance the design activity on paper

worked very well so the takeaway here

was really when we're designing or using

existing AI resources for family AI

literacies like thinking like how could

we support both kids and parents to have

discussions around these activities to

build on each other's ideas to be able

to test and Tinker

uh with these activities and not think

that because it's AI everything has to

be digital that actually providing

Hands-On and crafty ways of engaging

with these Concepts goes a long way

um so that was like a big takeaway from

from this from the study

um and kind of like moving forward you

know it was important to see how

families of different backgrounds we had

families that spoke several languages

different ethnicities different uh parts

of the world uh different family

configuration

uh we're such a diverse range of

families from 10 different states in

North America

um what we found is that they engage in

multiple forms of AI literacy which is

why we we use this theory in

um multiliteracy uh lens

um and the way we situate like Ai

literacies and not only Literacy for for

a diverse set of families is like the

ability to engage in the following

practices being able to engage in

multi-model and in body situated

practices in their home being able to

learn about different AI Concepts being

able to

engage in a critical framing of AI and

being able to design future meaningful

uses so this this approach Builds on the

theory of multiple literacies uh from

the New London group and for each of

these facets we have different

activities that are also available

online that you could try out if you so

want to

um so to end I wanted to to share some

of my current ideas for like what would

be cool to do in the space of learning

about AI uh developing AI Literacy for

families and I think it's very important

to take an interdisciplinary

approach for the curriculum we're

creating in this space to focus much

more on social impact

um topics that are relevant for both for

kids and parents

um we know we all live through covet

like what can we learn about that Nai at

the same time

climate change is big on on youth's

Minds how could we use AI to do

something about that and of course as a

form of personal expression creativity

um especially when we've seen everything

that is happening in the space of the

diffusion model stability AI Delhi like

it's like creativity on steroids it's

like really really uh it's been

exploding so creating a rich collection

of projects and lessons that stimulate

families in various ways and allow them

to engage and have lots of interactive

um playgrounds where like uh both kids

and parents could learn by doing so

I took here an example from of the

spaces interactive spaces that are on

hugging face which is the largest I

think we might need you to

I think we might need it on share and

re-share one last time okay

that is annoying uh let me try again

can you see it now

yeah yeah

so like looking at the spaces from

hugging face which is the largest open

source Community for natural language

processing machine learning uh where

people build like all of these

interactive spaces to to play with AI

and thinking like what would that look

like if we have something similar that

is customizable demos that allows for

easy integration in curriculum for youth

curriculum for families

um and then also thinking about what is

this generative AI that is exploding

right now what does that mean for for

the future of AI literacy like can we

support automatic asset creation and

meet kids where they're at right like if

they spend a lot of time on Minecraft

like what does it look like to actually

automate like assets and worlds in

Minecraft and allowing them to do that

and customize that

um how do we allow them to learn from

their own data right like I showed you

how uncognits they can train and test

their own models like

um I I noticed that kids and parents

have much more intuition about Ai and ml

Concepts when they work with their own

data

then like how do we integrate some of

these AI libraries plugin programming in

platforms that are widely successful

with kids like Roblox

um again hugging face demos

um and yeah chats are big like maybe we

could use a smart assistant to get to

teach kids like how to train their own

assistant

um and there are many many things that

we could do in this space so

uh I'm gonna stop here because I'm I

think I'm a time and we also have the

technical challenges but I'm looking

forward to the discussion later and

thank you for your attention

thanks Steph uh really interesting stuff

thanks for sharing um during you ready

you're up next

yes I will share my screen just a sec

and uh I'm not sure about this the

slides issue will continue I'll try to

um either like wave my hand or put it in

the chat if they get seem to get stuck

again

yeah just let me know can y'all see my

screen

yep

great

um well I've really enjoyed the talk so

far thanks so much for having me today

um I obviously didn't come up with the

world's most creative title for this

talk but

um just to give a little context about

who I am

um I am an assistant professor in the

department of communication studies at

Northwestern

um and I'm a human centered AI

researcher so sort of half of my work is

focused on designing uh learning

interventions that can help people

better understand AI Technologies

um so like Stefani I've worked with

families and kids and I'm also

interested in looking at other sort of

informal learning spaces like helping

people understand AI in the workplaces

and elsewhere in their everyday lives

and then the sort of flip side of my

research focuses on designing AI that

can better understand people

um so AI That's better able to engage

with us in sort of social creative

environments and explain itself to us

but today I'll talk about the the AI

literacy side of things

um

so in the the spirit of sort of the

um uh topic of the session

um I wanted to talk today sort of about

why I think AI literacy is important and

in particular um you know in the

educational context why I think AI is

sort of different from other types of

technologies that we interact with

um and why this this literacy is

necessary I'm going to talk a little bit

about how I Define and conceptualize AI

literacy

um it's uh you know a very like emerging

field and so I'm really excited to

engage in discussion about sort of what

fits in the definition of AI literacy I

think that's very much uh sort of still

forming

um and then I'm going to give one

example of how I have fostered ai

literacy in my own work uh just an

example of a project that I've worked on

um

so to dive into it

um I'm going to talk first about why I

think AI literacy is important

um and I I think this is important to

distinguish because there's a lot of

Technology that's become integrated into

our lives and even though we use it

every day we usually don't really need

to know much about how it works so I can

like screw in a light bulb and I usually

don't need to know a lot about

electricity except to understand you

know how not to get shocked

um I don't need to you know understand

in depth how my microwave Works in order

to use it and so something I get asked a

lot in my work is like how is AI

different from these Technologies uh you

know it's integrated into our everyday

lives it's showing up in our homes our

workplaces our schools and even in a lot

of the same contexts as the Technologies

I showed on the previous slides it's

being used in our TVs it's being used in

our light bulbs it's being used in our

cell phones so what sets AI apart from

these other technologies that are sort

of we sort of take for granted and we

don't necessarily dig into how they work

and and I think in my opinion one of the

things that really sets AI apart from

these other Technologies is that it's

engaged in active decision making

processes with and for us in a way that

other Technologies are not so AI is

deciding um you know what content to put

in front of us when it's selecting what

to show on our news feeds selecting what

to display when we type in a search

query it's providing us with

recommendations that it decides to show

us when we're engaging in entertainment

uh searches and shopping and an even

more consequential domains it engages in

decision making uh sort of with and on

behalf of us in terms of deciding people

to hire deciding when someone should be

released from jail deciding who and who

who is and who is not a criminal and I

think that this really makes AI

um much sort of more consequential and

more important for us to be able to

understand at least at a high level

um how it works so that we can you know

make critical decisions about where to

use it when it's appropriate to use it

um and I think this is sort of

exacerbated by the sort of Black Box

nature of the decision-making process

which has been shown in Prior work to

um

sort of lead to issues with trusting AI

so we you know we see the input that we

put in we see the you know search result

that we type into the query we see the

uh sorry we see the query we type in we

see the results that come back but we

don't always know what's happening in

between and this can lead to issues with

trust people not even recognizing that

they're engaging with algorithms

um and um sort of confusion over why

algorithms are making the decisions that

they are

um confusion over you know when it's

appropriate to share data

um and so I for all of these reasons I

think it's really important that we have

and Foster broader uh public AI literacy

and I think this is particularly

important within an educational context

um you know to to draw on the title of

the the conference to empower Learners

um to sort of critically engage with

when they're sharing their data with AI

algorithms understanding what that data

is being used for making sense of like

you know Technologies that they might be

using to help with their own research

and learning processes and understanding

you know what's put in front of them if

it's like recommended by an AI system so

I think this is equally as important in

an educational context as it is in other

contexts

um so with that context in mind I'm

going to talk a little bit about how

I've defined AI literacy in my own work

and how that sort of informed the work

that I've done

um

I've defined AI literacy in my work as a

set of competencies that enables

individuals to critically evaluate AI

Technologies communicate and collaborate

effectively with AI and use AI as a tool

online at home and in the workplace so

this is sort of a very practical

definition I'm thinking a lot about like

how this can be useful to people

and in particular the framing that I

take on AI literacy is focused on things

that can be useful to people even if

they don't know how to program AI so

they may not know how to develop or

build AI systems but what are some high

level ideas that they can sort of take

away that can help them as they engage

with systems in their everyday lives and

I take this Focus because I think it it

lowers the barrier to entry a little bit

for people and also helps to I think

there are useful things that people can

understand about AI that do not

um even if they may not have an interest

in learning how to program or build AI

um and so I situate AI literacy sort of

in respect to some other literacies that

are commonly talked about I see AI

literacy as sort of intersecting with

data literacy especially within the sort

of subfield of machine learning which

engages a lot with data

um I see other forms of literacy as

being able to inform AI literacy so for

example computational literacy uh

understanding how to like program and

um using computers to build and create

things I see this as something that can

certainly help understanding AI but it's

not necessarily uh required

um and

um but of course I see like

understanding how to use and interact

with devices as being more required for

AI literacy so this is sort of how I've

been conceptualizing this I'd love to

engage in more discussion about other

people's um you know thoughts on this

um and in my work I've engaged in a

review of uh literature

um sort of in the space

um relating to AI literacy so looking at

literature related to AI education human

AI interaction Computer Science

Education

um research on

um

sort of how people perceive and make

sense of AI and from that I've

synthesized a set of competencies and

design principles so the competencies

are sort of high level ideas uh for

people to understand about Ai and the

design principles are aimed at like

researchers researchers Educators and

designers who might be interested in

designing AI literacy learning

interventions some uh sort of principles

based on the literature that

um Can facilitate effective learning

interventions and I'm not going to talk

about all of these today but I just want

to highlight a couple

um to give you an idea of what I'm

talking about so one of the AI literacy

competencies

um that I defined based on prior work

was the ability to recognize the

computers often learned from data

including one's own data so this is the

type of sort of high level idea I'm

talking about

and one of the uh design principles is

to consider designing interventions with

embodied simulations of algorithms or

Hands-on Physical experimentation with

AI technology I know this is something

that both Stefania and I have found in

our work and that has sort of

corroborated each other is that this

sort of like tangible embodied nature of

AI literacy learning interventions can

really help concretize this like

abstract concept of AI for learners

and um I just want to close with um sort

of how I've fostered AI literacy in my

own work I'm just going to give one

example

um for context this was developed as

sort of an at-home activity

um for families with kids of all ages to

interact with we are focused mostly on

like Middle School age learners but we

were developing activities for like the

whole family to engage with

and in this activity uh Learners were

tasked with creating data sets to teach

an AI how to recognize and distinguish

birds from non-birds and they were given

a set of cards to create data sets with

so the cards contained creatures some of

the creatures uh were Birds others were

things that might be mistaken for Birds

such as like a turtle or an alligator or

a bat and each card contained a picture

of the creature and the list of features

describing the creatures with things

like it's Habitat it's color its size

and they were tasked with creating a

positive training data set and a

negative training data set with like a

very limited number of examples so they

placed like three cards in the positive

training data set three in the negative

training data set and they were supposed

to sort of engage in thinking about how

to balance even that very small training

data set so thinking about how to

represent

um you know all birds with just three

examples

um and then they could put weights on

different birds to put little bit more

weight on that example if they thought

it was more emblematic of being a bird

so if you put like three tokens on a

sparrow and one token on a goose the

algorithm would be trained with three

sparrows and one goose and then they

could take a picture of the board they

created upload it to a website we

developed and see how well their

algorithm did at classifying different

animals as Birds versus non-birds and

they could sort of iterate on this and

test out different outcomes and sort of

explore how different types of data sets

resulted in different outcomes

and this was intended to communicate AI

literacy competition competencies

relating to understanding the steps of

machine learning understanding how AI

uses knowledge representations to make

decisions and understanding that AI

systems learn from data and it used

design principles like contextualizing

data providing opportunities for social

and embodied interaction making AI more

explainable and providing opportunities

to like teach or program AI so that's

just one example of how I've done this

in in my own work and I look forward to

the panel discussion

thanks Jerry that was super interesting

I love love the video at the end

um we'll just in the interest of time

just pass it right over to Tyron if

you're if you're ready to get going

uh yeah let me go ahead and

share my screen and hopefully

fingers crossed

all right

um so I guess in for context uh I am a

been the educator for many years now and

and recently stepping away from the

classroom but as I kind of think about

digital literacy I was thinking of it

really from this practitioner standpoint

and so even in preparing for this it was

really looking at a lot of the work of

of various people

um but really looking at the work that

dirty long actually I did so kind of

what she was just explaining uh in some

of the competencies I began to kind of

dig into that and then think about some

of this research of what does this mean

from a practitioner's standpoint and it

was really around somebody's

competencies of Ethics

um in where we have designers that think

about some of these ethics pieces

um but it's helping students think about

the outside world and think about how do

we interpret what is the ethics of the

people who created this should I be

questioning how am I engaging with this

when they see something at school

because of the particular space or

spirit that they're in in that moment

there is a automatic assumption that

this is good for me and this is safe and

this is okay and sometimes students

bring that over into their home life and

to say if a friend has it or someone

shows this to me some of these things of

Ethics it must be okay and it must be

good for me and so as we get to think

more about this type of work I began to

think well what many of us probably grew

up with was this realm of digital

literacy or this term in the sub of

digital literacy and thinking about well

what's the culture around this and how

should I be saved and what's a practical

thing

um so for those of you who can remember

the days of like aim and AOL chats and

all that good stuff you would really

begin to think that was the initial push

for about the safety piece

um and so we didn't see anything wrong

with it Myspace was a thing at one point

where we just didn't see anything wrong

with chatting in certain ways and then

there became this like larger social

Consciousness around this term digital

literacy so I put the tape there to say

don't forget it it is really tied and

interconnected into this and

understanding what is a part of the

culture of

um the students that we're kind of

working with what is in the culture of

families and what is in the culture of

um this generation

and so thinking about in the culture of

the generation just

in that same P same vein uh we have to

think about what is it that we think

about as far as risk versus reward right

um this is what is the constant battle

whether that is an educator trying to

decide what do they bring into their

classroom

um District leaders trying to figure

those pieces out

um but students are are also starting to

have to think about this families are

starting to have to think about this big

risk versus reward and so in that vein

of both digital literacy and AI literacy

um you have to really begin to think

about

what is it that we're putting in front

of each other what are we putting in

front of our families and so this would

be um I'm not sure if anyone has been on

social media in the last like 72 to 120

hours you have probably seen a friend

family member someone who wanted their

morphed themselves into this these

different characters and these different

types of avatars to see what their

identities would look like

um

but in that it seems like one of those

it seems fun it seems entertaining it

seems very now and everyone is doing it

it's like all the cool kids are doing it

so we all want to try to do this try out

the Avatar everyone was trying to figure

out what is that but then

I go back to this and I began to think

about some of these components right

um I can't say inherently that the

developers are doing something unethical

but I

I'm not certain and then I began to

think about well is are they fully

transparent in certain things I then can

begin to think about a checklist for

myself to say do I want to engage or do

I not engage

um and so that is something that youth

are constantly having to deal with and

that Educators in the classroom are

constantly having to deal with to figure

out we want to be hip we want to be one

student to be engaged we want it to be

relevant

um to what's going on but we need a

checklist and I think that's one of the

things that

hasn't been as crystal clear because

technology has been so rapidly advancing

we haven't had a clear understanding of

what are the new checklists that we need

to do what are the new types of

questions we need to be asking to ensure

that both students and families can make

informed decisions about how they engage

with AI

um at that point I think the last I feel

like the best big wave of that might

have been around having Siri and uh

Alexa and saying well they're listening

to all our conversations and that would

was a big point contention for some

people about how much could they have

access to and how much cert how much

data is starting apps maintain and so

now some people just download apps

without even thinking twice about it but

it's that type of checklist that the

public needs to think about

um when we're thinking about what are

the known inputs and outputs of like

what are we actually feeding directly

into it and then what are we immediately

getting back but then also thinking

about what are the possible unknown

inputs and uh outputs that should be

um of what's going to be coming out of

this how is my data going to be used how

do I think about what are some of the

ethical pieces of this and then what are

the potential uh what is the potential

risk and reward for a lot of this so I'm

interested to hear from hear from you

all as we kind of talk about this and

delve into this a bit more

um just to see what people think

thanks everyone I really appreciate the

the perspective from someone who's been

in the classroom with middle school

right in the last few years

um yeah so I'm pretty sure seventh day

eighth graders will are right on this

um wave

um okay well thank you all for these

really really interesting talks I really

appreciate um your effort putting them

together I want to go ahead and uh you

know invite the the attendees to go

ahead and drop any questions that you

have in the the Q a box that you have

um but but I want to start um just kind

of falling off of Tyron while other

folks maybe have a chance to type in

their q a but

um I think you bring up with some really

interesting points about how to

translate this to uh teachers in the

classroom especially when you have this

um you know an age group that does have

access to a lot of devices and apps that

are just sort of readily available and

so I'm curious if any other panelists

have any ideas for what what that looks

like you know if you have any

recommendations for like you know or

what would be like a in a perfect world

how would that work for for especially I

know like you know talking about inside

the classroom or or um Stefania you were

talking about like at home like what are

what are some of the things that people

might pay attention to when we're

thinking about

what are the apps that would promote AI

literacy or what are the what what are

what are some of the things that we want

to consider as we are filled with the

world full of AI

yeah I just want to say I love the idea

of this like checklist of considering

you know what we should be looking at

when we're you know or what children

should be looking at when they're

starting to engage with these

Technologies and some of the things that

like pop into my mind that would be

interesting to have on you know such a

checklist is

um you know who created the data set

like where

um you know was this data like pulled

from

um asking what data like they're

collecting from you

um those would be some initial things

that I think would be valuable to ask

about these applications

um just off the top of my head

yeah

um

sorry about that uh I was about to say

the when you're saying that just now Dr

long

um even just thinking about these pieces

of pushing developers about this piece

of transparency on actually just having

to like blatantly list it out and not

buried in the fine terms of fine print

that could be randomly emailed to you at

a later time

I was going to say that my entire lab

actually wrote a book about this which

is available online uh it's called

critically conscious Computing and on

the each chapter has like teaching units

and activities and on the AI chapter

there's actually a unit on teaching AI

critique that actually goes through a

series of questions that you can ask

about each new application or each new

you know like technology model Gadget

that you might be exposed to or your

friends would introduce to you at home

in the classroom or just on the web so

I'll post the links on the chat

um that's like an immediate immediate

like action item or like response

um I think the part that is

I I really like that you talked about uh

lensa and you know stability Ai and

generative AI I think the part that is

tricky is that the technology is moving

very fast and it is for most part pretty

opaque it's a black box so

um it's kind of a chicken and egg

because you want people to understand

more how it works so they ask for their

rights so they understand like oh my

data is being scraped without my consent

or like this is actually using artists

work without uh proper reference or like

you you want to understand how the

technology works just so you can keep

the designers and the companies that

created accountable at the same time

it's hard to understand how how it works

when it's constantly evolving changing

and it's so opaque right so my push in

the space is to to have like more craft

with AI so instead of like okay I have

Dali I have lens I have all of these

things they make super cool images is

maybe like try to look at like where's

the API where's the SDK or like where is

like where can I see what's behind you

know like the scenes like how can I if

we had a physical device we could

actually open it and take it apart right

and we we did this activity with kids

where we asked them to draw what's

inside Alexa and it's fascinating right

so kind of like thinking like how do we

break these black boxes and how do we

constantly try to not be swayed Away by

the

you know the the hype wave and uh the

compelling examples but try to

understand more like how they work under

the hood

yeah I love that I like the the example

of asking them to draw draw it out

like inside

yeah alarms while you're unmuted

oh yeah I just had a question it's kind

of for Tyrone but it's kind of for

everyone but I was just thinking I feel

like we all kind of talked a lot about

teaching how AI works and and like how

the models works and I think that's

really really important but I also think

about like what our responsibility is

since we're talking a lot about this

within the context of Education I think

a lot about

you know a lot of funding is being

poured into educational Technologies

right now that are teaching you know

reading or writing or math or science or

all these sorts of things and a big

driver of the AI

is like automated scoring and adaptivity

and personalization of learning and I

think

I I guess I don't really know how to

form this question exactly Tyrone but

but I'm sort of wondering you know

what's the responsibility in in terms of

that transparency piece you're talking

about of these companies when when you

know you say something can score

something if you have people that don't

necessarily know how well it's scoring

and they're not going to sit there and

read your accuracy rates you know for

every single model and all the different

sorts of

um things but you know if we're saying

oh I can score this piece of writing

that you have right then

and we just put it in your classroom and

I don't know I was just thinking of that

piece and what you think we should do

about that

yeah and and it was it's funny as you

were asking I'm reading from uh the

question from Priscilla Gonzalez where

she is kind of hitting on this thing if

it's changing uh like human interactions

and the whole I guess that essence of

our human lives

um because some of it is you uh with

that adaptive AI I think some of the

frustration pieces that naturally

happened in learning

um

that yeah that naturally happens in

learning uh I sometimes gets eliminated

because students don't have to have some

of that collaboration with one another

and I think in the successful

integration of AI into spaces it's

making sure that there is that space for

collaboration there is some space for

there to even just be a step away from

the technology and it's just pins on

paper with a group with a couple group

members to brainstorm some things and so

I think it is trying to strike the

balance of understanding yes Ai and yes

technology is going to be there to

assist us with teaching but there are

still socialization that there's still

socialization that needs to happen

um in order for students to be able to

develop health and be able to be

productive in various settings

um whether that's within personal or

work settings

all right if no one else else has any

follow-up on that I just want to uh go

to we have about four minutes left maybe

three just so we have some time for

closing and

um we have a question about what AI

literacy looks like in University

programs that are specifically focused

on computer science

um and if any of these programs may be

missing some of the critical AI literacy

skills

whoever can Whoever has any thoughts can

jump in first

I guess I'm almost wondering if this is

some like with this question I guess if

someone could probably take away I

should say if some of this is the

critical thinking critical reasoning

pieces and aspects of it

um because I almost wonder in some of

those places of what does it mean for

some of that critical analysis of things

that we often approach to as being very

linear

um in in having just like a kind of on

off switch or if this is in the realm of

thinking about what's missing from

computer science programs is some of

that

um

what are the kind of ramifications of

what it is that we create

um so thinking about both the intended

consequences and under consequences of

the work and

uh that's why I think that one

competency uh

uh Dory you you all hit on the head when

you when you hit the part about the

ethics and I think it's often a part of

AI that is often missing and where we

are going to constantly need to Circle

back to about what are the ethical

immoral where where is ethics immorality

fall into exposing students to this

yeah I definitely think that ethics is

super important to hit on and I also

think that

um you know machine learning and like a

lot of the like most current AI

Technologies are not even necessarily

covered in a lot of like core CS

curriculum

um there's an interesting uh paper I'll

share here

um on machine learning

um in the undergraduate curriculum and

sort of a call for a need for that to be

incorporated

um you know in a sort of required way in

computer science undergraduate courses

and I think part of that is the the sort

of ethical implications and also

thinking about the I mean I something

I'm personally interested is sort of

like the interdisciplinary potential

um of a lot of these tools and

um you know how can we create

um you know experiences uh in like the

undergraduate curriculum or even in high

school curriculums that sort of help

span disciplines

um and engage people in learning about

AI in computer science but also you know

in the Arts and Humanities where it's

relevant

yeah this is super super interesting

thanks for sharing I see a lot of

resources in the chat so hopefully

everybody has a chance to to Mark those

down um we're just at the top of the

hour so I just want to yep go ahead

I had a quick ad to say that my first

reaction to this question was that

ethics shouldn't be an add-on on the Cs

classes

um and maybe that's the first question

that the Cs instructor should start with

it8 and taught in AI informatics ethics

and we start with the with ethics like

should we do this and what are the risks

and challenges and implications of

starting this project or doing this app

um so I think that should be the first

entry point and not a uh an add-on that

makes people feel good and I would say

the work that Casey fistler has on in

kind of keeping track of all the classes

and good resources for csfx would be a

great great thing to look at

thank you thank you for that Edition

okay well I just want to say thank you

again to all of you this is super

fascinating and and really uh some

interesting things to think about as we

as we move forward so thanks again for

your time I know that you all spend time

preparing for this so I really really

appreciate it and uh looking forward to

more later