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Why is the most tinker able tool Teachable Machine from 2017.

...good question.

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While I understand the value of understanding how AI works and wanting students to actively create/interact with creating models, etc., I am unsure of how "essential" it is. Students, adults, humans are going to use AI and absolutely see its products daily. If we are really thinking about distilling this down to what is vital to know, I wonder if we can say building AI is more important or as important as understanding when and how to use an AI tool. Also, can we write something that will make sense in 5 years given how quickly developments in AI will continue to happen?

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The main thing you can cover in a short period of time is how people feel about the topic, building identity and connection. What Emily's class at ReachU does, also talked to Charlotte about what Mark Cuban measures.

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With a full AI plate, using books like the Dr Seuss books to embed digital literacy is a way forward.

__

I will also add my own book series in here - Amazon.co.uk : ava and chip :)

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The presentation from Mahmoud was good as it really encouarged thought around how much power we had over to companies who generate AI articles and other artefacts "on our behalf".

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Linking books like Dr Seuss and computational thinking. Of course I enjoyed this as I also write books for children :)

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In our group discussion, we spoke about examples of modality representation. Many more of these examples need to be surfaced to help educators. I had a few ah-ha moments :)

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Amber Jones examples of career cards, robot horses, speech and facial recognition were all great.

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"I don't want them to think the Pyramids must be made by aliens because they don't know algebra" -- Amber Jones.

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While we are thinking about data privacy: Latanya Sweeney's Data Privacy Lab has a lot of interesting visualizations and interactive things around data anonymity, how it travels, and privacy concerns. I've used it with a lot of classes, and it's often an aha moment for the class. https://dataprivacylab.org

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Trustworthy AI teaching modules for K-12 Educators

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CAP: Capacity Building for Trustworthy AI in Medical Systems (TAIMS) - SACS Meharry

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CAP: Capacity Building for Trustworthy AI in Medical Systems (TAIMS) Upcoming Seminars News Feed This project is supported by National Science Foundation under award number 2334391. Any opinions, findings, conclusions,…

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Kate Lockwood's student seeing data science in art and the world around them is #goals for my classes.

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Also loved the 3Cs and want to use it as a framework - Consumers, Creators and Citizens

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Computers are not Magic. AI is not magic (despite things that actively have "magic" in their name). That's what is important for our K-2s to understand.

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K-2 & beyond lessons about AI. Many of these have been used in upper elementary, middle school, high school, and even in higher ed. All lessons include CSTA standards & AI4K12 Guidelines.

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I just want to take a moment and be in awe of this group and this work. The results of these conversations has the potential to positively impact teachers and students for years...just kind of amazed that I am here.

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"I got 180. Did you get 180? Wait, they got 180 too! Teacher!! We all got 180!!!"

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I have done a few workshops run by the Tang Institute - they have a philosophy teacher connect with a CS teacher to create ethics content connected to all the technical content throughout the year. Don't know what they've been up to very recently, but I've been impressed with their content. https://tanginstitute.andover.edu/projects/ethics-project

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The ethi{CS} project helps computer science and other technical educators incorporate ethical thinking meaningfully into their coding and other classroom projects.

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Virginia Computer Science Standards of Learning: https://www.doe.virginia.gov/home/showpublisheddocument/57144/638609727259600000

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With the focus on CS not generally - what sort of understanding of civics are we expecting of students in a CS class? What foundations in that do we need? (On the slido, but I thought worth posting here maybe?)

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I recently learned about a tool that acts like a social media system except the data goes only to the teachers. It allows students to see what systems can learn about people, and what it will show them in a safer setting. (Below is a paper about it that links to it - linking directly to the system doesn't give as much info about it.) https://arxiv.org/html/2402.01813v1

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Nicolas Pope1, Juho Kahila2, Jari Laru3, Henriikka Vartiainen2, Teemu Roos4 and Matti Tedre1 1School of Computing, University of Eastern Finland, Joensuu, Finland 2School of Applied Educational Science and Teacher Education, University of Eastern Finland, Joensuu, Finland 3University of Oulu, Faculty of Education and Psychology 4Department of Computer Science, University of Helsinki, Finland
Email: firstname.lastname@uef.fi
Email: firstname.lastname@uef.fi
Email: firstname.lastname@oulu.fi
Email: firstname.lastname@helsinki.fi

Abstract

This paper introduces an educational tool for classroom use, based on explainable AI (XAI), designed to demystify key social media mechanisms-tracking, profiling, and content recommendation-for novice learners. The tool provides a familiar, interactive interface that resonates with learners' experiences with popular social media platforms, while also offering the means to "peek under the hood" and exposing basic mechanisms of datafication. Learners gain first-hand experience of how even the slightest actions, such as pausing to view content, are captured and recorded in their digital footprint, and further distilled into a personal profile. The tool uses real-time visualizations and verbal explanations to create a sense of immediacy: each time the user acts, the resulting changes in their engagement history and their profile are displayed in a visually engaging and understandable manner. This paper discusses the potential of XAI and educational technology in transforming data and digital literacy education and in fostering the growth of children's privacy and security mindsets.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

I Introduction

Social media has become an integral element of daily life for many people, fundamentally influencing the way people of all ages, including children, perceive and interact with the world [1]. Its impact has been the focus of sustained research efforts, and educational interventions have followed. Research studies have highlighted the importance of educating users, especially the youth, about how to mitigate risks related to social media, focusing on misinformation, privacy, and personal cybersecurity [2, 3, 4, 1, 5]. Consequently, social media education has primarily meant guidelines on themes like information literacy, privacy policies, and preventing cyberbullying [6, 7, 8]. Many of these initiatives avoid discussing the actual mechanisms and algorithms used by social media platforms, such as profiling, so that most people resort to "folk theories" when asked to explain their functioning [9, 10].

We argue that, while important, education focusing on personal safety awareness and guidelines is not enough: learning about the mechanisms underlying social media platforms is necessary for preparing learners to understand the algorithm-driven dynamics on social media platforms, including "how echo chambers are formed, emotions amplified, and behavior engineered-and through them how products are sold, elections swayed, and mass movements born" [11]. Going beyond surface-level understanding also enables children to assume a more active role in envisioning alternative solutions that better meet their needs and expectations.

A major challenge in educating young learners about the mechanisms of social media lies in the absence of practical, classroom-ready tools that work at a level of abstraction that exposes the mechanisms at play but does not bury those mechanisms under complicated technical detail. Unplugged methods can provide a bird's eye view on demystifying these complex mechanisms, but they fall short in terms of demonstrating how automation and scale change the dynamics. Unplugged methods are also challenged by the multiplicity of ways in which behavioral patterns are captured and aggregated into digital footprints and targeted content through profiling and recommendation algorithms.

The gap between learning material and reality is particularly concerning in light of growing concerns around the ethical implications of tracking, profiling, and recommendation systems. The need for innovative educational tools for filling this gap is evident-we need tools elucidating mechanisms underlying social media platforms in safe and engaging ways, allowing children to learn through hands-on experience without compromising their personal data.

This paper presents Somekone ( https://somekone.generation-ai-stn.fi/), an educational tool for learning the mechanisms of data collection, profiling, and recommendation in social media. The tool provides learners an image feed app with the usual social media engagement modalities: like, follow, share, comment, and react with emoji. Learners can monitor and explore the depth of data collection either on the app or by connecting another smartphone for monitoring. They can explore how profiles develop through different kinds of user engagement, and explore how recommendations are made based on data and profiles. At the same time the teacher can visualize on a projector how the classroom's social network develops in real-time as learners scroll through images and engage with them. Fig. 1 shows a photo from a classroom intervention, with two laptops paired to show an image feed and analytics data, as well as the social network display for the whole classroom.

This paper presents a technological proof of concept on teaching children about the inner workings of social media platforms. It presents the design principles, security and privacy features, and functionalities of Somekone. It demonstrates an approach to teaching children the principles of algorithm-driven automation on social media platforms, done at an abstraction level that does not require advanced mathematical concepts or programming skills. It advocates an approach that relies on immediacy of feedback between learners' browsing activities and analytics views. The tool we describe serves as an example of GDPR-safe social media educational technology that respects children's privacy in a safe learning environment. The paper is aimed at educators, researchers, and policymakers interested in developing social media education in K-12 schools.

II Design of the Tool

Somekone (literally translated as "social media machine") has a user interface reminiscent of Instagram, which allows children to learn in an easy-to-use environment that many are already familiar with [12]. It provides an infinite scrolling feed of images, coupled with a set of generic social media engagement features, such as liking, reacting with emoji, commenting, following other users, and sharing with everyone, friends, or privately. The image set is derived from a hand-picked and labeled set of 727 images, selected from Pixabay by two classes of eight-graders and labeled by the same children at the time of selection. The dataset was further curated by two researchers. Uploading children's own images is prohibited to prevent any uncurated, age-restricted material from entering the classroom, and to maintain image tag cohesion.

The tool is designed to respect children's privacy. All engagement data are collected and stored locally within the teacher's laptop in the classroom; none of children's data are stored on external servers. Only the app, along with the image and label dataset, are retrieved from outside the classroom. The tool uses WebRTC for peer-to-peer communication between devices, eliminating the need for an Internet connection beyond the initial signaling, provided the devices are in the same network.

The three key mechanisms that Somekone is designed to illustrate are (i)𝑖 (i)( italic_i ) tracking, (i⁢i)𝑖 𝑖 (ii)( italic_i italic_i ) profiling, and (i⁢i⁢i)𝑖 𝑖 𝑖 (iii)( italic_i italic_i italic_i ) content recommendation. Additionally, it provides a detailed view into (i⁢v)𝑖 𝑣 (iv)( italic_i italic_v ) the social network formed by building a graph where users with similar profiles are grouped near each other. Below we describe these key concepts and functionalities.

II-A Data Collection (Tracking)

The first of the three key social media mechanisms that Somekone is designed to teach is data collection (tracking). It is implemented by tracking a variety of engagement metrics from the user's browsing session. These metrics include seeing an image, time spent viewing an image, likes, comments and their length, periods of user inactivity, reactions with one of five different emoji, following the image creator, and sharing the image privately, with friends, or publicly. Given the classroom context, some data collection channels, such as location data, cannot be captured. Consequently, the typical Instagram features like likes, follows, and comments are slightly extended to include additional reaction types found on other platforms. Figure 2 shows a screenshot of the interface when the user engages with the like button, revealing the range of available emoji reactions.

To facilitate exploration of the relationship between their engagement scores and the data collected in the user's action log during their browsing sessions, children can access a real-time visualization of the action log. Somekone offers two methods for this: An integrated "Engagement Overview" tab for individual use case, and the same view from a separate device for pair work (Figure 2(a) ). This paper presents a pair work example: "Jarmo" browses the image feed on his device and "Anna" monitors, on her own device, the underlying data collection process as Jarmo engages with the image feed. In Figure 2(a) Anna connects her device to Jarmo's, allowing her to view a live feed of all the data Somekone is tracking about Jarmo's session. For example, her feed indicates Jarmo's high level of engagement with a microphone image (10/10 engagement), including sharing it with friends, commenting, following its creator, and reacting with a "heart eyes" emoji. As Jarmo continues to browse his feed, Anna's device displays the collected data in real time.

II-B Profiling (Modeling)

The second key concept that Somekone is designed to teach is profiling. This is implemented by transforming user actions into profiles, clustering them, and visualizing the results. The profiles in this tool are primarily composed of topic affinities, updated based on the user's engagement with labeled images. The user's action log is then used to generate an engagement score for each image. Each type of interaction carries a different weight, contributing more or less to the engagement score. These scores, combined with the labels of the images, update the user's affinity for each label. The set of label affinities is transformed into a taste vector, which can be compared with other users using cosine similarity (see, e.g., [13]). The resulting similarity score is used as the weight on the social network graph's edges, facilitating clustering of users with similar tastes in the visualization. To enhance recommendation accuracy, each user's profile includes a set of learned weights indicating the most effective recommendation strategies or features for that user. More complex profiling, such as demographic identification or using features beyond topic affinities, is not pursued due to limited resources and data in the browser, and due to the increasing difficulty to explain such features to learners.

Content profiling is also enhanced by user data, where related images are connected using co-engagements. This method of collaborative filtering allows for the identification of relationships between images beyond their labels, although it requires substantial interaction data. Figure 4(b) presents a graph of image co-engagements after a learning session with 18 learners.

To facilitate children's exploration of how their profiles are created, Somekone offers a range of visualizations of the profiling mechanisms. The tool combines image tags and engagement scores to create a list of the most engaged topics, visualizing those tags as a word cloud. This word cloud is broken down into individual tags, and for each tag the tool displays a set of images that the user engaged with more than others. It also visualizes which topics received the most engagement through actions such as sharing, following, reacting with emoji, and viewing. Figure 2(b) shows a screenshot where Anna is analyzing Jarmo's profile on her device. The profile is updated in real-time as Jarmo continues to interact with the image feed. Anna sees that Jarmo's most engaged topics are #musiikki (music) and #taiteellinen (artistic), and by scrolling further, she can view a breakdown of the most engaged #musiikki-tagged images and other visualizations of Jarmo's profile.

II-C Recommendation

The third key concept to be taught, recommendation, was implemented by ranking image candidates based on a number of criteria (for an overview of recommendation systems, see, e.g., [14]): a) collaborative-filtering using engagement by other similar user profiles; b) content-based filtering using the user's topic affinities identified from tag engagements; c) collaborative-filtering via image co-engagement and popularity (images engaged with together in a session); and d) occasionally, random selection. Each of these candidates is scored and ranked using a range of user and content features, with the most significant score component being provided as an explanation of the ranking. Children learn that recommendations in social media do not require the systems to understand or care about the image content or semantics. Tags are not essential, either, although they improve the quality of recommendations. The key requirement is engagement data from numerous users. They also discover that their continuous interaction with social media not only enhances their personal experience but also influences other users' experience with the platform, too.

Figure 2(c) shows a screenshot where Anna is analyzing Jarmo's recommendations. She can see Jarmo's queue of the next images and scroll down for a detailed explanation of why each of the next five images has been queued. For example, in Fig. 2(c) , the next recommended image is a photo of studio mixing table with neon colors, and Somekone provides a verbal explanation saying it was selected due to its high taste similarity to Jarmo's most engaged topic #musiikki, and because it had not appeared in Jarmo's feed recently.

II-D Social network

To illustrate what "similarity" means and to show how clusters of similar users develop in social media services, Somekone shows a real-time visualization of social network of the current class, clustered by profile similarity. Children learn that clustering forms around not just their friends or whom they follow but around anyone with a similar profile. They also learn how clustering influences the recommendations they see. The visualization uses a force layout where edges act as springs and nodes are charged to repel each other. User similarity is used as the edge weight which determines the strength and length of the edge in the visualization, a strong similarity score between users results in shorter and stronger edges. Consequently, similar users move together to form clusters. Colouring is achieved by propagation along the similarity weighted edges.

Figure 4 shows an example teacher view from classroom testing, shown to children on the classroom projector. It shows eight children's profiles as nodes labeled by their nicknames and filled with their most engaged images. Nodes are clustered by their profile similarity, based on tags in images they have engaged with the most. The teacher can swap between a variety of visualizations such as tag cloud (Fig. 4(a) ) and engagement score, highlight profiles and their connections, examine each children's profile (data log, profile, recommendations), and display image co-engagement (Fig. 4(b) ) or topic co-engagement (Fig. 4(c) ) networks.

III Discussion

This paper presents an educational tool designed to expose and explain the processes by which familiar social media platforms track users, profile them, and recommend content tailored to individual preferences. It aims to foster the development of data agency and critical data-conscious mindsets among children at a crucial age when their data agency, ability to safely navigate the digital environment, and personal data strategies are evolving [15]. It also aims to enable children to understand the implications of their online behavior to their social media experience.

This tool bridges the gap in existing educational tools for teaching social media mechanisms by simplifying complex concepts without overwhelming young children with technical details. The tool's main innovation lies in its real-time visualizations and the immediacy of its interactive elements, enhancing the learning experience: Every action a user takes on on one device immediately impacts the displays on other devices, illustrating the dynamics of data collection, profiling, and recommendations in an understandable and tangible manner. The tool serves as an example of how explainable AI (XAI) principles can be applied in a practical, educational context to help learners develop their understanding of digital technologies beyond folk theories that people commonly resort to in order to explain their everyday experiences [9].

The tool supports experiential learning, encouraging children to learn through hands-on experience, and it encourages them to build their own understanding of social media mechanisms by experimenting with a system that exposes the underlying mechanisms of social media in a real-time visual environment.

References

[3] N. Selwyn and L. Pangrazio, "Doing data differently? Developing personal data tactics and strategies amongst young mobile media users," Big Data & Society, vol. 5, no. 1, pp. 1-12, 2018. [Online]. Available: https://doi.org/10.1177/2053951718765021

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An Educational Tool for Learning about Social Media Tracking, Profiling, and Recommendation

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Nicolas Pope1, Juho Kahila2, Jari Laru3, Henriikka Vartiainen2, Teemu Roos4 and Matti Tedre1 1School of Computing, University of Eastern Finland, Joensuu, Finland 2School of Applied Educational Science and Teacher Education, University of Eastern Finland, Joensuu, Finland 3University of Oulu, Faculty of Education and Psychology 4Department of Computer Science, University of Helsinki, Finland Email: firstname.lastname@uef.fiEmail: firstname.lastname@uef.fiEmail: firstname.lastname@oulu.fiEmail: firstname.lastname@helsinki.fi Abstract This paper introduces an educational tool for classroom use, based on explainable AI (XAI), designed to demystify key social media mechanisms-tracking, profiling, and content recommendation-for novice learners.

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Sharing access to GCPS AI Learning Framework & K-12 Continuum (for all) and 3 course CTE high school pathway standards (shared during CSTA 2024) for reference during best practice presentation.

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"Interrogation is an important word here" - thinking about the power that we're ceding to these systems directly.

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Foundational content gets weird in HS - how much Math does every student needs to know? Bio / Chem / Physics? Personally, I think less than what we currently teach in those subjects, but it's good to have them available. Where does AI knowledge fit on that scale?

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A thing I've been thinking about a lot wrt gigo - the input to LLMs is GIGANTIC - we've traditionally talked about gigo with having more representative samples, but with LLMs, the problem is we have too many not-representative samples. How do we change the way we talk about bias in input for LLMs?

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Where do the AI ethics conversations in classrooms end up? Ethics is complex philosophy. where do students land in the different approaches?

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This is just an FYI for my own interest...when should the use of AI tools be cited? Any strong feelings on this?

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"What do all adults need to know about AI to function in society?" Dr. Em Thomforde rethinks what it means to be (and teach) a non-CS teacher.

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Teaching teachers about AI, teaching teachers to teach about AI. how much distinction is being drawn in current efforts?

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An outcome of “meaningfully engaging with future PD when it’s available” is really interesting, clever, and insightful.

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We talked about edge detection when looking at the standards, and the amount of math that often takes, which is a lot for middle schoolers. Going to pitch my own work for a minute - and the work of the people I highlighted - in the How CNNs work which made it a lot easier to process edge detection without knowing matrix Math. (The resources are increasingly old and I'm not keeping them updated anymore, but lots of them are still relevant)

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Access free educational resources from AI4ALL to explore AI. Designed for 9th grade through early AI professional, our tools and guides help you learn, teach, or build a career in artificial intelligence.

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So just a thought...Love the interrogation question idea/approach. We have contextualized AI with a similar question-driven approach. Is there a world where this work can lead with guiding questions?

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Resource introduced by Dr. Sonia Koshy, Kapor Foundation

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Are there pre-requisite commitments, values, or knowledge for best uptake of critical questions/interrogation? Seems important to ask with "...for all" goals.

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Can you post the report and dataset from IACE that compared state standards?

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I have a student who is doing student-led research on AI in education, the military and journalism that will become a paper by the end of the school year. He has an anonymous survey about educator's experiences, and I told him I'd share with this crowd. Thanks in advance for helping a student out! https://docs.google.com/forms/d/e/1FAIpQLSfhoxYDHkNBkTJ3M_i-6yYitnsefg_GDedCcke9NfIwQ0u22w/viewform

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Page 19 foundational content? You spoke about clarifying student learning objectives as goal for this meeting. Are the outcomes starting on page 19 in reimaging CS pathways what you are referring to?