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Similarly to the n. 9 proposal, AI can be used as an artificial student, not as a peer of our human students, but as a junior student. This means that our students would have the task to teach AI on various topics. AI, in this case, will act as a student, giving feedback on what has been clearly explained and what has not. This process of "learning by teaching" will foster reasoning and consolidation of knowledge and skills in our students.

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Use AI like Renaissance artists and craftspeople ran their workshops where the student establishes the creative approach or palette and AI generates works based on it. The learner controls the elements of the story, the music, the painting using their knowledge of the domain and their own creative identity. Then the AI  produces a plethora of new artifacts based on the learner’s creative style.

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Our MS Robotics students craft, connect, and code creative cardboard bots using Hummingbird technology kits, and showcase them at a Robot Petting Zoo. In addition to all the sensors that come with the kit, we're excited to build image, audio, and pose recognition into the bots for the next Zoo!

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Biomimicry - showing complex systems for better understanding. 

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Biomimicry - showing complex systems for better understanding. 

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Using AI/ML to develop agents and models that allow you to think about and then practice having difficult conversations, working through conflict, and standing up for yourself and others

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Provide Dynamic Support to Constructionist Learning Experiences with LLMs

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Provide Dynamic Support to Constructionist Learning Experiences with LLMs

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With the capability of understanding & generating code, LLMs have the potential to support many constructionist learning experiences (e.g. writing Logo/Arduino/Scratch programs) in a dynamic & personalized way. How can we leverage this potential into the future? How should we reconsider the learning objectives for computational literacy in the coming age of code-capable LLMs? What will continue / cease to be the core skills working with AI? - John Chen

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Climate - sustainability - learning the complexities of the solutions and the interactions needed for global warming Include engineering, human impacts, and solutions analysis.

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Climate - sustainability - learning the complexities of the solutions and the interactions needed for global warming Include engineering, human impacts, and solutions analysis.

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Create interactive stories, where the storyline not just depends on a deliberate action by the player, but the presented story options change based on previous decisions.
To create and program such a story could give insight in how "filter bubbles" work. One could also include information about browser, device, etc., that are available for a programmer, but the player does not knowingly provide it.

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(AI/ML coupled with AR) Learners can interact with physical simulations with corresponding visualizations on a screen. These simulations can help students visualize and understand difficult concepts like underlying invisible forces (e.g., gravitational forces, electricity/electromagnetism, temperature/heat etc.) (Radu & Schneider, 2019). For example, using computer vision libraries like OpenCV, learners can put multiple objects in front of a camera and visualize the transfer of heat between two objects. Learners might be able to determine certain parameters of the different objects (in this case, the thermal resistance of different objects) and then play out a simulation of the interactions between the objects.

Similar use-case for kinetic energy:
https://proceedings.neurips.cc/paper/2015/file/d09bf41544a3365a46c9077ebb5e35c3-Paper.pdf

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Creative proof “literature”

Socratic dialogues with Socrates and others

Debates

Illustrated stories

Text-based adventures

See slide 19 in

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14 web apps created by conversing with ChatGPT

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14 web apps created by conversing with ChatGPT

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See slide 10 for the list of those I've made.

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Students can program virtual pets that can speak to them. Like a tamagatchi with sass.

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Make a to do list app that uses AI agents to give advice or break down complex tasks. Use API tools to have agents actually do the things on the list (i.e. Schedule a meeting with Nathan Holbert) Example

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Take ingredients as user input and then have AI mix them into strange and disgusting (or tasty) recipes. Example

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In Solomon and Papert (1971), #16 says to understand something you should make it, and to better understand it #18 says to explain yourself and make revisions according to your reflections.  Then go back to #16. AI/ML applications are increasingly in our world and are affecting our daily lives. There are concerns about the ethics used in creating these applications, resulting in a desire to be able to have critical inquiry as part of the creative process. One way children could model this inquiry process is by choosing a type of application that happens in their everyday life (we’ll use scheduling) and finding more concrete or familiar ways to model it. They would contact the creators of the application and ask them questions like "How do we know your software treats all people fairly ?”, or "How do you ensure that some people don't get preferential treatment due to bias in the systems?”. As an example, I’ve chosen a company I happen to be familiar with.  Gray Oncological Solutions creates a scheduling system for the oncology departments of hospitals, using machine learning algorithms.  The answer to the questions are basically that the patients have a clinical priority assigned by a human and that is what is used to help organize their treatments, and is where biases could occur.  Students would also create schedules, but for their schools (gym times, library usage, etc) using human based decisions on how the spaces should be distributed. They could go all concrete and use colour coded blocks or cards to be able to create schedules, or they could use a computer to do the scheduling (many ways to do this). The important part for this would be thinking about the ethics of how weights were formed by humans to affect the scheduling.  For example, there might be a rule that says a certain class must always have gym after lunch.  Once they have a working schedule, they can discuss how fair they think the “rules” they created were, adjust them , and go back to #16.  Repeat until satisfied. This type of engagement with ideas that human input can be adjusted and will cause the results to change could result in more empathic thinking and more regular questioning of ethical considerations.  In other words, less about the results and more about the ethical nature of the process.

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Generate Conversations between Fictional or Historical Characters

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Generate Conversations between Fictional or Historical Characters

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You can create multiple AI agents with personalities of fictional or historic people and then introduce them to each other and loop them into a conversation. Example

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Write a program that takes user input and then creates a joke using that input. Example

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Generate twenty more ideas for creative AI!

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Espacio Sensorial con un asistente virtual IA.

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Espacio Sensorial con un asistente virtual IA.

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Es una realidad que tanto los estudiantes como los profesores muchas veces nos encontramos con sentimientos desencontrados, situación difíciles y estrés que debemos dejar de lado cuando estamos en el aula. En la actualidad esta situación está muy presente ante los grandes cambios de la sociedad y se hace muy difícil hasta el punto de no poder brindar lo mejor de nosotros mismos. Si bien buscamos alternativas para salir adelante (actividades físicas, amigos, viajes, terapias, entre otros), también somos conscientes de que los tiempos son acotados y también queremos pasar tiempo con nuestras familias.
Para esta situación comparto la idea de crear un Espacio Sensorial que interactúe con 4 de los sentidos (vista, olfato, tacto y oídos) a través de aromas, música, luces, tecnologías y un asistente virtual con IA que se pueda comunicar con las personas y sus emociones, brindándole un espacio con diferentes actividades virtuales que desarrollen su concentración, memoria, habilidades, emociones y valores; con el fin de promover una experiencia en donde las personas encuentren relajación y reflexionen sobre su vida y situaciones vividas que pueden mejorar para continuar creciendo como tal.

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In #18, Solomon and Papert (1971) invite learners to explain themselves asking “How good of a model could you make of a person?.” A similar thing could be done with AI/ML, inviting learners to think about how the qualities of synthetic text differ from those of text written by humans, how good of a co-learner an AI/ML system can be, or how good of a player an AI agent can be. The explain yourself activity can serve as a space for learners to think about the differences between human and machine learning, to explore the limitations of both humans machines and to think about their own thinking.

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“Design models of students (rather than teachers) that would accompany children in learning new material and articulate (perhaps simplistic, but concrete) strategies for thinking about the material. Such models might be customizable or programmable for students–that is, students might be able to create (in high-level form) particular rules for learning to see how those rules play out in the behavior of their artificial colleague.” (Eisenberg et al. 2017 p.3)

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#8 Artificial creature or system for a natural habitat

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#8 Artificial creature or system for a natural habitat

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Building on advances in embodied cognition, Eisenberg and colleagues propose that children could “create free-standing simple creatures whose job is to be placed in a terrarium or aquarium alongside living creatures and to interact with their (biological) environment” (Eisenberg et al. 2017 p.3) to reflect on the embodied nature of their own thinking. Such projects would involve building AI/ML models for the systems to recognize environmental behaviors and react to them. This would also be a rich space for learners to think about technology not only as embodied and socio-technical but also as being in relationship with ecological systems. “Children might create birdhouses (to be placed in natural settings) that offer food and shelter preferentially to certain species of birds, or only under specific conditions of temperature or illumination; studying how birds respond to the artifact over time might be an introduction both to ontogenetic learning in animals and (over still longer periods) to the notion of evolutionary constraints.” (Eisenberg et al. 2017 p.3) Such projects would also involve thinking about ethics and the environmental impact of children’s creations.

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Papert & Solomon (# 11, 1971) suggested creating a music box with programmed tunes. Later in #12, they present a project that creates random songs. Today, children could build data sets of tunes and use them to train a model that generates music, these “compositions” could follow the traditions of aleatoric and minimalist music or even explore noise music traditions. Here, like in #2 learners could engage in discussions about creativity in humans and machines and copyright.

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Young people interact with AI/ML powered face filters when using social media (SnapChat, TikTok) with their friends. Creating filters can involve them in learning about AI/ML in two ways: exploring how to recognize facial features and body parts and using generative techniques to create images to transform what is captured or superposed over the camera feed. Making face filters can be a powerful way for learners to explore how something that they interact with everyday works, to inquire into why some filters work for some people, and to engage with ideas of algorithmic justice in a personally relevant way.

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Similarly to #4, there’s a lot of promise for young people to learn about AI/ML while engaging with sports (Jordan et al., 2021; Kumar & Worsley, 2023; Zimmermann-Niefield; 2019). Here youth could create applications that recognize athletic moves and support them in improving their form (e.g., when pitching a ball). Other applications could imitate commercial fitness apps, supporting youth to create models with data generated with their own bodies (e.g., heart beat, body temperature, speed). These types of applications could also serve as opportunities to discuss issues of data privacy and ethics.

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Dance in AI/ML education has been growing over the past few years with projects that have learners partner to dance with AI agents (Long et al., 2020) and have learners create choreographies and animations (Castro et al., 2022; Jordan et al., 2021). Building on these efforts, children could design their own dance games similar to Just Dance, where they train models to recognize players’ moves (sequences of poses) using either pose recognition or data from wearable sensors and compare these to predetermined choreographies or where based on a set of basic moves the system generates possible choreographies that match music rhythms. In building their games, children could incorporate their own music and dance taste, create personalized animations and test the games with their peers.

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In the original memo, Papert and Solomon (#19, 1971) propose making puppets and using motors to control them. With AI/ML we can extend this idea to create more interactive puppets that can recognize words and speech or movements. Older children and youth could have puppets recognize speech or words (from using simple classifiers to recognize keywords to more complex voice recognition libraries) and respond to them accordingly (with preset responses—à la Eliza—or using synthetic text and text to speech libraries). Younger children, as Tseng and colleagues (2021) demonstrate, could use plushies to tell stories and build classifiers of plushie movements to trigger sounds and voice recordings.

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Papert and Solomon (#15, 1971) explored how creating sentence generators may support learners to better understand language structures and computing. With current off-the-shelf AI/ML libraries, learners can generate poetry while exploring more sophisticated natural language processing techniques. From simple Markov chains to training models using nanoGPT, youth can create synthetic text including poetry, short stories, and music lyrics. Creating synthetic text can also provide opportunities to inquire and discuss critical issues related to disinformation, copyright, creativity, the limitations of synthetic text, and the amount of energy that language models may consume.

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Making games has always been a rich context for engaging with computing in constructionist ways. With AI/ML children cannot only create games to play with each other (#5 Papert & Solomon, 1971), but also train models that when incorporated into the games can enable them and their peers to play against the machine. Examples include creating, training, and then using neural networks to play TicTacToe as Kahn and Winter (2021) suggested in half-baked AI projects or by designing and playing a Rock-Paper-Scissors game with an application that recognizes speech and hand gestures (Kahn & Winter, 2018). These activities may support learners to think about how they think when playing games as they design and build models they can play against.