Overview matrix of coded ML teaching activities

Table 1: Overview matrix of coded ML teaching activities: learning procedure, learner perspective, context category, and supplementary codes (set 1/2). Code micro:bit ScratchAI (Arastoopour Irgens et al., 2022) Staging (Bilstrup et al., 2020) danceON (Castro et al., 2022) Cognimates AI (Druga and Ko, 2021a) Third Space for AI (Druga and Ko, 2021a) GTeach (Dwivedi et al., 2021) Role-Playing Game (Henry et al., 2021) NLP4All (Hjorth, 2021) PoseBlocks (Jordan et al., 2021) Ethics and AI (Krakowski et al., 2022) AI-Infused (Lee et al., 2021b) ML Model in Scratch (Ng et al., 2022) PRIMARYAI (Lee et al., 2021c) LearningML (Rodr´ ıguez-Garc´ ıa et al., 2021) SmartBin (Song et al., 2022) Teachable Machine Primary (Toivonen et al., 2020) Collaborative-ML-Model-Building (Tseng et al., 2024) PoseNet (Vartiainen et al., 2020) CONVO (Zhu and Van Brummelen, 2021) ML4K (Shamir and Levin, 2022a) StoryQ (Jiang et al., 2022) JS-Eden (Toivonen and Jormanainen, 2016) MLM2.0 (Bilstrup et al., 2022) SignLanguage (Vahedian Movahed et al., 2024) Learn Machine Learning (Mariescu-Istodor and Jormanainen, 2019) MixMatch (Jansen and Colombo, 2023) ArtBotSL (Voulgari et al., 2021a) ArtBotRL (Voulgari et al., 2021b) ARtonomous (Dietz et al., 2022) ApricotStoneCity (Moore et al., 2024) Hexapawn (Opel et al., 2019) Scratch-NB (Quiroz and Gutierrez, 2024) RL Activity (Annaluru et al., 2022) Teachable-Machine (Chen et al., 2020) ClassyTrashMonster (Bae et al., 2022) QUBE (Xie et al., 2019) ChemAIstry (Martin et al., 2024) Pasta-Land (Ma et al., 2023) Penguin-k-NN (Ma et al., 2023) ML-Quest (Priya et al., 2021) CODAP NetsBlox (Broll et al., 2022) Contour-to-Classification (Lee and Ali, 2021) LuminAI (Long et al., 2021) DoodleIt (Mahipal et al., 2023) GANs (Ali et al., 2021a) VotestratesML (Kaspersen et al., 2022) neuron-based water system (Shamir and Levin, 2022b) Mini-Impurity (Lehner and Landman, 2025) Food Decision Tree learning (Podworny et al., 2021) Decision Tree Learning in Orange (Godec et al., 2019) Decision Tree Learning (Elia et al., 2021) ML with Candy (Huppenkothen and Eadie, 2021) SmileyCluster (Wan et al., 2020) Glyphs (Zhou et al., 2021) Early Introduction of AI (Fern´ andez-Mart´ ınez et al., 2021) Capture-it! (Guerreiro-Santalla et al., 2022) TryColors (Lee et al., 2021a) Maze (ENARIS, 2023) Calypso-for-Cozmo (Tedre et al., 2021) TensorFlow Playground (Sato, 2016) Lawn bowling (Mindetbay and Woollard, 2019) K-Means (Jatzlau et al., 2019a) ANN (Jatzlau et al., 2019a) Crawling-robot (Laumeyer et al., 2023) Expected-Goals (Krone and Fischer, 2023) ScratchML4K (Garcia et al., 2019) Learning procedure reinforcement l. X X X X X X X X X X unsupervised l. X X X X supervised l. X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Perspective user X XXXXXXX XXXXXXXXXXXXXX X XX X X X X X X X XXX X X X X technical X X X X X X X X X X X X X X X X X X X X X X X X X X X X societal X X X X X X X X X X X Context category many contexts X X X X X X only context X X X X NLP c. X X X X X X X X X X X visual c. X X X X X X X X X X X X X X X X X X X X X X X X X X data c. X X X X X X X X X X X X games X X X X X X generative AI X X autonomous driving X X robot X X X X X X X no context X X X X X X X X Supplementary codes NN X X X X X X unplugged X X X X X X X X X X X X X plugged X XXXX XXXXXXXXXXXXXXXXXX XXXX X XXXX XXXXX XX XX XXXXX XXXXXX X Table 2: Overview matrix of coded ML teaching activities: learning procedure, learner perspective, context category, and supplementary codes (set 2/2). Code Chocolate-Chips (Evangelista et al., 2018) AWS-DeepRacer (Garza-Coello et al., 2023) Robobo-SmartCity (Naya-Varela et al., 2023) Q-Learning-Playground (Olari et al., 2021) NeuralNetwork-Playground (Olari et al., 2021) Post-its (Olari et al., 2021) Cognimates (Druga, 2018) Tooee (Park and Shin, 2021) Text-Classifier (Reddy et al., 2021) AlpacaML (Zimmermann-Niefield et al., 2020) ScratchML (Agassi et al., 2019) K-MEANS Scratch (Estevez et al., 2019) ANN (Estevez et al., 2019) Goldrush (Michaeli et al., 2020) Milo (Rao et al., 2018) ecraft2learn (Kahn and Winters, 2018) PRIMARYAI (Lee et al., 2021c) BlockWiSARD (Lacerda Queiroz et al., 2021) Zhu’s MIT App-Inventor (Zhu, 2019) PopBots (Williams et al., 2019) interaction with black-box (Hitron et al., 2019) WoZ-based (Hitron et al., 2018) CART (Kajiwara et al., 2023) Monkeys (Lindner et al., 2019) Images with Neural Networks (Lindner et al., 2019) Snap! (Kahn et al., 2018) Personalizing homemade bots (Narahara and Kobayashi, 2018) Machine-Learning-Unplugged (Ossovski and Brinkmeier, 2019) Mango (Sakulkueakulsuk et al., 2018) Any-Cubes (Scheidt and Pulver, 2019) cookbook (Van Brummelen et al., 2021a) Build a Neural Network (Curiosity Machine, ) recycling-problem (Essinger and Rosen, 2011) AppInventor (MIT App Inventor, 2023) Sports and Machine Learning (Zimmermann-Niefield et al., 2019) AppInventor (MIT App Inventor, 2023) ReadyAI (ReadyAI, nd) Q-Learning Snap! (Jatzlau et al., 2019b) Minecraft-Education-AI-4-Oceans (Minecraft Education, 2024) AI For Oceans (Shamir and Levin, 2022c) AIR4Children (Kammoun et al., 2022) Neuron-Sandbox (Touretzky et al., 2024) Brain in a Bag (Touretzky et al., 2024) Personal Image Classifier (Technology, 2019) DeepScratch (Alturayeif et al., 2020) ecraft2learn (Kahn and Winters, 2018) mBlock5.0 (Sabuncuoglu, 2020) AWS Simulator (Holowka, 2020) Industry 4.0 Robots (Verner et al., 2021) Interactive Visualizations (Chittora and Baynes, 2020) AIThaiGen (Aung et al., 2022) SAILORS (Vachovsky et al., 2016) Alexa (Van Brummelen et al., 2021b) DeepFakes (Ali et al., 2021b) nim game (Alexandre et al., 2021) PlushPal (Tseng et al., 2021) GenderBias (Melsi´ on et al., 2021) CUHKiCar (Chiu et al., 2022) Google Teachable Machine and Scratch (Yu and Huang, 2024) TicTacToe (Barelli et al., 2024) BugBrain (Morton, 2002) WiSaRd (Queiroz et al., 2024) Make-me-Happy (Druga and Ko, 2021b) Semantic networks (Ketamo, 2009) The-Popstar (Van Brummelen, 2019) Personal Image Classifier (Technology, 2019) Child friendly (Kahn and Winters, 2017) Learning procedure reinforcement l. X X X X X X X X X unsupervised l. X X X X X X X X supervisedl. X X XXXXX XXXXXXXXXXX XXXX X XX XX X XXXX XXX XXXXX XX XXX Perspective user X XXXXX XX XXXX XX XX XXX XX XXXX XX XXXX XX XXXXX technical X X X X X X X X X X X X X X X X X X X X X X X X X societal X X X X X X X Context category many contexts X X X only context X X X NLP c. X X X X X X X X X visual c. X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X data c. X X X X X X X X X X X X X X games X X X X X generative AI X X autonomous driving X X X X X X robot X X X X X no context X X X X X X X X Supplementary codes NN X X X X X X X X X unplugged X X X X X X X X X X plugged XXXX XXXXXXXXXXXXXXXXX XX XXX XXX XXXXXX XXXXXXXXX XXXXXXXXXXXX REFERENCES Agassi, A., Erel, H., Wald, I. Y., and Zuckerman, O. (2019). Scratch nodes ml: A playful system for children to create gesture recognition classifiers. 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