AI for K-12

As part of the AI-CARING NSF Institute, we are developing a set of interactive tools to introduce novice learners to ideas in AI.

Ice Cream Express

Ice Cream Express was developed by Nicole Tian and Devansh Shah in the Developing AI Tools for K-12 course at UTSA (Fall 2025). This tool scaffolds the A* search algorithm for K-12 learners through an ice cream delivery game, where users are motivated to find the shortest path to maximize tips. The bot’s delivery visualizes the A* search and reveals heuristic logic. The tool also captures real-time telemetry and state logging for each user.

Try it here! Ice Cream Express

AI Hoops

AI Hoops was developed by Nicole Tian and Chris Arellano in the Developing AI Tools for K-12 course at UTSA (Fall 2025). This tool teaches Q-Learning through an interactive basketball game played with an AI bot. It visualizes the bot’s training process by displaying real-time Q-Table updates. Additionally, the tool captures telemetry and state logging to facilitate user analysis.

Try it here! AI Hoops

AI-See

AI-See was developed by UTSA undergraduate Durga Rajarajan in Spring 2025. It introduces the concepts of feature extraction in image recognition. Through interactive activities and challenge levels, AI-See teaches students about feature maps, kernels, and the matrix mathematical operations involved in convolution. AI-See was included in a summer camp module on image recognition, and a full paper will be presented at SIGCSE TS 2026.

Try it here! AI-See

Into The Rabbit Hole

Into The Rabbit Hole was developed by UTSA undergraduate Durga Rajarajan in Fall 2024. It introduces the concepts of depth-first search (DFS) and breadth-first search (BFS) via a game of helping a rabbit find carrots in a maze. It starts with a tutorial and then has a game mode where you have to solve as many DFS and BFS puzzles as possible in the time allotted. IntoTheRabbitHole: A Web Application for Teaching Middle School Students About Search Algorithms was presented at ITiCSE 2025 and was nominated for a Best Paper award.

Try it here! Into The Rabbit Hole

FaunaForest

FaunaForest was created by Pragathi Durga Rajarajan and Adrian Cisneros as part of the Spring 2024 Developing AI Tools for K-12 course at UTSA.

FaunaForest aims to teach K-12 students about decision trees via three levels of interactive decision tree puzzles. Each puzzle involves completing a decision tree that has blank mystery nodes in such a way that it will correctly classify various animals. FaunaForest: A Novel Software Tool for Teaching Decision Trees to Middle School Students was presented at the 2025 IEEE Integrated STEM Education Conference (ISEC).

Try it here!

DoodleIt

Taking inspiration from Google’s Quick, Draw! demo, Vaishali Mahipal (MS in CS, 2022) led the development of DoodleIt, a browser-based tool that demonstrates how Convolutional Neural Networks (CNNs) perform image recognition.

DoodleIt was presented as a poster at SIGCSE ‘23 and a paper at ACE ‘23. Here are worksheets for the hands-on kernel filter activities we created as part of developing the tool.

You can interact with DoodleIt at this live web link.

ChemAIstry

ChemAIstry is an interactive software tool for children which demonstrates training and classification in machine learning. Students select which everyday items are safe to bring into a chemistry lab (e.g., a lab coat is safe; pizza is not). These selections serve as training input for a decision tree classifier. After training, students see how the trained model performs in classifying new objects.

The software was developed by Garima Jain (BS in CS 2023) and Vaishali Mahipal (MS in CS 2022) with assistance from Dr. Ismaila Sanusi and Srija Ghosh (now a computer science student at Cornell University).

ChemAIstry was presented at SIGCSE ‘24. Here is the paper. Interact with the live version!

AI for American Sign Language

AI for American Sign Language (AI for ASL) teaches children hand gestures for several letters of the American Sign Language (ASL) alphabet while introducing them to image recognition and models.

It was developed by James Dimino and Andrew Farrell during a Spring 2023 class at UMass Lowell and adapted for the web by Angela Wang (now an undergrad in CS at Cornell University) and Ryan Maradiaga (finishing his BS in CS at UMass Lowell).

Here is a live web link to AI for ASL.

AI Chef Trainer

AI Chef Trainer was developed by UTSA Ph.D. candidate Saniya Vahedian Movahed in Spring 2024. Its goal is to introduce students to key ideas in machine learning via the engaging task of recipe recommendation. Students select ingredients to see what recipes are predicted by the AI Chef; then they enter their own recipes (retraining the AI) and then test to see how their recipes can then be recommended by the AI Chef. AI Chef Trainer: Introducing Students to the Importance of Data in Machine Learning was presented at the Educational Advances in AI symposium at AAAI 2025.

Ask Me Anything

Ask Me Anything (AMA) is a specialized chatbot that answers only topic-specific questions in three areas—astronomy, sneakers and shoes, and dinosaurs.

AMA was developed by UTSA Ph.D. candidate Saniya Vahedian Movahed (with UMass Lowell undergraduate Erika Salas) and is being used to study children’s attitudes of trust and confidence in AI chatbots.

Results from the study using AMA are published the article Ask Me Anything: Exploring Children’s Attitudes Toward an Age-tailored AI-powered Chatbot in the International Journal of Artificial Intelligence in Education.

References

2025

Ask Me Anything: Exploring Children’s Attitudes Toward an Age-tailored AI-powered Chatbot

Saniya Vahedian Movahed*, and Fred G. Martin

International Journal of Artificial Intelligence in Education, Oct 2025

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Conversational agents, such as chatbots, have increasingly found their way into many dimensions of our lives, including entertainment and education. In this exploratory study, we designed Ask Me Anything (AMA), a child-friendly, topic-specific chatbot, and investigated children’s attitudes and trust toward AI-driven conversational agents. Unlike general-purpose assistants, AMA was constrained to three child-relevant topics—astronomy, sneakers and shoes, and dinosaurs—to prompt targeted questioning from students and drive engagement. We tested AMA with 63 students, ages 6 to 14, including first-graders and middle school students (grades 6 to 8), in a public school in the Northeastern United States. Students worked in small groups, interacted with our tool for three to ten minutes, and completed a post-survey. We identified three key themes that emerged from student conversational interactions with AMA: expressing wonder, surprise, and curiosity; building trust and developing confidence; and building relationships and anthropomorphizing. Also, we observed a broad attitude of openness and comfort. Students trusted the chatbot responses in general, indicating a high level of trust in and reliance on AI as a source of information. They described AMA as “knowledgeable,” “smart,” and that they could “trust it.” To confirm their perception of reliability, some students tested the chatbot with questions to which they knew the answers. This behavior illustrated a fundamental aspect of children’s cognitive development: the process of actively evaluating the credibility of sources. This study contributes to the growing literature on child–AI interaction by revealing gaps in children’s critical engagement and highlighting the need for design that scaffolds AI literacy. It reveals the need for age-sensitive, trust-aware design and points to a broader gap in digital safety awareness among children interacting with AI.
From Play to Pedagogy: Discovering the Ecosystem of AI Educational Tools and Curricula BEST PAPER NOMINATION

Saniya Vahedian Movahed*, and Fred Martin

In Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1, Oct 2025

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This paper explores the evolution of artificial intelligence (AI) and machine learning (ML) educational tools and curricula for children, describing a spectrum from playful exploration to structured pedagogy. We address three research questions: (RQ1) What themes are revealed in the literature on AI/ML for children? (RQ2) In what ways do the development of AI/ML tools and curricula co-evolve? (RQ3) How does the historical context of the field frame current work on AI/ML for children? We observe a co-evolution of tools and curricula, where researchers build new tools and curriculum developers adopt tools created by others. As tools prove successful, they become widely available and are adopted by a broad range of users. Additionally, we note instances where tool creators take on the role of curriculum developer. Recognizing the need for a clear overview of AI/ML educational resources, we introduce major themes of tools, tool-forward, and curriculum-forward works. These themes are designed to help educators, researchers, and curriculum designers in discerning the fundamental nature and potential applications of resources. By distinguishing tools that are standalone from those integrated into curricula, our framework supports strategic decisions in resource adoption and development. Drawing on the field’s history, we outline a continuum of tools and platforms, from constructionist elements to interactive play environments.
IntoTheRabbitHole: A Web Application for Teaching Middle School Students About Search Algorithms BEST PAPER NOMINATION

Pragathi Durga Rajarajan*, and Fred Martin

In Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1, Oct 2025

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As artificial intelligence (AI) technology rapidly advances and integrates further into everyday life, building AI literacy in children is now more important than ever. AI literacy includes understanding key AI concepts such as search algorithms, which are foundational and have wide-ranging real-world applications. To support curricula that foster AI and computational literacy in K-12 students, we developed a novel, easy-to-use software tool to introduce this foundational topic by teaching two search algorithms to middle school children. We present IntoTheRabbitHole, an interactive web application where users help a rabbit find its carrot to learn about Depth First Search (DFS) and Breadth First Search (BFS). In this work, our research questions were: (RQ1) Can we develop an engaging and enjoyable web application to teach search algorithms?; (RQ2) Could we find evidence of student learning of Depth First Search and Breadth First Search from using our application?; and (RQ3) How do students compare Depth First Search and Breadth First Search after interacting with IntoTheRabbitHole? We collected both quantitative and qualitative data on student use of IntoTheRabbitHole at an after-school AI program. We found that IntoTheRabbitHole was successful in fostering an understanding of both DFS and BFS. Furthermore, we obtained a statistically significant result showing that interaction with IntoTheRabbitHole led to better performance on a DFS traversal pre- and post-survey item, indicating that many students learned DFS through interaction with our tool.
From Pre-Conceptions to Theories: How Middle School Student Ideas about Predictive Text Evolve after Interaction with a New Software Tool

Saniya Vahedian Movahed*, and Fred Martin

In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, Oct 2025

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We developed Next Word Adventure, a new software tool that helps middle school students understand n-gram models. Students see how n-grams are constructed from sentences that they provide to the software. Students can modify the n-value and see its effect on the n-gram that is created. A dynamic diagram helps students grasp the statistical processes and how data and parameter choices influence outcomes. In a study with 48 students from 6th to 8th grade, survey items revealed pre-conceptions, such as equating word prediction functionality with internet searches and overestimating such capabilities. After interaction with our software tool, 31 students recognized n-grams’ dependency on statistical data rather than assuming a cognitive understanding of text. They appreciated the significance of the n parameter in enhancing prediction accuracy. The study suggests the software was effective in helping students developing a probabilistic model of text prediction.
Word2Vec4Kids: Interactive Challenges to Introduce Middle School Students to Word Embeddings

Nathan Wiatrek*, Yash Verma*, and Fred Martin

Proceedings of the AAAI Conference on Artificial Intelligence, Apr 2025

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As Artificial Intelligence (AI) continues to integrate into more aspects of society, equipping younger generations with foundational AI knowledge becomes increasingly critical. This paper presents Word2Vec4Kids (W2V4K), an interactive application designed to familiarize middle school students with word embeddings, a key aspect of Natural Language Processing (NLP). W2V4K leverages the Word2Vec model, allowing students to explore word associations, similarity, and vector arithmetic through engaging game modes. The application was tested with 38 middle school students aged 11-14 at a Science Technology Engineering Math (STEM)-focused charter school. Data were collected on students’ interactions with the application, including screen recordings, audio, and survey responses. Results demonstrated that W2V4K effectively introduces NLP concepts to students. Qualitative observations revealed high levels of engagement with students expressing excitement and curiosity about word relationships. As they progressed through the game modes, students showed increasing confidence in predicting word associations, brainstorming relevant words, and connecting the concepts to real-world applications. Quantitative data from post-interaction surveys indicated positive learning outcomes with 44.5% of students achieving perfect scores on concept-related items. Additionally, students demonstrated an ability to critically think about language representation. This study suggests that W2V4K provides an effective and engaging method for introducing NLP concepts to middle school students, contributing to the broader goal of enhancing AI literacy among younger generations.
AI Chef Trainer: Introducing Students to the Importance of Data in Machine Learning

Saniya Vahedian Movahed*, and Fred Martin

Proceedings of the AAAI Conference on Artificial Intelligence, Apr 2025

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The AI Chef Trainer is an educational web app that introduces children to the role of data in machine learning (ML) through the engaging task of recipe recommendation. Initially, students tested the AI Chef’s capabilities by selecting from a list of ingredients to see what the system recommended as possible recipes. After observing the recommendations, they contributed by adding their own recipes—each being a set of ingredients and a corresponding recipe-name—which were used to retrain the model and finally re-tested recipe suggestions. This cyclical process of testing, contributing, retraining, and post-training testing provided students with hands-on experience in how AI systems learn and adapt over time based on new data. We tested our software with middle school students. The results indicated that students recognized the importance of both data quantity and specificity in the training process. 45 of 52 students entered recipes, and 26 of the 52 tested their own recipes using the specific ingredients they entered. Students were introduced to the concept of confidence percentages via the AI recipe suggestions. Even as the primary focus was the role of data in machine learning, the AI Chef Trainer software also served as a window into students’ cultural expression and personal preferences.
TrainYourSnakeAI: A Novel Tool to Teach Reinforcement Learning to Middle School Students

Cesar Hinojosa*, Priyanka Kumar, Pragathi Durga Rajarajan*, and 1 more author

In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1, Apr 2025

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Artificial Intelligence (AI) is growing rapidly in our society and is now apparent in our day-to-day lives. With the recent burst of interest in AI, many individuals and children may view AI as something mystic and magical. It is important to demystify and introduce to them how AI is made and works. To address this need, we developed a software application that allows children to specify the parameters used by a Reinforcement Learning (RL) algorithm. Then students experience how RL is used to train an AI model to play the game "Snake." This software tool was tested with 71 middle school-age students. Here, we describe the design of the TrainYourSnakeAI application, the approach we used to introduce the associated ideas to middle school children, and how we assessed student learning. Qualitative data collected from students are presented and discussed. We surveyed their knowledge of AI before and after using the application. In this work, our research questions were: (RQ1) How can we create an engaging tool to teach reinforcement learning? and (RQ2) Does using our application foster a stronger understanding of reinforcement learning in children? Our findings indicate that students were able to understand the functionality of reward functions and how agents can learn from the environment using the concept of RL. We found that out of the 51 students who were not previously familiar with RL, 40 were able to provide adequate descriptions of RL after using TrainYourSnakeAI.
FaunaForest: A Novel Software Tool for Teaching Decision Trees to Middle School Students

Pragathi Durga Rajarajan*, Adrian Cisneros*, and Fred Martin

In 2025 IEEE Integrated STEM Education Conference (ISEC), Mar 2025

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Artificial intelligence literacy is fast becoming a foundational skill for students to develop as artificial intelligence technology rapidly advances and integrates further into key areas of society and everyday life. Artificial intelligence literacy involves understanding key concepts such as decision tree algorithms, which are essential for making informed decisions in numerous fields. To support K-12 curricula that foster artificial intelligence literacy, we developed a novel, easy-to-use software tool to teach middle school students about decision trees. We present FaunaForest, an interactive web application that allows students to complete decision tree puzzles, helping them learn what decision trees are and how they function. In this work, our research questions were: (RQ1) What evidence is there of learning within FaunaForest?; (RQ2) Are there any differences in learning outcomes between different grade levels?; and (RQ3) Is FaunaForest engaging? We collected and analyzed both quantitative and qualitative data. Our findings showed that students who played Level 3 of FaunaForest multiple times demonstrated a statistically significant improvement in performance compared to those who played it only once. Overall, the students enjoyed playing FaunaForest and made meaningful connections to ideas they had encountered before.
ClawAI: A Software Tool Teaching Text Classification and AI Systems to Middle School Students

Aishat Kolawole*, Yeshaswini Parvatham*, and Fred Martin

In 2025 IEEE Integrated STEM Education Conference (ISEC), Mar 2025

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Machine learning (ML) is a transformative technology shaping the modern world, yet its concepts remain inaccessible to many, especially to middle school students. To address this gap, we created an educational game, ClawAI, that introduces AI text classification through interactive play. ClawAI was built using python and connecting our game to the Machine Learning for Kids platform using a Watson API Key. The game engages children in describing objects for an AI claw machine to identify. Students learn key concepts like confidence levels and text classification across three rounds of gameplay. Tested with 43 students aged 7–14, the game demonstrated its effectiveness in teaching AI concepts, with 34 participants showing understanding of text classification through gameplay. This suggests our game successfully bridges the gap between AI education and engagement, offering a scalable model for teaching machine learning in K–12 settings. We had two groups of students, one group that were visuals, and the other group were conceptual students. These two strategies emerged from successful and unsuccessful students. In our findings we found two distinctive groups being formed based on the way they played and the words they used to classify these items. Overall half of the students played the game well and proved how they understood how text classification works with AI, while the other half did not. Out of the half that did well, two-thirds of them showcased a learning experience. Our results demonstrate that our game and games alike can be utilized as a learning tool when students are taught about AI in K-12 education.

2024

AI MyData: Fostering Middle School Students’ Engagement with Machine Learning through an Ethics-Infused AI Curriculum

Ismaila Temitayo Sanusi, Fred Martin, Ruizhe Ma, and 5 more authors

ACM Trans. Comput. Educ., Dec 2024

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As initiatives on AI education in K-12 learning contexts continues to evolve, researchers have developed curricula among other resources to promote AI across grade levels. Yet, there is a need for more effort regarding curriculum, tools, and pedagogy, as well as assessment techniques to popularize AI at the middle school level. Drawing on prior work, we created original curriculum activities with innovative use of existing technology, a new computational teaching tool, and a series of approaches and assessments to evaluate students’ engagement with the learning resources. Our curriculum called AI MyData comprises elements of ML and data science infused with ethical orientation. In this article, we describe the novel AI curriculum and further discuss how we engaged students in learning and critiquing AI ethical dilemmas. We gathered data from two pilot studies conducted in the Northeast United States, one Artificial Intelligence Afterschool (AIA) program, and one virtual AI summer camp. The AIA program was carried out in a local public school with four middle school students aged 12 to 13; the program consisted of eleven 2-hour sessions. The summer camp consisted of 2-hour sessions over 4 consecutive days, with 18 students aged 12 to 15. We facilitated both pilot programs with hands-on plugged and unplugged activities. The method of capturing data included artifact collection, structured interviews, written assessments, and a pre- to post-questionnaire tapping participants’ dispositions about AI and its societal implication. Participant artifacts, written assessments, survey, observation, and analysis of tasks completed revealed that the children improved in their knowledge of AI. In addition, the AI curriculum units and accompanying approaches developed for this study successfully engaged the participants, even without prior knowledge of related concepts. We also found an indication that introducing ethics of AI to adolescents will help their development as ethically responsive citizens. Our study results also indicate that lessons establishing links with students’ personal lives (e.g., letting students choose personally meaningful datasets) and societal implications using unplugged activities and interactive tools were particularly valuable for promoting AI and the integration of AI in middle school education across the subject domains and settings. Based on these results, we discuss our findings, identify their limitations, and propose future work.
Introducing Children to AI and ML with Five Software Exhibits

Saniya Vahedian Movahed*, James Dimino*, Andrew Farrell*, and 10 more authors

In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, Dec 2024

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Artificial intelligence (AI) and machine learning (ML) have a deepening impact in our world. For empowered citizenship and career readiness, elementary and middle school students need to understand these technologies. To provide engaging introductory experiences, we created five original interactive software exhibits introducing children to hands-on activities in AI and ML. The exhibits were tested by 125 elementary and middle school students (aged 7 to 14). Four themes emerged: Students recognized that AI and ML systems can process data from cameras (perception); they saw that these systems responded to their training input (trust); they appreciated the practical import of AI/ML systems (affective and cognitive attitudes); and students were introduced to models and modes (specialization). This paper presents our goals in creating the exhibits and results from our initial testing with children. Our work contributes to the literature on formal and informal activities for introducing AI and ML to young learners.
ChemAIstry: A Novel Software Tool for Teaching Model Training in K-8 Education

Fred Martin, Vaishali Mahipal*, Garima Jain*, and 2 more authors

In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, Dec 2024

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Machine learning (ML) systems are increasingly in use in society. For young learners to be informed citizens and have full career potential it is important for them to understand these concepts. To support this learning, we created "ChemAIstry,” an interactive software tool for children which demonstrates training and classification in machine learning. Students select which everyday items are safe to bring into a chemistry lab (e.g., a lab coat is safe; pizza is not). These selections serve as training input for a decision tree classifier. After training, students see how the trained model performs in classifying new objects. ChemAIstry was tested with 40 students aged 7 to 14 years at a public K?8 school. The software captured student selections during training. We analyzed these interactions to yield a "Correspondence Score,” a measure of student understanding of the classification task. We screen-recorded student use of the software and audio-recorded our conversations with them during this use. Our analysis of these data indicates that students were able to understand the concept of model training, including that items were subsequently classified based on their training input. More than half of the student trials indicated that students correctly understood the task. This suggests ChemAIstry was effective in introducing students to these ideas in machine learning. We recommend continued development of related tools for curriculum integration of AI in K-8 education.

2023

AI Literacy: Finding Common Threads between Education, Design, Policy, and Explainability

Duri Long, Jessica Roberts, Brian Magerko, and 3 more authors

In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, Dec 2023

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Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy.
Developing Machine Learning Algorithm Literacy with Novel Plugged and Unplugged Approaches

Ruizhe Ma, Ismaila Temitayo Sanusi*, Vaishali Mahipal*, and 2 more authors

In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, Dec 2023

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Data science and machine learning should not only be research areas for scientists and researchers but should also be accessible and understandable to the general audience. Enabling students to understand the details behind the technology will support them in becoming aware consumers and encourage them to become active participants. In this paper, we present instructional materials developed for introducing students to two key machine learning algorithms: decision trees and k-nearest neighbors. The materials were tested in a middle school’s afterschool artificial intelligence program with four participating students aged 12 to 13. A combination of hands-on activities, innovative technology, and intuitive examples facilitated student learning. With hand-drawn decision trees and penguin species classifications, students used the algorithms to solve problems and anticipate other possible applications. We present the technology used, curriculum materials developed, and classroom structure. Following the guidelines from AI4K12 and introducing foundational machine learning algorithms, we hope to foster student interest in STEM fields.
DoodleIt: A Novel Tool and Approach for Teaching How CNNs Perform Image Recognition

Vaishali Mahipal*, Srija Ghosh*, Ismaila Temitayo Sanusi*, and 3 more authors

In Proceedings of the 25th Australasian Computing Education Conference, Dec 2023

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To introduce middle school students to key concepts in image recognition, we created an interactive web application that performs sketch recognition and an afterschool curriculum for its use. Our app, called DoodleIt, was inspired by Google’s Quick, Draw!, and makes use of its accompanying open-source sketch library. With DoodleIt, students make simple line drawings on a canvas area and a previously-trained convolutional neural network (CNN) identifies the object drawn. The application dynamically visualizes the different layers that are involved in the process of CNNs, including a display of kernels, the resulting feature maps, and the percentage of match at output neurons. We used DoodleIt in an 18-hour curriculum to introduce middle school students to artificial intelligence, machine learning, and data science. Four hours of content were related to image recognition and the ethics of using AI. Here, we describe the design of the DoodleIt application, the approach we used to introduce the associated ideas to the students, and how we assessed student learning. Qualitative data collected from students are presented and discussed. Our findings indicate that students were able to understand the functionality of the kernels and feature maps involved in the CNN to perform rudimentary image recognition.

2019

Envisioning AI for K-12: What Should Every Child Know about AI?

David Touretzky, Christina Gardner-McCune, Fred Martin, and 1 more author

Proceedings of the AAAI Conference on Artificial Intelligence, Jul 2019

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The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA’s national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work.