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.

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.

Slides for using AI Chef Trainer with your students are available here.

Try it out here! AI Chef Trainer

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.

Try it here! Into The Rabbit Hole

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!

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.

AMA was presented at SIGCSE ‘24 in a poster entitled Perception, Trust, Attitudes, and Models: Introducing Children to AI and Machine Learning with Five Software Exhibits.

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.

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.

Try it here!

References

2024

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, 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, 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, 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, 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, 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.