More Like this

1. Machine Learning and the Five Big Ideas in AI

   Touretzky, David ; Gardner-McCune, Christina ; Seehorn, Deborah ( January 2023 , International journal of artificial intelligence in education)

   Wang, Ning ; Lester, James (Ed.)

   This article provides an in-depth look at how K-12 students should be introduced to Machine Learning and the knowledge and skills they will develop as a result. We begin with an overview of the AI4K12 Initiative, which is developing national guidelines for teaching AI in K-12, and briefly discuss each of the “Five Big Ideas in AI” that serve as the organizing framework for the guidelines. We then discuss the general format and structure of the guidelines and grade band progression charts and provide a theoretical framework that highlights the developmental appropriateness of the knowledge and skills we want to impart to students and the learning experiences we expect them to engage in. Development of the guidelines is informed by best practices from Learning Sciences and CS Education research, and by the need for alignment with CSTA’s K-12 Computer Science Standards, Common Core standards, and Next Generation Science Standards (NGSS). The remainder of the article provides an in-depth exploration of the AI4K12 Big Idea 3 (Learning) grade band progression chart to unpack the concepts we expect students to master at each grade band. We present examples to illustrate the progressions from two perspectives: horizontal (across grade bands) and vertical (across concepts for a given grade band). Finally, we discuss how these guidelines can be used to create learning experiences that make connections across the Five Big Ideas, and free online tools that facilitate these experiences. 

   more » « less
2. Unplugged K-12 AI Learning: Exploring Representation and Reasoning with a Facial Recognition Game

   https://doi.org/10.1609/aaai.v38i21.30376  

   Lim, Hansol ; Min, Wookhee ; Vandenberg, Jessica ; Cateté, Veronica ; Mott, Bradford ( March 2024 , Proceedings of the AAAI Conference on Artificial Intelligence)

   With the growing prevalence of AI, the need for K-12 AI education is becoming more crucial, which is prompting active research in developing engaging and age-appropriate AI learning activities. Efforts are underway, such as those by the AI4K12 initiative, to establish guidelines for organizing K- 12 AI education; however, effective instructional resources are needed by educators. In this paper, we describe our work to design, develop, and implement an unplugged activity centered on facial recognition technology for middle school students. Facial recognition is integrated into a wide range of applications throughout daily life, which makes it a familiar and engaging tool for students and an effective medium for conveying AI concepts. Our unplugged activity, “Guess Whose Face,” is designed as a board game that focuses on Representation and Reasoning from AI4K12’s 5 Big Ideas in AI. The game is crafted to enable students to develop AI competencies naturally through physical interaction. In the game, one student uses tracing paper to extract facial features from a familiar face shown on a card, such as a cartoon character or celebrity, and then other students try to guess the identity of the hidden face. We discuss details of the game, its iterative refinement, and initial findings from piloting the activity during a summer camp for rural middle school students.

    

   more » « less
3. Envisioning AI for K-12: What Should Every Child Know about AI?

   https://doi.org/10.1609/aaai.v33i01.33019795  

   Touretzky, David ; Gardner-McCune, Christina ; Martin, Fred ; Seehorn, Deborah ( July 2019 , Proceedings of the AAAI Conference on Artificial Intelligence)

   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. 

   more » « less
4. An empirical analysis of high school students' practices of modelling with unstructured data

   https://doi.org/10.1111/bjet.13253  

   Jiang, Shiyan ; Nocera, Amato ; Tatar, Cansu ; Yoder, Michael Miller ; Chao, Jie ; Wiedemann, Kenia ; Finzer, William ; Rosé, Carolyn P. ( July 2022 , British Journal of Educational Technology)

   To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making.

   What is already known about this topic

   Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.

   While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.

   There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.

   What this paper adds

   Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.

   Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.

   Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.

   Implications for practice and/or policy

   It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.

   Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.

   To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).

    

   more » « less
5. Developing and validating Next Generation Science Standards ‐aligned learning progression to track three‐dimensional learning of electrical interactions in high school physical science

   https://doi.org/10.1002/tea.21672  

   Kaldaras, Leonora ; Akaeze, Hope ; Krajcik, Joseph ( October 2020 , Journal of Research in Science Teaching)

   The Framework for K‐12 science education (TheFramework) and Next Generation Science Standards (NGSS) emphasize the usefulness of learning progressions in helping align curriculum, instruction, and assessment to organize the learning process. TheFrameworkdefines three dimensions of science as the basis of theoretical learning progressions described in the document and used to develop NGSS. The three dimensions include disciplinary core ideas, scientific and engineering practices, and crosscutting concepts. TheFrameworkdefines three‐dimensional learning (3D learning) as integrating scientific and engineering practices, crosscutting concepts, and disciplinary core ideas to make sense of phenomena. Three‐dimensional learning leads to the development of a deep, useable understanding of big ideas that students can apply to explain phenomena and solve real‐life problems. While theFrameworkdescribes the theoretical basis of 3D learning, and NGSS outlines possible theoretical learning progressions for the three dimensions across grades, we currently have very limited empirical evidence to show that a learning progression for 3D learning can be developed and validated in practice. In this paper, we demonstrate the feasibility of developing a 3D learning progression (3D LP) supported by qualitative and quantitative validity evidence. We first present a hypothetical 3D LP aligned to a previously designed NGSS‐based curriculum. We further present multiple sources of validity evidence for the hypothetical 3D LP, including interview analysis and item response theory (IRT) analysis to show validity evidence for the 3D LP. Finally, we demonstrate the feasibility of using the assessment tool designed to probe levels of the 3D LP for assigning 3D LP levels to individual student answers, which is essential for the practical applicability of any LP. This work demonstrates the usefulness of validated 3D LP for organizing the learning process in the NGSS classroom, which is essential for the successful implementation of NGSS.

    

   more » « less