Survey: Artificial Intelligence, Computational Thinking and Learning

2022 ◽  
Author(s):  
Nina Bonderup Dohn ◽  
Yasmin Kafai ◽  
Anders Mørch ◽  
Marco Ragni
Keyword(s):  
Artificial Intelligence ◽  
Computational Thinking

scholarly journals PopBots: Designing an Artificial Intelligence Curriculum for Early Childhood Education

2019 ◽  
Vol 33 ◽  
pp. 9729-9736 ◽  
Author(s):  
Randi Williams ◽  
Hae Won Park ◽  
Lauren Oh ◽  
Cynthia Breazeal
Keyword(s):  
Artificial Intelligence ◽  
Young Children ◽  
Computational Thinking ◽  
Social Robot ◽  
Kindergarten Children ◽  
Learning Needs ◽  
Teaching Approaches ◽  
Childhood Education ◽  
Hands On ◽  
Specific Learning

PopBots is a hands-on toolkit and curriculum designed to help young children learn about artificial intelligence (AI) by building, programming, training, and interacting with a social robot. Today’s children encounter AI in the forms of smart toys and computationally curated educational and entertainment content. However, children have not yet been empowered to understand or create with this technology. Existing computational thinking platforms have made ideas like sequencing and conditionals accessible to young learners. Going beyond this, we seek to make AI concepts accessible. We designed PopBots to address the specific learning needs of children ages four to seven by adapting constructionist ideas into an AI curriculum. This paper describes how we designed the curriculum and evaluated its effectiveness with 80 Pre-K and Kindergarten children. We found that the use of a social robot as a learning companion and programmable artifact was effective in helping young children grasp AI concepts. We also identified teaching approaches that had the greatest impact on student’s learning. Based on these, we make recommendations for future modules and iterations for the PopBots platform.

Critical Digital Humanities

2019 ◽  
Author(s):  
James E. Dobson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Methods ◽  
Digital Humanities ◽  
Data Science ◽  
Computational Thinking ◽  
Major Question ◽  
Computational Tools ◽  
Multiple Dimensions ◽  
Selection For

This book seeks to develop an answer to the major question arising from the adoption of sophisticated data-science approaches within humanities research: are existing humanities methods compatible with computational thinking? Data-based and algorithmically powered methods present both new opportunities and new complications for humanists. This book takes as its founding assumption that the exploration and investigation of texts and data with sophisticated computational tools can serve the interpretative goals of humanists. At the same time, it assumes that these approaches cannot and will not obsolete other existing interpretive frameworks. Research involving computational methods, the book argues, should be subject to humanistic modes that deal with questions of power and infrastructure directed toward the field’s assumptions and practices. Arguing for a methodologically and ideologically self-aware critical digital humanities, the author contextualizes the digital humanities within the larger neo-liberalizing shifts of the contemporary university in order to resituate the field within a theoretically informed tradition of humanistic inquiry. Bringing the resources of critical theory to bear on computational methods enables humanists to construct an array of compelling and possible humanistic interpretations from multiple dimensions—from the ideological biases informing many commonly used algorithms to the complications of a historicist text mining, from examining the range of feature selection for sentiment analysis to the fantasies of human subjectless analysis activated by machine learning and artificial intelligence.

scholarly journals LearningML: A Tool to Foster Computational Thinking Skills Through Practical Artificial Intelligence Projects

2020 ◽  
Vol 20 (63) ◽  
Author(s):  
Juan David Rodríguez García ◽  
Jesús Moreno-León ◽  
Marcos Román-González ◽  
Gregorio Robles
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Lessons Learned ◽  
Supervised Machine Learning ◽  
Hands On ◽  
The Face ◽  
Multiple Levels ◽  
Critical Citizens

El uso de sistemas de inteligencia artificial en múltiples niveles de la sociedad ofrece nuevas y prósperas oportunidades, pero también introduce nuevos riesgos y cuestiones éticas que deben abordarse. Sostenemos que la introducción de contenidos de inteligencia artificial en las escuelas a través de proyectos prácticos es el camino a seguir para educar ciudadanos conscientes y críticos, para despertar vocaciones entre los jóvenes, y para fomentar las habilidades de pensamiento computacional de los estudiantes. Sin embargo, la mayoría de las plataformas educativas de programación existentes carecen de algunas características necesarias para desarrollar proyectos completos de IA y, en consecuencia, se requieren nuevas herramientas. En este artículo presentamos LearningML, una nueva plataforma dirigida al aprendizaje automático supervisado, una de las técnicas de IA más exitosas que se encuentra en la base de casi todas las aplicaciones actuales de IA. Este trabajo describe las principales funcionalidades de la herramienta y discute algunas decisiones tomadas durante su diseño, para el que hemos tenido en cuenta las lecciones aprendidas al revisar trabajos anteriores realizados para introducir la IA en la escuela y el análisis de otras soluciones que permiten proyectos prácticos de IA. También se presentan los próximos pasos en el desarrollo de LearningML, que se centran en la validación, tanto aparente como instruccional, de la herramienta. The use of artificial intelligence systems in multiple levels of society offers new and thriving opportunities, but also introduces new risks and ethical issues that should be dealt with. We argue that the introduction of artificial intelligence contents at schools through practical, hands-on, projects is the way to go in order to educate conscientious and critical citizens, to awaken vocations among youth people, as well as to foster students’ computational thinking skills. However, most existing programming platforms for education lack some required features to develop complete AI projects and, consequently, new tools are required. In this paper we present LearningML, a new platform aimed at learning supervised Machine Learning, one of the most successful AI techniques that is in the basis of almost every current AI application. This work describes the main functionalities of the tool and discusses some decisions taken during its design, for which we took into account the lessons learned while reviewing previous works carried out for introducing AI in school and from the analysis of other solutions that enable practical AI projects. The next steps in the development of LearningML are also presented, which are focused on both the face and instructional validation of the tool.

Artificial intelligence, computational thinking, and mathematics education

2017 ◽  
Vol 34 (2) ◽  
pp. 133-139 ◽  
Author(s):  
George Gadanidis
Keyword(s):  
Artificial Intelligence ◽  
Mathematics Education ◽  
New Media ◽  
Classroom Practice ◽  
Computational Thinking ◽  
Sociocultural Perspective ◽  
Cognitive Ecology ◽  
Content Type ◽  
Human Thinking ◽  
And Mathematics

Purpose The purpose of this paper is to examine the intersection of artificial intelligence (AI), computational thinking (CT), and mathematics education (ME) for young students (K-8). Specifically, it focuses on three key elements that are common to AI, CT and ME: agency, modeling of phenomena and abstracting concepts beyond specific instances. Design/methodology/approach The theoretical framework of this paper adopts a sociocultural perspective where knowledge is constructed in interactions with others (Vygotsky, 1978). Others also refers to the multiplicity of technologies that surround us, including both the digital artefacts of our new media world, and the human methods and specialized processes acting in the world. Technology is not simply a tool for human intention. It is an actor in the cognitive ecology of immersive humans-with-technology environments (Levy, 1993, 1998) that supports but also disrupts and reorganizes human thinking (Borba and Villarreal, 2005). Findings There is fruitful overlap between AI, CT and ME that is of value to consider in mathematics education. Originality/value Seeing ME through the lenses of other disciplines and recognizing that there is a significant overlap of key elements reinforces the importance of agency, modeling and abstraction in ME and provides new contexts and tools for incorporating them in classroom practice.

Computational Thinking in Basic School in the Age of Artificial Intelligence: Where is the Teacher?

2021 ◽  
Vol 23 (6) ◽  
pp. 270-299
Author(s):  
Celina A.A.P. Abar ◽  
José Manuel Dos Santos Dos Santos ◽  
Marcio Vieira de Almeida
Keyword(s):  
Artificial Intelligence ◽  
Computational Thinking ◽  
Basic School
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