This paper describes the results of a systematic review dealing with the use of personas, avatars, and characters in conjunction with AI-supported tools such as chatbots or generative AI in education. Although the use generative AI in education is gaining traction, this study seeks to systematically review the body of knowledge dealing with personified and conversational approaches to education with both pre-generative and generative AI. The results of the study emphasize the importance of the three key elements of such systems: the use of pedagogical agents, interaction, and personalization. These key elements can be relevant when considering the adoption of the new generation of generative AI in education. Such systems should scaffold learners’ understanding providing guidance and support, promote self-directedness and ensure effectiveness in learning, provide customized learning paths, and promote ethical use.

We will demonstrate blocks integrated into Snap! capable of a wide range of AI services, interactive AI programming guides, and a selection from thirty sample projects. Sessions and workshops in both school settings and informal learning contexts have been held in many countries. The full version of this paper includes descriptions of the Snap! blocks and unpublished descriptions of student experiences in India.

The interest in artificial intelligence (AI) education is growing exponentially; nevertheless, how to learn about AI, particularly Natural Language Processing (NLP), has been a challenging problem for educators and researchers worldwide. This study used a graphical programming platform Snap! to facilitate learning by allowing learners to explore AI and its NLP techniques in class. Data from 18,452 logged events were collected and Lag Sequential Analysis (LSA) was used to examine how learners behaved and learned sequentially. Non-cognitive factors were used to group learners as detailed and subtle behavior sequences that did not occur by chance could be uncovered. The results showed that five groups of learners, that is Passive Learners, Performers, Adaptive Learners, Interested Learners, and Dedicated Learners. They presented varied learning behavior patterns, which should be considered further in designing personalized and intelligent learning platforms to support AI education.

Logo is more than a programming language. It is a learning environment where children explore mathematical ideas and create projects of their own design. Logo, the first computer language explicitly designed for children, was invented by Seymour Papert, Wallace Feurzeig, Daniel Bobrow, and Cynthia Solomon in 1966 at Bolt, Beranek and Newman, Inc. (BBN). Logo’s design drew upon two theoretical frameworks: Jean Piaget’s constructivism and Marvin Minsky’s artificial intelligence research at MIT. One of Logo’s foundational ideas was that children should have a powerful programming environment. Early Lisp served as a model with its symbolic computation, recursive functions, operations on linked lists, and dynamic scoping of variables. Logo became a symbol for change in elementary mathematics education and in the nature of school itself. The search for harnessing the computer’s potential to provide new ways of teaching and learning became a central focus and guiding principle in the Logo language development as it encompassed a widening scope that included natural language, music, graphics, animation, story telling, turtle geometry, robots, and other physical devices.

Teams of students creating digital artefacts using crafts, 3D printing, electronics, microcontrollers, and computer programming can result in significant science, technology, engineering, art, and mathematics (STEAM) learning. An ecosystem of carefully selected tools, diverse project ideas, and a well-designed pedagogic structure can greatly facilitate this.

Desktop ToonTalk was first released twenty years ago and was successfully used by children as young as 3 to construct computer programs. Uniquely these programs are constructed by demonstration using game elements such as robots, birds, trucks, boxes, and magic wands. The user's avatar inhabits a town where computations can be created and executed in an animated manner.

Book synopsis: This publication covers papers presented at the Artificial Intelligence in Education conference 2009 (AIED). AIED2009 is part of an ongoing series of biennial international conferences for top quality research in intelligent systems and cognitive science for educational computing applications. The conference provides opportunities for the cross-fertilization of techniques from many fields that make up this interdisciplinary research area, including: artificial intelligence, computer science, cognitive and learning sciences, education, educational technology, psychology, philosophy, sociology, anthropology, linguistics and the many domain-specific areas for which AIED systems have been designed and evaluated. AIED2009 focuses on the theme 'Building learning systems that care: from knowledge representation to affective modelling'. The key research question is how to tackle the complex issues related to building learning systems that care, ranging from representing knowledge and context to modelling social, cognitive, metacognitive and affective dimensions.This requires multidisciplinary research that links theory and technology from artificial intelligence, cognitive science and computer science with theory and practice from education and the social sciences.

Providing a child-appropriate interface to an arithmetic package with arbitrarily large numbers and exact fractions is surprisingly challenging. We discuss solutions to problems ranging from how to present fractions such as 1/3 to how to deal with numbers with tens of thousands of digits to how to deal with irrational numbers. As with other objects in ToonTalk ® , we strive to make the enhanced numbers work in a concrete and playful manner.