This article explores the evolution of constructionism as an educational framework, tracing its relevance and transformation across three pivotal eras: the advent of personal computing, the networked society, and the current era of generative AI. Rooted in Seymour Papert constructionist philosophy, this study examines how constructionist principles align with the expanding role of digital technology in personal and collective learning. We discuss the transformation of educational environments from hierarchical instructionism to constructionist models that emphasize learner autonomy and interactive, creative engagement. Central to this analysis is the concept of an expanded personality, wherein digital tools and AI integration fundamentally reshape individual self-perception and social interactions. By integrating constructionism into the paradigm of smart education, we propose it as a foundational approach to personalized and democratized learning. Our findings underscore constructionism enduring relevance in navigating the complexities of technology-driven education, providing insights for educators and policymakers seeking to harness digital innovations to foster adaptive, student-centered learning experiences.

Over the past decade, there have been increasing proclama5ons from diverse stakeholders that humanity is at an inflec5on point due to advances in Ar5ficial Intelligence (AI) technologies (e.g., Crawford, 2017). The general public are condi5oned by this messaging to expect both big (though so far largely non-descript) changes to our lives, including to the way that we learn and teach. Warnings have been also ar5culated regarding whether and how AI might fundamentally change the way we perceive reality, how we form our beliefs, or interact with one another (Bostrom, 2017). More recently, ques5ons started to emerge about AI's transforma5ve poten5al (for beLer or worse) for our func5oning at neurocogni5ve, socio-emo5onal, individual and collec5ve levels (UNESCO, 2022; Pedro, et al., 2019, Porayska-Pomsta, 2023), along with concerns regarding the ethical implica5ons of using AI for suppor5ng human decision-making in contexts that are both high-stakes (e.g., for medical diagnoses or for student assessment) and rela5vely low-stakes, e.g., selec5ng movies on streaming sites. Such hope-fear rhetoric is also present in the context of AI applica5ons to suppor5ng human learning in formal and informal contexts. Recent hopes for AI in educa5on (AIED) largely relate to delivering learning at scale across different geographical and cultural contexts, especially in light of growing global teacher shortages and diminishing funding for educa5on in many countries (UNESCO, 2023). These hopes are increasingly used to fuel poli5cally and market mo5vated discourse about the need to'release teachers from tedious tasks' such as standardised assessments to allow them to focus on the'things that maLer' (Gen5le et al., 2023), or to jus5fy the narrowing of the formal educa5on curricula mainly to STEM subjects.

Perceptrons, Reissue of the 1988 Expanded Edition with a new foreword by Léon Bottou: An Introduction to Computational Geometry (The MIT Press) [Minsky, Marvin, Papert, Seymour A., Bottou, Leon] on Amazon.com. *FREE* shipping on qualifying offers. Perceptrons, Reissue of the 1988 Expanded Edition with a new foreword by Léon Bottou: An Introduction to Computational Geometry (The MIT Press)

The last decade has seen remarkable improvements in the ability of computers to understand the world around them. Photo software automatically recognizes people's faces. Smartphones transcribe spoken words into text. Self-driving cars recognize objects on the road and avoid hitting them. Underlying these breakthroughs is an artificial intelligence technique called deep learning.

Like a lot of working parents, when I'm walking my daughters to school or listening to them recount their days at the dinner table, one question is often on my mind: What should I be doing to prepare them for the world they'll enter as adults? When my daughters and their peers enter the workforce in 10 years, the global economy will be even more competitive, automated and technology-driven than it is today. Computing will be faster and cheaper. Artificial intelligence will be even more powerful, complemented by sensors everywhere in our environments--making it impossible to distinguish between "online" and "offline." Our greatest challenges, from climate change to economic inequality to privacy, will be even more acute.

Dormehl starts with the 1964 World's Fair -- held only miles from where I lived as a high school student in Queens -- evoking the anticipation of a nation working on sending a man to the moon. He identifies the early examples of artificial intelligence that captured my own excitement at the time, like IBM's demonstrations of automated handwriting recognition and language translation. He writes as if he had been there. Dormehl describes the early bifurcation of the field into the Symbolic and Connectionist schools, and he captures key points that many historians miss, such as the uncanny confidence of Frank Rosenblatt, the Cornell professor who pioneered the first popular neural network (he called them "perceptrons"). I visited Rosenblatt in 1962 when I was 14, and he was indeed making fantastic claims for this technology, saying it would eventually perform a very wide range of tasks at human levels, including speech recognition, translation and even language comprehension. As Dormehl recounts, these claims were ridiculed at the time, and indeed the machine Rosenblatt showed me in 1962 couldn't perform any of these things.

Stephen Grossberg The expanded edition of Perceptrons (MIT Press, Cambridge, Mass, 1988, 292 pp, $12.50) by Marvin L. Minsky and Seymour A. Papert comes at a time of unprecedented interest in the biological and technological modeling of neural networks. The one-year-old International Neural Network Society (INNS) already has over 3500 members from 38 countries and 49 U.S. states, with members joining at the rate of more than 200 per month. The American Association for Artificial Intelligence was, in fact, a cooperating society at the INNS First Annual Meeting in Boston on 6-10 September 1988. Hardly a week goes by in which a scientific meeting or special journal issue does not feature recent neural network research. Thus, substantive technical reviews or informed general assessments of the broad sweep of neural network research are most welcome to help interested scientists find their way into this rapidly evolving technology.

Stephen Grossberg The expanded edition of Perceptrons (MIT Press, Cambridge, Mass, 1988, 292 pp, $12.50) by Marvin L. Minsky and Seymour A. Papert comes at a time of unprecedented interest in the biological and technological modeling of neural networks. The one-year-old International Neural Network Society (INNS) already has over 3500 members from 38 countries and 49 U.S. states, with members joining at the rate of more than 200 per month. The American Association for Artificial Intelligence was, in fact, a cooperating society at the INNS First Annual Meeting in Boston on 6-10 September 1988. Hardly a week goes by in which a scientific meeting or special journal issue does not feature recent neural network research. Thus, substantive technical reviews or informed general assessments of the broad sweep of neural network research are most welcome to help interested scientists find their way into this rapidly evolving technology.

Stephen Grossberg The expanded edition of Perceptrons (MIT Press, Cambridge, Mass, 1988, 292 pp, $12.50) by Marvin L. Minsky and Seymour A. Papert comes at a time of unprecedented interest in the biological and technological modeling of neural networks. The one-year-old International Neural Network Society (INNS) already has over 3500 members from 38 countries and 49 U.S. states, with members joining at the rate of more than 200 per month. The American Association for Artificial Intelligence was, in fact, a cooperating society at the INNS First Annual Meeting in Boston on 6-10 September 1988. Hardly a week goes by in which a scientific meeting or special journal issue does not feature recent neural network research. Thus, substantive technical reviews or informed general assessments of the broad sweep of neural network research are most welcome to help interested scientists find their way into this rapidly evolving technology.

Stephen Grossberg The expanded edition of Perceptrons (MIT Press, Cambridge, Mass, 1988, 292 pp, $12.50) by Marvin L. Minsky and Seymour A. Papert comes at a time of unprecedented interest in the biological and technological modeling of neural networks. The one-year-old International Neural Network Society (INNS) already has over 3500 members from 38 countries and 49 U.S. states, with members joining at the rate of more than 200 per month. The American Association for Artificial Intelligence was, in fact, a cooperating society at the INNS First Annual Meeting in Boston on 6-10 September 1988. Hardly a week goes by in which a scientific meeting or special journal issue does not feature recent neural network research. Thus, substantive technical reviews or informed general assessments of the broad sweep of neural network research are most welcome to help interested scientists find their way into this rapidly evolving technology.