Charting the futures of artificial intelligence in education

  • Meaning Processing
  • University College London / UNESCO / IRCAI / Council of Europe
  • Ecole des Ponts Business School; University of New Brunswick; University of Stavanger
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The study seeks to understand how the AI ecosystem might be implicated in a form of knowledge production which reifies particular kinds of epistemologies over others. Using text mining and thematic analysis, this paper offers a horizon scan of the key themes that have emerged over the past few years during the AIEd debate. We begin with a discussion of the tools we used to experiment with digital methods for data collection and analysis. This paper then examines how AI in education systems are being conceived, hyped, and potentially deployed into global education contexts. Findings are categorised into three themes in the discourse: (1) geopolitical dominance through education and technological innovation; (2) creation and expansion of market niches, and (3) managing narratives, perceptions, and norms.
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Artificial intelligence has led to a generation of technologies in education – for use in classrooms and by school systems more broadly – with considerable potential to bring education forward. This chapter provides a broad overview of the technologies currently being used, their core applications, and their potential going forward. The chapter also provides definitions of some of the key terms that will be used throughout this book. It concludes with a discussion of the potentials that may be achieved if these technologies are integrated, the shifts in thinking about supporting learners through one-on-one learning experiences to influencing systems more broadly, and other key directions for R&D and policy in the future.
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Artificial Intelligence (AI) will radically change our lives and transform our societies. This shift, which has already started, will most probably be the deepest and the fastest humanity has ever experienced. While most of the ongoing discussions on AI limit themselves to the short and medium-term effects, this short and comprehensive report tries to go beyond the most immediate challenges and to explore also some of the longer-term impacts that AI may have on humans and societies. It summarizes the key issues in 10 takeaways and suggests a list of possible actions to be taken by policymakers.
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Artificial intelligence (AI) is envisioned as a new tool to accelerate the progress towards the achievement of SDG 4. Policies and strategies for using AI in education are central to maximizing AI’s benefits and mitigating its potential risks. Fostering AI-ready policy-makers is the starting point of the policy development process. This publication offers guidance to policy-makers in understanding AI and responding to the challenges and opportunities in education presented by AI. Specifically, it introduces the essentials of AI such as its definition, techniques, technologies, capacities and limitations. It also delineates the emerging practices and benefit-risk assessment on leveraging AI to enhance education and learning, and to ensure inclusion and equity, as well as the reciprocal role of education in preparing humans to live and work with AI. The publication summarizes three approaches to the policy responses from existing practices: independent approach, integrated approach and thematic approach. In a further step, it proposes more detailed recommendations and examples for planning AI and education policies, aligned with the recommendations made in the 2019 Beijing Consensus on AI and Education.
Technical Report
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Will today’s emerging technologies impact the teaching profession in the future? Which parts of the teaching tasks or learning processes could be substituted, enhanced and transformed through automatisation, algorithms and machines? To help educational stakeholders with strategic reflection and anticipatory thinking, eight future-oriented scenarios are outlined using foresight methods. The aim of the scenarios is to see the future as something to shape. These near-future scenarios aim to solve a number of problems that educators of today say prevent them from delivering quality education and training. They take place in classrooms, lecture halls, training centres and digital learning environments in which emerging technologies could be used to support educators in their profession. Key challenges emerging from the scenarios relate to ethical considerations (e.g. balance between human autonomy and machines, datafication of education, pedagogical models) and the evolving competence requirements of teaching professionals. At the end of the report, a number of insights for policy reflection are raised. They aim to prompt the need today to discuss the future role of emerging technologies in education and training, and their impact on the teaching profession
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Digital platforms have become central to interaction and participation in contemporary societies. New forms of ‘platformized education’ are rapidly proliferating across education systems, bringing logics of datafication, automation, surveillance, and interoperability into digitally mediated pedagogies. This article presents a conceptual framework and an original analysis of Google Classroom as an infrastructure for pedagogy. Its aim is to establish how Google configures new forms of pedagogic participation according to platform logics, concentrating on the cross-platform interoperability made possible by application programming interfaces (APIs). The analysis focuses on three components of the Google Classroom infrastructure and its configuration of pedagogic dynamics: Google as platform proprietor, setting the ‘rules’ of participation; the API which permits third-party integrations and data interoperability, thereby introducing automation and surveillance into pedagogic practices; and the emergence of new ‘divisions of labour’, as the working practices of school system administrators, teachers and guardians are shaped by the integrated infrastructure, while automated AI processes undertake the ‘reverse pedagogy’ of learning insights from the extraction of digital data. The article concludes with critical legal and practical ramifications of platform operators such as Google participating in education.
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Like previous educational technologies, artificial intelligence in education (AIEd) threatens to disrupt the status quo, with proponents highlighting the potential for efficiency and democratization, and skeptics warning of industrialization and alienation. However, unlike frequently discussed applications of AI in autonomous vehicles, military and cybersecurity concerns, and healthcare, AI’s impacts on education policy and practice have not yet captured the public’s attention. This paper, therefore, evaluates the status of AIEd, with special attention to intelligent tutoring systems and anthropomorphized artificial educational agents. I discuss AIEd’s purported capacities, including the abilities to simulate teachers, provide robust student differentiation, and even foster socio-emotional engagement. Next, to situate developmental pathways for AIEd going forward, I contrast sociotechnical possibilities and risks through two idealized futures. Finally, I consider a recent proposal to use peer review as a gatekeeping strategy to prevent harmful research. This proposal serves as a jumping off point for recommendations to AIEd stakeholders towards improving their engagement with socially responsible research and implementation of AI in educational systems.
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Artificial intelligence (AI) is arguably the driving technological force of the first half of this century, and will transform virtually every industry, if not human endeavors at large. Businesses and governments worldwide are pouring enormous sums of money into a very wide array of implementations, and dozens of start-ups are being funded to the tune of billions of dollars. It would be naive to think that AI will not have an impact on education—au contraire, the possibilities there are profound yet, for the time being, overhyped as well. This book attempts to provide the right balance between reality and hype, between true potential and wild extrapolations.
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In knowledge management literature it is often pointed out that it is important to distinguish between data, information and knowledge. The generally accepted view sees data as simple facts that become information as data is combined into meaningful structures, which subsequently become knowledge as meaningful information is put into a context and when it can be used to make predictions. This view sees data as a prerequisite for information, and information as a prerequisite for knowledge. In this paper, I will explore the conceptual hierarchy of data, information and knowledge, showing that data emerges only after we have information, and that information emerges only after we already have knowledge. The reversed hierarchy of knowledge is shown to lead to a different approach in developing information systems that support knowledge management and organizational memory. It is also argued that this difference may have major implications for organizational flexibility and renewal.
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This article focuses on contributions that AI can make to address longterm educational goals. Challenges are described that support: (1) mentors for every learner; (2) learning 21st century skills; (3) interaction data for learning; (4) universal access to global classrooms; and (5) lifelong and lifewide learning. A vision and brief research agenda are described for each challenge along with goals that lead to development of global educational resources and the reuse and sharing of digital educational resources. Instructional systems with AI technology are described that currently support richer experiences for learners and supply researchers with new opportunities to analyze vast data sets of instructional behavior from big databases that record elements of learning, affect, motivation, and social interaction. Personalized learning is described that facilitates student and group experience, reflection, and assessment. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.
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Dedication to Martial Vivet The first author of this paper had the good fortune to interact with Martial Vivet over many years; in particular with Martial and his colleagues and students in Le Mans for five weeks during the Spring of 1999. His passing has left us without an important voice in the AI & Education community. In addition to his many professional contributions he was an inspiration to many students and colleagues. His warm personality, his adherence to rigorous scientific standards and his concern for the people with whom he interacted will always be a beacon for us to follow. He was concerned about ethics and the impact, both for good and potential harm, that AI research could have on education. It is in his memory and with his concern for students that we would like to dedicate this paper. Abstract: This paper explores the human and ethical issues implicit in the use of AI in education. Our intention is to begin a discussion that will lead to a deeper understanding of the issues and eventually to a consensus within the research community concerning what is desirable and what is not in the use of AI in education.
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What stories do we tell about the future? This article develops a topology of storytelling about the future, which is used to develop a definition of ‘futures literacy’. It goes on to outline a hybrid strategic scenario method for acquiring the capacities of futures literacy.
Full paper available, open access, The concept of chronotope was introduced in the 1920s by the Russian neurophysiologist A.A. Ukhtomsky, and extensively used by Mikhail Bakhtin in his analysis of the development of literary forms. Chronotope structures the possibilities for meaningful action and different chronotopes thus generate different forms of agency and future. In this paper, three approaches to foresight are analyzed, showing how their different chronotopes lead to different ways of understanding the future. We differentiate between probabilistic, possibilistic and constructivist frameworks for foresight. Probabilistic approaches are shown to rely on recursive chronotopes that capture future as a repetition of the past. Possibilistic approaches, here exemplified by the “gold standard” Schwartz/GBN scenario method, are shown to rely on narrative chronotopes that can tell stories of emergent futures and the impact of innovation. Scenario methods, however, describe changes in the environment as forces and trends in a recursive chronotope. As a result, they have limited capacity to address qualitative novelty. In contrast to possibilistic and probabilistic approaches, a constructivist dialogical approach described in this paper explicitly aims at integrating qualitative novelty and radical innovation as important elements of foresight. Constructivist foresight does not aim for “knowing” the future; instead, it aims at creating the future. Knowing when to use these different forms of foresight is an important element in strategy and policy development.
This book presents the theory of anticipation, and establishes anticipation of the future as a legitimate topic of research. It examines anticipatory behavior, i.e. a behavior that ‘uses’ the future in its actual decisional process. The book shows that anticipation violates neither the ontological order of time nor causation. It explores the question of how different kinds of systems anticipate, and examines the risks and uses of such anticipatory practices. The book first summarizes the research on anticipation conducted within a range of different disciplines, and describes the connection between the anticipatory point of view and futures studies. Following that, its chapters on Wholes, Time and Emergence, make explicit the ontological framework within which anticipation finds its place. It then goes on to discuss Systems, Complexity, and the Modeling Relation, and provides the scientific background supporting anticipation. It restricts formal technicalities to one chapter, and presents those technicalities twice, in formal and plain words to advance understanding. The final chapter shows that all the threads presented in the previous chapters naturally converge toward what has come to be called “Discipline of Anticipation”
This article explores organisational anticipation in uncertain times. ‘Anticipation’ is interpreted as a mediating process between knowledge and action, where ‘feed-forward’ is causal. The context for examining organisational anticipation is one of ontological insecurity; raising issues of epistemological and therefore methodological uncertainty. The paper draws on re-emerging areas of study in the futures literature especially with respect to anticipatory systems and post-normal science. Rosen's 1985 theory of anticipatory systems is not well known, though has received recent attention as part of a growing discourse on Anticipation, for example as a possible discipline and as a form of governance. For Rosen, causality is mediated through a modelling relationship between actor and environment which entails causality, not by the direct effect of the environment on the actor. The paper discusses the implications of this perspective on the role of scenario planning in organisations, which is but one of multiple anticipatory systems at work in the organisation and hence often weak in power. The argument is further developed by considering ‘modelling relations’ which are inherent to active anticipatory systems. The conclusion is that in human social systems in uncertain environments require approaches to anticipation that recognise the multiplicity of modelling relations. One approach to this has been set out in earlier work by Funtowicz and Ravetz (1993), which they called post-normal science. The paper concludes by suggesting that the epistemology of anticipatory systems and methodology developed from PNS might be used to reduce Cartesian anxiety with respect to ontological insecurities of uncertain times. This has radical implications for scenario planning as it is currently conceived.
This paper attempts an analysis of some current trends and future developments in computer science, education, and educational technology. Based on these trends, two possible future predictions of AIED are presented in the form of a utopian vision and a dystopian vision. A comparison of these two visions leads to seven challenges that AIED might have to face in the future: intercultural and global dimensions, practical impact, privacy, interaction methods, collaboration at scale, effectiveness in multiple domains, and the role of AIED in educational technology. The paper discusses these challenges and the associated risks and opportunities.
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