An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All (original) (raw)
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Computing and AI for a Sustainable Future
IEEE Intelligent Systems, 2011
When preparing for a March 2007 talk at the US National Science Foundation (NSF), I searched the Web for scholarly work on AI and climate change, the natural environment, and sustainability. My search was not exhaustive, largely based on keywords, but it wasn't trivial either. Still, little turned up in the intersection of AI and sustainability in early 2007, and most of what did, as I recall, was in environmental science publications and appeared to be dominated by European researchers using evolutionary computation for the purposes of optimization. 1 AI and sustainability has grown substantially in the last few years. To some extent, this tracks with increasing interest in sustainability and computing more generally. However, AI is helping to drive this larger movement, rather than simply riding along. Indeed, it's hard to imagine that AI would not be central to understanding and managing the great complexity of maintaining a healthy planet in the face of pervasive and transformative human activity. A visible and scientifically significant landmark in this growth of AI and sustainability is the establishment of the Computational Sustainability Institute, 2 with its focus on AI and many sustainability areas, such as biodiversity and alternative energy. The institute grew from a 2008 Expedition in Computing Award from the NSF to Cornell University, Oregon State University, Bowdoin College, Howard University, and other partners, quickly attracting other researchers, educators, government, and industry. The first conference on computational sustainability took place in 2009, followed by a second in 2010 and leading in 2011 to a special track on Computational Sustainability at the Association for the Advancement of Artificial Intelligence (AAAI) conference. Coinciding with the institute's founding was a groundswell of activity to include sustainability tracks at other AI-related conferences. Machine learning and data mining have been strong among these, and in 2010, a second sustainability-focused Expedition in Computing award was given to the University of Minnesota and its partners for data-driven understanding of climate change and related phenomena. Forthcoming articles in this new IEEE Intelligent Systems AI and Sustainability Department will elaborate on AI's deployment in many areas of sustainability as well as the challenges and opportunities that sustainability issues bring to AI research, education, and practice. This opening article will touch upon the main themes at the intersection of AI and sustainability, but it will primarily concentrate on the larger contexts of sustainability, and on computing and sustainability, thereby setting the stage for articles to come. Sustainability The United Nations' Bruntland report contains a popular and succinct definition of sustainability: "Sustainable development is development that meets the needs of the present
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RAYHANS, 2023
This paper explores the role of artificial intelligence (AI) in sustainable development, investigating the opportunities it presents and the challenges that need to be addressed. The urgent need for sustainable development to address global challenges such as climate change, resource depletion, and social inequality is juxtaposed with the rapid advancement of AI technologies. AI has the potential to revolutionize various sectors and provide innovative solutions. The paper examines the intersection of AI and sustainable development, analyzing its potential to address sustainable development goals (SDGs) and presenting case studies of successful AI applications. It also delves into the key challenges and risks associated with AI in sustainable development, including ethical considerations, data privacy concerns, and socio-economic implications. The policy and regulatory frameworks for AI in sustainable development are discussed, along with the importance of responsible and ethical AI development. The paper emphasizes the need for collaboration and partnerships among various stakeholders and provides recommendations for policymakers, researchers, and practitioners. The future outlook highlights potential advances and emerging trends in AI for sustainable development, along with strategies to overcome challenges and maximize opportunities. Ultimately, the paper underscores the importance of leveraging AI technologies responsibly to achieve sustainable development goals and create a more sustainable and inclusive future.
Artificial Intelligence for Sustainable Development: Synthesis Report, Mobile Learning Week 2019
2019
The designations employed and the presentation of material throughout this publication do not imply the expression of any opinion whatsoever on the part of UNESCO and Right to Education Initiative concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The ideas and opinions expressed in this publication are those of the authors; they are not necessarily those of UNESCO or Right to Education Initiative and do not commit the Organizations.
Artificial Intelligence: A Double-Edged Sword For The Education And Environment Of The Global Market
2024
This study examines artificial intelligence's revolutionary impact on workplace learning. It highlights how artificial intelligence can enhance learning quality, accuracy, and precision at both the individual and organizational levels. Artificial intelligence reduces negative consequences while increasing favorable outcomes, notably in domains like early childhood education. The study focuses on the two disadvantages of artificial intelligence in the early worldwide market. The significant impact of artificial intelligence is producing profound changes in the education and environmental sectors. This work explores the dual nature of artificial intelligence using case studies from two major fields. Google Environmental Insights for the environmental market and Squirrel AI Learning for education. It also poses problems like unequal access, data privacy issues, and a dependence on technological advancements. Artificial intelligence's specific insights, useful resource management, and scalability are useful to the environmental industry. Nonetheless, troubles with statistical reliability, implementation, and intake of power exist. This paper emphasizes strategies in which artificial intelligence is a dual-edged sword with wonderful and terrible qualities that have a significant influence. It highlights how important it is to be open to opportunities and handle conflict in an equitable manner. The author demonstrates how, with careful legislation, ethical thought, and rigorous technique, artificial intelligence has the potential to substantially enhance environmental sustainability and education. It also has limitations, such as erratic access, difficulties for privateers with statistics, and a reliance on technology developments. The environmental industry may gain from synthetic intelligence's specialized insights, practical resource management, and scalability. The study focuses on how artificial intelligence is a two-edged sword with significant benefits and drawbacks, as well as how it has broad implications.
Frontiers in education, 2024
Introduction: The purpose of this empirical research was to map the capabilities and perceptions of undergraduate business administration students about artificial intelligence (AI) and its potential to answer questions related to sustainable transition in society, and to obtain information about the suitable pedagogical solution to increase the knowledge and understanding related to these themes. Methods: The data was gathered among higher education (HE) students in a workshop that consisted of introductory lecture, answering surveys, questionnaire, group discussions, and reflective narratives on the relationship and possibilities of AI and sustainable development. In data analysis an abductive qualitative research methodology was adopted. Results: Through abduction new insights were obtained and new knowledge was created new knowledge regarding AI literacy in the context of sustainable development. This brought new knowledge in the context of HE studies. The taxonomy of AI literacy in sustainable development created a new reference framework for learning tasks, and course planning in HE. The findings showed that the students had difficulties solving the actual problem because they lacked knowledge and understanding of the basics of AI and sustainable development. However, in groups where one person had a deeper understanding of the concepts, the whole group began to understand the task and work on both meta-level ethical questions and practical examples. Discussion: The assistance of AI potentially creates opportunities for developing solutions supporting sustainable development. However, utilizing this potential requires AI literacy. In this task HE plays a significant role. This study contributes to the pedagogical approach where AI and sustainable development are integrated in HE curricula.
AI in Learning: Designing the Future
AI in Learning: Designing the Future
Artificial intelligence (AI) plays an increasingly prevalent role in the global education system. However, there is a lack of transparency about how AI technologies work alongside certain ethical guidelines. Achieving the global benefits of AI-driven learning and education requires international dialogue on the commonplaces of ethical considerations. This paper, taking China and Finland as two contextual cases, analyzes how AI-related policies at the national level have focused on educational themes and established aims for improving the quality of learning and education. As a result, four themes for AI ethics have emerged: (1) inclusion and personalization, (2) justice and safety, (3) transparency and responsibility, and (4) autonomy and sustainability. Although both China and Finland recognize the importance of AI ethics, the differences are manifested in their policy approaches, properties, and strategies due to sociocultural variation. This chapter emphasizes the need for intern...
Is the future of AI sustainable? A case study of the European Union
Transforming Government: People, Process and Policy
Purpose The purpose of this paper is to raise awareness on the need for a more comprehensive approach on the interdependence between artificial intelligence (AI) and environmental sustainability. It provides an overview of existing sustainable AI policy initiatives at the national and regional level. More precisely, it discusses whether existing European Union (EU) environmental policies are suitable for the AI era or whether new regulations are needed in this field. Finally, this paper assesses cross-fertilisation opportunities between the EU and non-EU countries. Design/methodology/approach This study is based on a qualitative analysis of sustainable applications of AI and the sustainability of AI. Emphasis is laid on the latter, and a “sustainable by design” approach is proposed, which in essence is a prerequisite for transparent, responsible and human-centred AI systems. The analysis primarily focuses on environmental sustainability. Findings The majority of studies focus on how...
Toward Responsible AI Use: Considerations for Sustainability Impact Assessment
arXiv (Cornell University), 2023
As AI/ML models, including Large Language Models, continue to scale with massive datasets, so does their consumption of undeniably limited natural resources, and impact on society. In this collaboration between AI, Sustainability, HCI and legal researchers, we aim to enable a transition to sustainable AI development by enabling stakeholders across the AI value chain to assess and quantitfy the environmental and societal impact of AI. We present the ESG Digital and Green Index (DGI), which offers a dashboard for assessing a company's performance in achieving sustainability targets. This includes monitoring the efficiency and sustainable use of limited natural resources related to AI technologies (water, electricity, etc). It also addresses the societal and governance challenges related to AI. The DGI creates incentives for companies to align their pathway with the Sustainable Development Goals (SDGs). The value, challenges and limitations of our methodology and findings are discussed in the paper.