From Algorithm Worship to the Art of Human Learning: Insights from 50-year journey of AI in Education

• URL: http://arxiv.org/abs/2403.05544v1
• Date: Mon, 5 Feb 2024 16:12:14 GMT
• Title: From Algorithm Worship to the Art of Human Learning: Insights from 50-year journey of AI in Education
• Authors: Kaska Porayska-Pomsta,
• Abstract summary: Current discourse surrounding Artificial Intelligence (AI) oscillates between hope and apprehension. This paper delves into the complexities of AI's role in Education, addressing the mixed messages that have both enthused and alarmed educators. It explores the promises that AI holds for enhancing learning through personalisation at scale, against the backdrop of concerns about ethical implications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current discourse surrounding Artificial Intelligence (AI) oscillates between hope and apprehension, painting a future where AI reshapes every facet of human life, including Education. This paper delves into the complexities of AI's role in Education, addressing the mixed messages that have both enthused and alarmed educators, policymakers, and the public. It explores the promises that AI holds for enhancing learning through personalisation at scale, against the backdrop of concerns about ethical implications, the devaluation of non-STEM subjects, and the potential transformative impact on our neurocognitive and socio-emotional functioning. Drawing on recent research and global discourse, the paper seeks to unpack the reasons behind the vagueness of current discussions on AI in Education (AIED) and the implications of this ambiguity for future educational practices and policies. By highlighting insights from educational research and synthesising evidence-based best practices in AIED, the aim is to provide a clearer understanding of how AI technologies can be aligned with the fundamental principles of learning and teaching, and explore what concrete actions may need to be prioritised now to truly enhance learning experiences and outcomes for all in the future.

Related papers

• Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions [101.67121669727354]
  Recent advancements in AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment. The lack of clarified definitions and scopes of human-AI alignment poses a significant obstacle, hampering collaborative efforts across research domains to achieve this alignment.
  arXiv  Detail & Related papers  (2024-06-13T16:03:25Z)
• Generative AI: The power of the new education [0.0]
  This study proposes an accelerated learning methodology in artificial intelligence, focused on its generative capacity, as a way to achieve this goal. Students' perceptions of generative AI are examined, addressing their emotions towards its evolution, evaluation of its ethical implications, and everyday use of AI tools. The study aims to provide educators with a deeper understanding of students' perceptions of AI and its relevance in society and in their future career paths.
  arXiv  Detail & Related papers  (2024-05-22T09:56:05Z)
• Now, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice [63.20307830884542]
  Next several decades may well be a turning point for humanity, comparable to the industrial revolution. Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts. We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.
  arXiv  Detail & Related papers  (2024-04-06T22:18:31Z)
• The Interplay of Learning, Analytics, and Artificial Intelligence in Education: A Vision for Hybrid Intelligence [0.45207442500313766]
  I challenge the prevalent narrow conceptualisation of AI as tools, and argue for the importance of alternative conceptualisations of AI. I highlight the differences between human intelligence and artificial information processing, and posit that AI can also serve as an instrument for understanding human learning. The paper presents three unique conceptualisations of AI: the externalization of human cognition, the internalization of AI models to influence human mental models, and the extension of human cognition via tightly coupled human-AI hybrid intelligence systems.
  arXiv  Detail & Related papers  (2024-03-24T10:07:46Z)
• Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
  We shed light on the intersectional studies of generative AI and adaptive learning. We argue that this union will contribute significantly to the development of the next-stage learning format in education.
  arXiv  Detail & Related papers  (2024-02-02T23:54:51Z)
• Generative AI and Its Educational Implications [0.0]
  We discuss the implications of generative AI on education across four critical sections. We propose ways in which generative AI can transform the educational landscape. Acknowledging the societal impact, we emphasize the need for updating curricula.
  arXiv  Detail & Related papers  (2023-12-26T21:29:31Z)
• Multimodality of AI for Education: Towards Artificial General Intelligence [14.121655991753483]
  multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intelligence (AGI) in educational contexts. This research delves deeply into the key facets of AGI, including cognitive frameworks, advanced knowledge representation, adaptive learning mechanisms, and the integration of diverse multimodal data sources. The paper also discusses the implications of multimodal AI's role in education, offering insights into future directions and challenges in AGI development.
  arXiv  Detail & Related papers  (2023-12-10T23:32:55Z)
• New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution [2.94944680995069]
  ChatGPT's high performance on standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education. This research aims to investigate the potential impact of AI on education through review and analysis of the existing literature across three major axes: applications, advantages, and challenges.
  arXiv  Detail & Related papers  (2023-05-12T08:22:54Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.