Generative AI and Digital Neocolonialism in Global Education: Towards an Equitable Framework

• URL: http://arxiv.org/abs/2406.02966v3
• Date: Sun, 16 Jun 2024 02:57:15 GMT
• Title: Generative AI and Digital Neocolonialism in Global Education: Towards an Equitable Framework
• Authors: Matthew Nyaaba, Alyson Wright, Gyu Lim Choi,
• Abstract summary: This paper critically discusses how generative artificial intelligence (GenAI) might impose Western ideologies on non-Western societies. It suggests strategies for local and global stakeholders to mitigate these effects.
• Score: 0.5586073503694489
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper critically discusses how generative artificial intelligence (GenAI) might impose Western ideologies on non-Western societies, perpetuating digital neocolonialism in education through its inherent biases. It further suggests strategies for local and global stakeholders to mitigate these effects. Our discussions demonstrated that GenAI can foster cultural imperialism by generating content that primarily incorporates cultural references and examples relevant to Western students, thereby alienating students from non-Western backgrounds. Also, the predominant use of Western languages by GenAI can marginalize non-dominant languages, making educational content less accessible to speakers of indigenous languages and potentially impacting their ability to learn in their first language. Additionally, GenAI often generates content and curricula that reflect the perspectives of technologically dominant countries, overshadowing marginalized indigenous knowledge and practices. Moreover, the cost of access to GenAI intensifies educational inequality and the control of GenAI data could lead to commercial exploitation without benefiting local students and their communities. We propose human-centric reforms to prioritize cultural diversity and equity in GenAI development; a liberatory design to empower educators and students to identify and dismantle the oppressive structures within GenAI applications; foresight by design to create an adjustable GenAI system to meet future educational needs; and finally, effective prompting skills to reduce the retrieval of neocolonial outputs.

Related papers

• Model-based Maintenance and Evolution with GenAI: A Look into the Future [47.93555901495955]
  We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of Model-Based Engineering (MBM&E) We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems.
  arXiv  Detail & Related papers  (2024-07-09T23:13:26Z)
• Securing the Future of GenAI: Policy and Technology [50.586585729683776]
  Governments globally are grappling with the challenge of regulating GenAI, balancing innovation against safety. A workshop co-organized by Google, University of Wisconsin, Madison, and Stanford University aimed to bridge this gap between GenAI policy and technology. This paper summarizes the discussions during the workshop which addressed questions, such as: How regulation can be designed without hindering technological progress?
  arXiv  Detail & Related papers  (2024-05-21T20:30:01Z)
• 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)
• Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics Cycle [0.0]
  Generative artificial intelligence (GenAI) holds significant potential for transforming education and enhancing human productivity. This paper delves into the prospective opportunities and challenges GenAI poses for advancing learning analytics (LA) We posit that GenAI can play pivotal roles in analysing unstructured data, generating synthetic learner data, enriching multimodal learner interactions, advancing interactive and explanatory analytics, and facilitating personalisation and adaptive interventions.
  arXiv  Detail & Related papers  (2023-11-30T07:25:34Z)
• Innovating Computer Programming Pedagogy: The AI-Lab Framework for Generative AI Adoption [0.0]
  We introduce "AI-Lab," a framework for guiding students in effectively leveraging GenAI within core programming courses. By identifying and rectifying GenAI's errors, students enrich their learning process. For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
  arXiv  Detail & Related papers  (2023-08-23T17:20:37Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and Millennial Generation teachers? [0.0]
  Gen Z students were generally optimistic about the potential benefits of generative AI (GenAI) Gen X and Gen Y teachers expressed heightened concerns about overreliance, ethical and pedagogical implications.
  arXiv  Detail & Related papers  (2023-05-04T14:42:06Z)
• Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education [2.0711789781518752]
  This study explores university students' perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. Concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed.
  arXiv  Detail & Related papers  (2023-04-29T15:53:38Z)
• Learning Robust Real-Time Cultural Transmission without Human Data [82.05222093231566]
  We provide a method for generating zero-shot, high recall cultural transmission in artificially intelligent agents. Our agents succeed at real-time cultural transmission from humans in novel contexts without using any pre-collected human data. This paves the way for cultural evolution as an algorithm for developing artificial general intelligence.
  arXiv  Detail & Related papers  (2022-03-01T19:32:27Z)
• Societal Biases in Language Generation: Progress and Challenges [43.06301135908934]
  Language generation presents unique challenges in terms of direct user interaction and the structure of decoding techniques. We present a survey on societal biases in language generation, focusing on how techniques contribute to biases and on progress towards bias analysis and mitigation. Motivated by a lack of studies on biases from decoding techniques, we also conduct experiments to quantify the effects of these techniques.
  arXiv  Detail & Related papers  (2021-05-10T00:17:33Z)

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.