In this paper we explore integrating generative AI methods into educational frameworks, and its potential to increase accessibility, the point of view, to foster} critical thinking, and to force re-evaluation by both educator and learner educator and learner alike. An extensive review of the research trends, key players and thematic areas suggest that machine learning, data science, and applied computing are the key drivers behind generative AI in education. To help promote responsible use, we discuss some ethical questions like fairness, transparency, and bias reduction. By matching technological innovations with educational goals, this study provides tangible suggestions for curriculum design, faculty development, strategic alliance development, assisting educational practice to be transformed by AI in the current academic environment.

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Generative AI in Higher Education: Bridging Research, Ethics, and Curriculum Innovation

  • Susana A. Arias,
  • Robert Vaca Alban,
  • Luis Arias Villaroel,
  • Janio Jadán-Guerrero

摘要

In this paper we explore integrating generative AI methods into educational frameworks, and its potential to increase accessibility, the point of view, to foster} critical thinking, and to force re-evaluation by both educator and learner educator and learner alike. An extensive review of the research trends, key players and thematic areas suggest that machine learning, data science, and applied computing are the key drivers behind generative AI in education. To help promote responsible use, we discuss some ethical questions like fairness, transparency, and bias reduction. By matching technological innovations with educational goals, this study provides tangible suggestions for curriculum design, faculty development, strategic alliance development, assisting educational practice to be transformed by AI in the current academic environment.