Computers and Education Artificial Intelligence· 2021Q1
Artificial intelligence in education: The three paradigms
- 835citations
- Q1SCImago
- 2021year
Short summary
Artificial intelligence in education (AIEd) has evolved through three distinct paradigms: AI-directed (learner-as-recipient), AI-supported (learner-as-collaborator), and AI-empowered (learner-as-leader).
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Key points
- AIEd's development is characterized by three paradigms: AI-directed, AI-supported, and AI-empowered.
- In the AI-directed paradigm, AI dictates learning and learners are recipients.
- In the AI-supported paradigm, AI assists learning, with learners acting as collaborators.
- In the AI-empowered paradigm, AI facilitates learning, with learners taking agency and leadership.
- The overall trend in AIEd is towards empowering learner agency, personalization, and learner-centered, data-driven learning.
AI-generated from the title and abstract; the full text is not read.
Abstract
With the development of computing and information processing techniques, artificial intelligence (AI) has been extensively applied in education. Artificial intelligence in education (AIEd) opens new opportunities, potentials, and challenges in educational practices. In its short history, AIEd has been undergoing several paradigmatic shifts, which are characterized into three paradigms in this position paper: AI-directed, learner-as-recipient, AI-supported, learner-as-collaborator, and AI-empowered, learner-as-leader. In three paradigms, AI techniques are used to address educational and learning issues in varied ways. AI is used to represent knowledge models and direct cognitive learning while learners are recipients of AI service in Paradigm One; AI is used to support learning while learners work as collaborators with AI in Paradigm Two; AI is used to empower learning while learners take agency to learn in Paradigm Three. Overall, the development trend of AIEd has been developing to empower learner agency and personalization, enable learners to reflect on learning and inform AI systems to adapt accordingly, and lead to an iterative development of the learner-centered, data-driven, personalized learning.
The authors' abstract, as published at the source. Computers and Education Artificial Intelligence, 2021 · DOI ↗
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Field: Computer Science Applications
Computer Science ApplicationsComputer Science