Artificial intelligence in education: The three paradigms

By Ouyang Fan, Pengcheng Jiao. Computers and Education: Artificial Intelligence, 2021

788 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/3799a5f00b37cf0792d4d4a1ce6259ece89985d7

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Citations in the corpus

Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning (2023, background): “With its ability to process vast amounts of data, analyze complex patterns, and deliver personalized insights, AI offers new possibilities for enhancing educational practices and student outcomes (e.g., Roll and Wylie, 2016; Ouyang and Jiao, 2021).”
Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world (2023, background): “AI technologies have the potential for supporting learner-centered pedagogical strategies, classroom differentiation and personalized learning to engage students in their learning process (Ouyang & Jiao, 2021).”
Learning design to support student-AI collaboration: perspectives of leading teachers for AI in education (2022, background): “…use of AI in students’ learning from simply solving a problem for students or providing them the right answer (Skinner, 1958; Afzal et al., 2019) to engaging them in problem-solving, providing a variety of problem-solving experiences to propel them into new ways of thinking (Ouyang & Jiao, 2021).”
Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course (2023, methodology): “AIEd transforms the instructor and student roles and agencies, offers individualized, personalized, student-centered learning experiences, and fosters knowledge connections, creation and knowledge network building (Ouyang & Jiao, 2021).”
Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020 (2022, methodology): “With the innovation and advancement of AI technologies and techniques, the applications of AI promote the transformation of higher education from traditional, instructor-directed lecturing to AI-enabled, student-centered learning (Chen et al., 2020a; Ouyang & Jiao, 2021).”
Learning and development (2013, neutral): “It is probably worth agreeing with researchers who consider AI capabilities insufficiently integrated with modern educational platforms: usually, teachers use artificial intelligence autonomously, without integration with other educational technologies (Hwang et al., 2020; Ouyang & Jiao, 2021).”
The promise and challenges of generative AI in education (2024, neutral): “Nowadays, in the AIED literature, one can find different techniques, such as natural language processing (NLP), neural networks (NNs), machine learning (ML), deep learning, and genetic algorithms (Ouyang and Jiao 2021).”