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Detail
Journal Article
인공지능 기반 학습자 맞춤형 교육을 위한 형평성과 편향성 연구
- Authors
- 방준성, 이상민
- Issue Date
- 2024-12
- Citation
- 멀티미디어 언어교육, v.27, no.4, pp.70-86
- ISSN
- 1229-8107
- Publisher
- 한국멀티미디어언어교육학회
- Language
- Korean
- Type
- Journal Article
- Abstract
- The integration of AI into education has catalyzed transformative changes, particularly in enabling customized and personalized learning experiences that were previously impractical in traditional classroom settings. While earlier AI automation systems focused on educational equality by providing uniform resources and opportunities, more recent AIdriven customized learning systems aim to achieve educational equity by providing differentiated support tailored to individual needs, ensuring comparable learning outcomes across diverse student populations. However, these AI-driven customized learning systems can inadvertently introduce bias in both data collection and algorithmic processing, potentially compromising educational equity. This risk is particularly pronounced in foreign language education, where learner populations exhibit significant demographic and cultural diversity, increasing the potential for data bias. With the planned introduction of AI English digital textbooks in 2025, addressing AI bias and educational inequality becomes critical, particularly in English education. This study systematically examines the sources of AI bias that occur at multiple stages: data collection, analysis, classification, and algorithmic processing. It identifies the manifestations and implications of these biases in educational contexts, and proposes comprehensive solutions that include both technical and non-technical approaches, drawing on existing literature. It also outlines recommendations for key stakeholders -developers, educators, and policymakers- to ensure equity in AI-driven educational systems.
- KSP Keywords
- Automation System, Cultural diversity, Customized learning, Data Collection, Digital textbook, English education, Foreign Language, Learning System, Learning experience, Learning outcomes, Non-technical
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC)
(CC BY NC)
