The Application of a Large Language Model (LLM) in Education Reform and Innovation
Language: English
Published by MDPI AG, 2026
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - This Reprint presents a curated collection of nine articles from the Systems Special Issue 'AI-Augmented Human-Machine Systems: Engineering and Design in Education', launched in response to the profound reshaping of education by large language models and generative AI. Adopting a systems perspective, the contributions examine how AI tools can be engineered to enhance learning outcomes while mitigating risks such as over-reliance, trust erosion, and ethical misalignment. Spanning K-12, vocational, and higher education, the collection employs diverse methodologies including quasi-experiments, surveys, grounded theory, systematic reviews, and neuro-symbolic development.Key contributions include the following: a dual-dimension trust model distinguishing system-like from human-like trust; empirical evidence establishing individual autonomy and moderate constraints as design principles for LLM-integrated project-based learning; ARGUS, a neuro-symbolic architecture generating actionable feedback; and domain-specific fine-tuning with transparent performance reporting. Vocational and K-12 studies, along with a stakeholder-driven privacy framework and a systematic review, further enrich the volume. Together, these articles offer both theoretical depth and actionable guidance, demonstrating that the challenge lies not in whether to adopt AI, but in how, under what principles, and governed by which ethical structures. This Reprint is an essential resource for researchers, educators, and policymakers. …
Seller Inventory # 9783725885596
- Title
- The Application of a Large Language Model (LLM) in Education Reform and Innovation
- Publisher
- MDPI AG
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3725885591
- ISBN 13
- 9783725885596
- Item weight
- 725 grams
- Dimensions
- 250x175x18 mm
This Reprint presents a curated collection of nine articles from the Systems Special Issue "AI-Augmented Human-Machine Systems: Engineering and Design in Education", launched in response to the profound reshaping of education by large language models and generative AI. Adopting a systems perspective, the contributions examine how AI tools can be engineered to enhance learning outcomes while mitigating risks such as over-reliance, trust erosion, and ethical misalignment. Spanning K-12, vocational, and higher education, the collection employs diverse methodologies including quasi-experiments, surveys, grounded theory, systematic reviews, and neuro-symbolic development.
Key contributions include the following: a dual-dimension trust model distinguishing system-like from human-like trust; empirical evidence establishing individual autonomy and moderate constraints as design principles for LLM-integrated project-based learning; ARGUS, a neuro-symbolic architecture generating actionable feedback; and domain-specific fine-tuning with transparent performance reporting. Vocational and K-12 studies, along with a stakeholder-driven privacy framework and a systematic review, further enrich the volume. Together, these articles offer both theoretical depth and actionable guidance, demonstrating that the challenge lies not in whether to adopt AI, but in how, under what principles, and governed by which ethical structures. This Reprint is an essential resource for researchers, educators, and policymakers.
"Synopsis" may belong to another edition of this title.
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