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Synopsis
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.
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The Application of a Large Language Model (LLM) in Education Reform and Innovation (Hardcover)
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Hardcover. Etat : new. Hardcover. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783725885596
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Hardcover. Etat : new. Hardcover. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9783725885596
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Hardcover. Etat : new. Hardcover. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9783725885596
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Buch. Etat : Neu. 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. N° de réf. du vendeur 9783725885596
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