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As technology rapidly evolves, AI tools such as automated scoring and intelligent tutors are revolutionizing how we teach STEM subjects. The book discusses the benefits, challenges, and ethical implications. It's a comprehensive guide that showcases the future of education in an AI-driven world.
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As technology rapidly evolves, AI tools such as automated scoring and intelligent tutors are revolutionizing how we teach STEM subjects. The book discusses the benefits, challenges, and ethical implications. It's a comprehensive guide that showcases the future of education in an AI-driven world.
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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Oxford University Press
- Seitenzahl: 624
- Erscheinungstermin: 1. Januar 2025
- Englisch
- Abmessung: 236mm x 163mm x 37mm
- Gewicht: 1188g
- ISBN-13: 9780198882077
- ISBN-10: 0198882076
- Artikelnr.: 71200524
- Herstellerkennzeichnung
- Produktsicherheitsverantwortliche/r
- Europaallee 1
- 36244 Bad Hersfeld
- gpsr@libri.de
- Verlag: Oxford University Press
- Seitenzahl: 624
- Erscheinungstermin: 1. Januar 2025
- Englisch
- Abmessung: 236mm x 163mm x 37mm
- Gewicht: 1188g
- ISBN-13: 9780198882077
- ISBN-10: 0198882076
- Artikelnr.: 71200524
- Herstellerkennzeichnung
- Produktsicherheitsverantwortliche/r
- Europaallee 1
- 36244 Bad Hersfeld
- gpsr@libri.de
Xiaoming Zhai is an Associate Professor in Science Education & Artificial Intelligence, serving as Director of the AI4STEM Education Center at the University of Georgia. He is interested in applying cutting-edge technologies such as AI to advance science teaching and learning, particularly assessment practices. He is lead investigator on federal-funded projects and his research has been published in top-tier journals. He has collaborated widely with researchers from the USA, Canada, Germany, Norway, China, Ghana, and India, and serves as a global leader in his area of research. Dr. Zhai chaired the NSF-funded 2022 International Conference for AI-based Assessment in STEM and serves as Founding Chair of the National Association of Research in Science Teaching's RAISE (Research in AI-involved Science Education) group. Joseph Krajcik currently serves as Director of the CREATE for STEM Institute at Michigan State University. CREATE for STEM (Collaborative Research for Education, Assessment and Teaching Environments for Science, Technology, Engineering, and Mathematics) is a joint institute between the Colleges of Natural Science and Education that seeks to improve the teaching and learning of science and mathematics from kindergarten to college through innovation and research. During his career, Professor Krajcik has focused on working with science teachers to reform science teaching practices to promote students' engagement in and learning of science through the design, development, and testing of project-based science learning environments.
* Preface
* 1: Xiaoming Zhai and Joseph Krajcik: Introduction: AI-based STEM
Education: Challenges and Opportunities
* AI in STEM Assessment
* 2: James W. Pellegrino: A New Era for STEM Assessment: Considerations
of Assessment, Technology, and Artificial Intelligence
* 3: Ross H. Nehm: AI in Biology Education Assessment: How Automation
Can Drive Educational Transformation
* 4: Marcia C. Linn and Libby Gerard: Assessing and Guiding Student
Science Learning with Pedagogically Informed Natural Language
Processing
* 5: Changzhao Wang, Xiaoming Zhai, and Ji Shen: Applying Machine
Learning to Assess Paper-Pencil Drawn Models of Optics
* 6: Mei-Hung Chiu and Mao-Ren Zeng: Automated Scoring in Chinese
Language for Science Assessments
* 7: Megan Shiroda, Jennifer Doherty, and Kevin C. Haudek: Exploring
Attributes of Successful Machine Learning Assessments for Scoring of
Undergraduate Constructed Response Assessment Items
* 8: Lei Liu, Dante Cisterna, Devon Kinsey, Yi Qi, Kenneth Steimel:
AI-based Diagnosis of Student Reasoning Patterns in NGSS Assessments
* AI Tools for Transforming STEM Learning
* 9: Anna Herdliska and Xiaoming Zhai: Artificial Intelligence-Based
Scientific Inquiry
* 10: Hee-Sun Lee, Gey-Hong Gweon, and Amy Pallant: Supporting
Simulation-mediated Scientific Inquiry through Automated Feedback
* 11: Marcus Kubsch, Adrian Grimm, Knut Neumann, Hendrik Drachsler,
Nikol Rummel: Using Evidence Centered Design to Develop an Automated
System for Tracking Students' Physics Learning in a Digital Learning
Environment
* 12: Janice D. Gobert, Haiying Li, Rachel Dickler, Christine Lott: Can
AI-Based Scaffolding Support Students' Robust Learning of Authentic
Science Practices?
* 13: Ehsan Latif, Xiaoming Zhai, Holly Amerman, Xinyu He: AI-SCORER:
An Artificial Intelligence-Augmented Scoring and Instruction System
* 14: Lei Wang, Cong Wang, Quan Wang, Jiutong Luo, Xijuan Li: Smart
Learning Partner--Chinese Core Competency-oriented Adaptive Learning
System
* AI-based STEM Instruction and Teacher Professional Development
* 15: Lehong Shi, Ikseon Choi: A Systematic Review on Artificial
Intelligence in Supporting Teaching Practice: Application Types,
Pedagogical Roles, and Technological Characteristics
* 16: Peng He, Namsoo Shin, Xiaoming Zhai, Joseph Krajcik: A Design
Framework for Integrating Artificial Intelligence to Support
Teachers' Timely Use of Knowledge-in-Use Assessments
* 17: 1. Abhijit Suresh, William R. Penuel, Jennifer K. Jacobs, Ali
Raza, James H. Martin, Tamara Sumner: Using AI Tools to Provide
Teachers with Fully Automated, Personalized Feedback on Their
Classroom Discourse Patterns
* 18: Lydia Bradford: Use of Machine Learning to Score Teacher
Observations
* 19: David Buschhüter, Marisa Pfläging, Andreas Borowski: Widening the
Focus of Science Assessment via Structural Topic Modeling: An Example
of Nature of Science Assessment
* 20: Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S.
Watson, Scott T. Acton: 1. Classification of Instructional Activities
in Classroom Videos Using Neural Networks
* Ethics, Fairness, and Inclusiveness of AI-based STEM Education
* 21: Sahrish Panjwani-Charania, Xiaoming Zhai: AI for Students with
Learning Disabilities: A Systematic Review
* 22: Selin Akgun, Joseph Krajcik: 1. Artificial Intelligence (AI) as
the Growing Actor in Education: Raising Critical Consciousness
Towards Power and Ethics of AI in K-12 STEM Classrooms
* 23: Wanli Xing, Chenglu Li: Fair Artificial Intelligence to Support
STEM Education: A Hitchhiker's Guide
* 24: Marvin Roski, Anett Hoppe, Andreas Nehring: Supporting Inclusive
Science Learning through Machine Learning: The AIISE Framework
* 25: Xiaoming Zhai and Joseph Krajcik: Pseudo Artificial Intelligence
Bias
* Conclusion
* 26: Xiaoming Zhai: Conclusions and Foresight on AI-based STEM
Education: A New Paradigm
* 1: Xiaoming Zhai and Joseph Krajcik: Introduction: AI-based STEM
Education: Challenges and Opportunities
* AI in STEM Assessment
* 2: James W. Pellegrino: A New Era for STEM Assessment: Considerations
of Assessment, Technology, and Artificial Intelligence
* 3: Ross H. Nehm: AI in Biology Education Assessment: How Automation
Can Drive Educational Transformation
* 4: Marcia C. Linn and Libby Gerard: Assessing and Guiding Student
Science Learning with Pedagogically Informed Natural Language
Processing
* 5: Changzhao Wang, Xiaoming Zhai, and Ji Shen: Applying Machine
Learning to Assess Paper-Pencil Drawn Models of Optics
* 6: Mei-Hung Chiu and Mao-Ren Zeng: Automated Scoring in Chinese
Language for Science Assessments
* 7: Megan Shiroda, Jennifer Doherty, and Kevin C. Haudek: Exploring
Attributes of Successful Machine Learning Assessments for Scoring of
Undergraduate Constructed Response Assessment Items
* 8: Lei Liu, Dante Cisterna, Devon Kinsey, Yi Qi, Kenneth Steimel:
AI-based Diagnosis of Student Reasoning Patterns in NGSS Assessments
* AI Tools for Transforming STEM Learning
* 9: Anna Herdliska and Xiaoming Zhai: Artificial Intelligence-Based
Scientific Inquiry
* 10: Hee-Sun Lee, Gey-Hong Gweon, and Amy Pallant: Supporting
Simulation-mediated Scientific Inquiry through Automated Feedback
* 11: Marcus Kubsch, Adrian Grimm, Knut Neumann, Hendrik Drachsler,
Nikol Rummel: Using Evidence Centered Design to Develop an Automated
System for Tracking Students' Physics Learning in a Digital Learning
Environment
* 12: Janice D. Gobert, Haiying Li, Rachel Dickler, Christine Lott: Can
AI-Based Scaffolding Support Students' Robust Learning of Authentic
Science Practices?
* 13: Ehsan Latif, Xiaoming Zhai, Holly Amerman, Xinyu He: AI-SCORER:
An Artificial Intelligence-Augmented Scoring and Instruction System
* 14: Lei Wang, Cong Wang, Quan Wang, Jiutong Luo, Xijuan Li: Smart
Learning Partner--Chinese Core Competency-oriented Adaptive Learning
System
* AI-based STEM Instruction and Teacher Professional Development
* 15: Lehong Shi, Ikseon Choi: A Systematic Review on Artificial
Intelligence in Supporting Teaching Practice: Application Types,
Pedagogical Roles, and Technological Characteristics
* 16: Peng He, Namsoo Shin, Xiaoming Zhai, Joseph Krajcik: A Design
Framework for Integrating Artificial Intelligence to Support
Teachers' Timely Use of Knowledge-in-Use Assessments
* 17: 1. Abhijit Suresh, William R. Penuel, Jennifer K. Jacobs, Ali
Raza, James H. Martin, Tamara Sumner: Using AI Tools to Provide
Teachers with Fully Automated, Personalized Feedback on Their
Classroom Discourse Patterns
* 18: Lydia Bradford: Use of Machine Learning to Score Teacher
Observations
* 19: David Buschhüter, Marisa Pfläging, Andreas Borowski: Widening the
Focus of Science Assessment via Structural Topic Modeling: An Example
of Nature of Science Assessment
* 20: Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S.
Watson, Scott T. Acton: 1. Classification of Instructional Activities
in Classroom Videos Using Neural Networks
* Ethics, Fairness, and Inclusiveness of AI-based STEM Education
* 21: Sahrish Panjwani-Charania, Xiaoming Zhai: AI for Students with
Learning Disabilities: A Systematic Review
* 22: Selin Akgun, Joseph Krajcik: 1. Artificial Intelligence (AI) as
the Growing Actor in Education: Raising Critical Consciousness
Towards Power and Ethics of AI in K-12 STEM Classrooms
* 23: Wanli Xing, Chenglu Li: Fair Artificial Intelligence to Support
STEM Education: A Hitchhiker's Guide
* 24: Marvin Roski, Anett Hoppe, Andreas Nehring: Supporting Inclusive
Science Learning through Machine Learning: The AIISE Framework
* 25: Xiaoming Zhai and Joseph Krajcik: Pseudo Artificial Intelligence
Bias
* Conclusion
* 26: Xiaoming Zhai: Conclusions and Foresight on AI-based STEM
Education: A New Paradigm
* Preface
* 1: Xiaoming Zhai and Joseph Krajcik: Introduction: AI-based STEM
Education: Challenges and Opportunities
* AI in STEM Assessment
* 2: James W. Pellegrino: A New Era for STEM Assessment: Considerations
of Assessment, Technology, and Artificial Intelligence
* 3: Ross H. Nehm: AI in Biology Education Assessment: How Automation
Can Drive Educational Transformation
* 4: Marcia C. Linn and Libby Gerard: Assessing and Guiding Student
Science Learning with Pedagogically Informed Natural Language
Processing
* 5: Changzhao Wang, Xiaoming Zhai, and Ji Shen: Applying Machine
Learning to Assess Paper-Pencil Drawn Models of Optics
* 6: Mei-Hung Chiu and Mao-Ren Zeng: Automated Scoring in Chinese
Language for Science Assessments
* 7: Megan Shiroda, Jennifer Doherty, and Kevin C. Haudek: Exploring
Attributes of Successful Machine Learning Assessments for Scoring of
Undergraduate Constructed Response Assessment Items
* 8: Lei Liu, Dante Cisterna, Devon Kinsey, Yi Qi, Kenneth Steimel:
AI-based Diagnosis of Student Reasoning Patterns in NGSS Assessments
* AI Tools for Transforming STEM Learning
* 9: Anna Herdliska and Xiaoming Zhai: Artificial Intelligence-Based
Scientific Inquiry
* 10: Hee-Sun Lee, Gey-Hong Gweon, and Amy Pallant: Supporting
Simulation-mediated Scientific Inquiry through Automated Feedback
* 11: Marcus Kubsch, Adrian Grimm, Knut Neumann, Hendrik Drachsler,
Nikol Rummel: Using Evidence Centered Design to Develop an Automated
System for Tracking Students' Physics Learning in a Digital Learning
Environment
* 12: Janice D. Gobert, Haiying Li, Rachel Dickler, Christine Lott: Can
AI-Based Scaffolding Support Students' Robust Learning of Authentic
Science Practices?
* 13: Ehsan Latif, Xiaoming Zhai, Holly Amerman, Xinyu He: AI-SCORER:
An Artificial Intelligence-Augmented Scoring and Instruction System
* 14: Lei Wang, Cong Wang, Quan Wang, Jiutong Luo, Xijuan Li: Smart
Learning Partner--Chinese Core Competency-oriented Adaptive Learning
System
* AI-based STEM Instruction and Teacher Professional Development
* 15: Lehong Shi, Ikseon Choi: A Systematic Review on Artificial
Intelligence in Supporting Teaching Practice: Application Types,
Pedagogical Roles, and Technological Characteristics
* 16: Peng He, Namsoo Shin, Xiaoming Zhai, Joseph Krajcik: A Design
Framework for Integrating Artificial Intelligence to Support
Teachers' Timely Use of Knowledge-in-Use Assessments
* 17: 1. Abhijit Suresh, William R. Penuel, Jennifer K. Jacobs, Ali
Raza, James H. Martin, Tamara Sumner: Using AI Tools to Provide
Teachers with Fully Automated, Personalized Feedback on Their
Classroom Discourse Patterns
* 18: Lydia Bradford: Use of Machine Learning to Score Teacher
Observations
* 19: David Buschhüter, Marisa Pfläging, Andreas Borowski: Widening the
Focus of Science Assessment via Structural Topic Modeling: An Example
of Nature of Science Assessment
* 20: Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S.
Watson, Scott T. Acton: 1. Classification of Instructional Activities
in Classroom Videos Using Neural Networks
* Ethics, Fairness, and Inclusiveness of AI-based STEM Education
* 21: Sahrish Panjwani-Charania, Xiaoming Zhai: AI for Students with
Learning Disabilities: A Systematic Review
* 22: Selin Akgun, Joseph Krajcik: 1. Artificial Intelligence (AI) as
the Growing Actor in Education: Raising Critical Consciousness
Towards Power and Ethics of AI in K-12 STEM Classrooms
* 23: Wanli Xing, Chenglu Li: Fair Artificial Intelligence to Support
STEM Education: A Hitchhiker's Guide
* 24: Marvin Roski, Anett Hoppe, Andreas Nehring: Supporting Inclusive
Science Learning through Machine Learning: The AIISE Framework
* 25: Xiaoming Zhai and Joseph Krajcik: Pseudo Artificial Intelligence
Bias
* Conclusion
* 26: Xiaoming Zhai: Conclusions and Foresight on AI-based STEM
Education: A New Paradigm
* 1: Xiaoming Zhai and Joseph Krajcik: Introduction: AI-based STEM
Education: Challenges and Opportunities
* AI in STEM Assessment
* 2: James W. Pellegrino: A New Era for STEM Assessment: Considerations
of Assessment, Technology, and Artificial Intelligence
* 3: Ross H. Nehm: AI in Biology Education Assessment: How Automation
Can Drive Educational Transformation
* 4: Marcia C. Linn and Libby Gerard: Assessing and Guiding Student
Science Learning with Pedagogically Informed Natural Language
Processing
* 5: Changzhao Wang, Xiaoming Zhai, and Ji Shen: Applying Machine
Learning to Assess Paper-Pencil Drawn Models of Optics
* 6: Mei-Hung Chiu and Mao-Ren Zeng: Automated Scoring in Chinese
Language for Science Assessments
* 7: Megan Shiroda, Jennifer Doherty, and Kevin C. Haudek: Exploring
Attributes of Successful Machine Learning Assessments for Scoring of
Undergraduate Constructed Response Assessment Items
* 8: Lei Liu, Dante Cisterna, Devon Kinsey, Yi Qi, Kenneth Steimel:
AI-based Diagnosis of Student Reasoning Patterns in NGSS Assessments
* AI Tools for Transforming STEM Learning
* 9: Anna Herdliska and Xiaoming Zhai: Artificial Intelligence-Based
Scientific Inquiry
* 10: Hee-Sun Lee, Gey-Hong Gweon, and Amy Pallant: Supporting
Simulation-mediated Scientific Inquiry through Automated Feedback
* 11: Marcus Kubsch, Adrian Grimm, Knut Neumann, Hendrik Drachsler,
Nikol Rummel: Using Evidence Centered Design to Develop an Automated
System for Tracking Students' Physics Learning in a Digital Learning
Environment
* 12: Janice D. Gobert, Haiying Li, Rachel Dickler, Christine Lott: Can
AI-Based Scaffolding Support Students' Robust Learning of Authentic
Science Practices?
* 13: Ehsan Latif, Xiaoming Zhai, Holly Amerman, Xinyu He: AI-SCORER:
An Artificial Intelligence-Augmented Scoring and Instruction System
* 14: Lei Wang, Cong Wang, Quan Wang, Jiutong Luo, Xijuan Li: Smart
Learning Partner--Chinese Core Competency-oriented Adaptive Learning
System
* AI-based STEM Instruction and Teacher Professional Development
* 15: Lehong Shi, Ikseon Choi: A Systematic Review on Artificial
Intelligence in Supporting Teaching Practice: Application Types,
Pedagogical Roles, and Technological Characteristics
* 16: Peng He, Namsoo Shin, Xiaoming Zhai, Joseph Krajcik: A Design
Framework for Integrating Artificial Intelligence to Support
Teachers' Timely Use of Knowledge-in-Use Assessments
* 17: 1. Abhijit Suresh, William R. Penuel, Jennifer K. Jacobs, Ali
Raza, James H. Martin, Tamara Sumner: Using AI Tools to Provide
Teachers with Fully Automated, Personalized Feedback on Their
Classroom Discourse Patterns
* 18: Lydia Bradford: Use of Machine Learning to Score Teacher
Observations
* 19: David Buschhüter, Marisa Pfläging, Andreas Borowski: Widening the
Focus of Science Assessment via Structural Topic Modeling: An Example
of Nature of Science Assessment
* 20: Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S.
Watson, Scott T. Acton: 1. Classification of Instructional Activities
in Classroom Videos Using Neural Networks
* Ethics, Fairness, and Inclusiveness of AI-based STEM Education
* 21: Sahrish Panjwani-Charania, Xiaoming Zhai: AI for Students with
Learning Disabilities: A Systematic Review
* 22: Selin Akgun, Joseph Krajcik: 1. Artificial Intelligence (AI) as
the Growing Actor in Education: Raising Critical Consciousness
Towards Power and Ethics of AI in K-12 STEM Classrooms
* 23: Wanli Xing, Chenglu Li: Fair Artificial Intelligence to Support
STEM Education: A Hitchhiker's Guide
* 24: Marvin Roski, Anett Hoppe, Andreas Nehring: Supporting Inclusive
Science Learning through Machine Learning: The AIISE Framework
* 25: Xiaoming Zhai and Joseph Krajcik: Pseudo Artificial Intelligence
Bias
* Conclusion
* 26: Xiaoming Zhai: Conclusions and Foresight on AI-based STEM
Education: A New Paradigm