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Cognitive Synergy: Exploring the Transformative Intersection of Human Intelligence and Artificial Intelligence in Designing Equitable Next Generation Science Assessments
Cognitive Synergy: Exploring the Transformative Intersection of Human Intelligence and Artificial Intelligence in Designing Equitable Next Generation Science Assessments
Detailed Information
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152642
- ISBN
- 9798383569290
- DDC
- 370
- 저자명
- Li, Tingting.
- 서명/저자
- Cognitive Synergy: Exploring the Transformative Intersection of Human Intelligence and Artificial Intelligence in Designing Equitable Next Generation Science Assessments
- 발행사항
- [Sl] : Michigan State University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 267 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
- 주기사항
- Advisor: Krajcik, Joseph S.;Spiro, Rand J.
- 학위논문주기
- Thesis (Ph.D.)--Michigan State University, 2024.
- 초록/해제
- 요약This study explores the intersection of human intelligence and Artificial Intelligence (AI) to design knowledge-in-use science assessments for supporting students' deep science learning. In the context of evolving educational paradigms, it seeks to harness AI tools (GPT), to enhance knowledge-in-use assessment design, ensuring equitable opportunities for diverse learners. Anchored in the Next Generation Science Assessment and an evidence-centered design, this study aspires to harmonize AI's computational strengths with human expertise in assessment design. Drawing from an array of theoretical frameworks-Hybrid Intelligence System, Distributed Cognition, and Self-Regulated Learning Theory-the study underscores the multi-faceted and dynamic nature of knowledge-in-use and the symbiotic integration of human and AI.Employing a Design-Based Research approach, the study proceeds in three stages: (1) Iteratively training GPT models for effective designing knowledge-in-use assessments; (2) Gathering multidisciplinary expert feedback on AI-co-designed assessments; and (3) developing a domain-specific GPT-model for tailored assessment design that capture knowledge-in-use and address diverse student needs. Diverse data analysis techniques, encompassing thematic analysis, and descriptive statistics, such as heat map and scatter plot, are leveraged. Anticipated results spotlight an exploratory GPT model adept at creating tailored assessments resonating with diverse learning needs while emphasizing equity, adaptability, and inclusivity. This study holds the potential to significantly enhance the educational landscape by advocating a balanced approach where AI complements human expertise, paving the way for a progressive and inclusive future in education.
- 일반주제명
- Educational psychology
- 일반주제명
- Educational technology
- 일반주제명
- Science education
- 키워드
- Equity
- 키워드
- Science teaching
- 기타저자
- Michigan State University Educational Psychology and Educational Technology - Doctor of Philosophy
- 기본자료저록
- Dissertations Abstracts International. 86-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■1001 ▼aLi, Tingting.▼0(orcid)0000-0002-5692-2042
■24510▼aCognitive Synergy: Exploring the Transformative Intersection of Human Intelligence and Artificial Intelligence in Designing Equitable Next Generation Science Assessments
■260 ▼a[Sl]▼bMichigan State University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a267 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-01, Section: B.
■500 ▼aAdvisor: Krajcik, Joseph S.;Spiro, Rand J.
■5021 ▼aThesis (Ph.D.)--Michigan State University, 2024.
■520 ▼aThis study explores the intersection of human intelligence and Artificial Intelligence (AI) to design knowledge-in-use science assessments for supporting students' deep science learning. In the context of evolving educational paradigms, it seeks to harness AI tools (GPT), to enhance knowledge-in-use assessment design, ensuring equitable opportunities for diverse learners. Anchored in the Next Generation Science Assessment and an evidence-centered design, this study aspires to harmonize AI's computational strengths with human expertise in assessment design. Drawing from an array of theoretical frameworks-Hybrid Intelligence System, Distributed Cognition, and Self-Regulated Learning Theory-the study underscores the multi-faceted and dynamic nature of knowledge-in-use and the symbiotic integration of human and AI.Employing a Design-Based Research approach, the study proceeds in three stages: (1) Iteratively training GPT models for effective designing knowledge-in-use assessments; (2) Gathering multidisciplinary expert feedback on AI-co-designed assessments; and (3) developing a domain-specific GPT-model for tailored assessment design that capture knowledge-in-use and address diverse student needs. Diverse data analysis techniques, encompassing thematic analysis, and descriptive statistics, such as heat map and scatter plot, are leveraged. Anticipated results spotlight an exploratory GPT model adept at creating tailored assessments resonating with diverse learning needs while emphasizing equity, adaptability, and inclusivity. This study holds the potential to significantly enhance the educational landscape by advocating a balanced approach where AI complements human expertise, paving the way for a progressive and inclusive future in education.
■590 ▼aSchool code: 0128.
■650 4▼aEducational psychology
■650 4▼aEducational technology
■650 4▼aScience education
■653 ▼aEquity
■653 ▼aFormative assessment
■653 ▼aHuman intelligence
■653 ▼aInterdisciplinary cognitive synergy
■653 ▼aScience teaching
■690 ▼a0525
■690 ▼a0710
■690 ▼a0714
■690 ▼a0800
■71020▼aMichigan State University▼bEducational Psychology and Educational Technology - Doctor of Philosophy.
■7730 ▼tDissertations Abstracts International▼g86-01B.
■790 ▼a0128
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163236▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
