Xiaoming Zhai
Biographic Data
| ID | 5877160 |
|---|---|
| NAME | Xiaoming Zhai |
| GIVEN NAMES | Xiaoming |
| FAMILY NAME | Zhai |
| SIGNATURE | ZHAI X |
| AFFILIATIONS | University of Georgia |
| ORCID | 0000-0003-4519-1931 |
| VERIFIED | Yes |
| TOTAL WORKS | 24 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 24 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Examining the impact of virtual reality on middle school students’ biology learning outcomes and experiences
Virtual reality (VR) is increasingly posited to transform biology education by visualising complex, microscopic, and abstract structures. However, prior research has yielded inconsistent findings regarding the comparative efficacy of different visualisation modalities, particularly when integrating cognitive, affective, and technological acceptance variables. This study conducted a six-week quasi-experimental investigation at a middle school, exa…
Transforming science teacher education in the era of generative artificial intelligence: Global perspectives
Since generative artificial intelligence (GenAI) has emerged as a transformative force in science teaching, learning, and evaluation, countries worldwide have launched initiatives and professional development programs to equip science teachers with essential AI competencies. This paper provides a comparative review of science teacher education on GenAI across eight countries: Australia, Canada, China, Germany, Ghana, Singapore, South Korea, and t…
A Framework for Designing an AI Chatbot to Support Scientific Argumentation
As large language models (LLMs) are increasingly used to support learning, there is a growing need for a principled framework to guide the design of LLM-based tools and resources that are pedagogically effective and contextually responsive. This study proposes a framework by examining how prompt engineering can enhance the quality of chatbot responses to support middle school students’ scientific reasoning and argumentation. Drawing on learning t…
Can Generative AI and ChatGPT Outperform Humans on Cognitive-Demanding Problem-Solving Tasks in Science
Collaborative Learning with Artificial Intelligence Speakers: Pre-service Elementary Science Teachers’ Responses to the Prototype
Realizing Visual Question Answering for Education: GPT-4V as a Multimodal AI
A Multimodal Interactive Framework for Science Assessment in the Era of Generative Artificial Intelligence
The rapid evolution of generative artificial intelligence (GenAI) is transforming science education by facilitating innovative pedagogical paradigms while raising substantial concerns about scholarly integrity. One particularly pressing issue is the growing risk of student use of GenAI tools to outsource assessment tasks, potentially compromising authentic learning and evaluations. Addressing these challenges requires reflection on existing asses…
Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework
In diverse classrooms, one of the challenges educators face is creating assessments that reflect the different cultural backgrounds of every student. This study presents a novel approach to the automatic generation of cultural and context-specific science assessments items for K-12 education using generative AI (GenAI). We first developed a GenAI Culturally Responsive Science Assessment (GenAI-CRSciA) framework that connects CRSciA, specifically …
Using educative learning progression to support novice science teachers’ lesson plan critiques
Learning progressions (LPs) are considered to have great potential to improve pedagogical practices. However, even with LPs, teachers may still be unaware of the barriers that keep students from progressing; many are struggling with essential pedagogical strategies to support students’ progression. This study thus proposed an educative LP (ELP), a framework that informs teachers about students’ cognitive development and provides pedagogical strat…
Using automated analysis to assess middle school students' competence with scientific argumentation
Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning—a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the measurement of knowledge and understanding to the measurement of performance and knowledge in use. Performance t…
From Campus to Market: The Algorithmic Influence on Academic Capitalism
AI and formative assessment: The train has left the station
In response to Li, Reigh, He, and Miller's commentary, Can we and should we use artificial intelligence for formative assessment in science , we argue that artificial intelligence (AI) is already being widely employed in formative assessment across various educational contexts. While agreeing with Li et al.'s call for further studies on equity issues related to AI, we emphasize the need for science educators to adapt to the AI revolution that has…
ChatGPT User Experience: Implications for Education
Developing effective and accessible activities to improve and assess computational thinking and engineering learning
Examining adults’ web navigation patterns in multi-layered hypertext environments
Applying machine learning to automatically assess scientific models
Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student‐developed models is time‐ and cost‐intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adj…
Assessing high‐school students' modeling performance on Newtonian mechanics
Assessing scientific modeling competence (SMC) is challenging because of the multi‐dimensionality of the construct and the potential variability of student performance across tasks. To deal with the challenges, this study applied Kane's validity framework to assess high‐school students' SMC in Newtonian mechanics and examined how students' performance depended on tasks. We first specified students' SMC in three dimensions: Conceptualization, Depl…
A Framework of Construct-Irrelevant Variance for Contextualized Constructed Response Assessment
Estimating and monitoring the construct-irrelevant variance (CIV) is of significant importance to validity, especially for constructed response assessments with rich contextualized information. To examine CIV in contextualized constructed response assessments, we developed a framework including a model accounting for CIV and a measurement that could differentiate the CIV. Specifically, the model includes CIV due to three factors: the variability …
Advancing automatic guidance in virtual science inquiry: From ease of use to personalization
Validating a partial-credit scoring approach for multiple-choice science items: An application of fundamental ideas in science
This study provides a partial-credit scoring (PCS) approach to awarding students’ performance on multiple-choice items in science education. The approach is built on fundamental ideas, the critical pieces of students’ understanding and knowledge to solve science problems. We link each option of the items to several specific fundamental ideas to capture their mastery patterns when an option is selected. Using these mastery patterns to order the op…
Assessing learning in technology-rich maker activities: A systematic review of empirical research
Assessing computational thinking: A systematic review of empirical studies
From substitution to redefinition: A framework of machine learning‐based science assessment
This study develops a framework to conceptualize the use and evolution of machine learning (ML) in science assessment. We systematically reviewed 47 studies that applied ML in science assessment and classified them into five categories: (a) constructed response, (b) essay, (c) simulation, (d) educational game, and (e) inter‐discipline. We compared the ML‐based and conventional science assessments and extracted 12 critical characteristics to map t…
Evaluation of construct-irrelevant variance yielded by machine and human scoring of a science teacher PCK constructed response assessment
Assessing learning in technology-rich maker activities: A systematic review of empirical research
Assessing computational thinking: A systematic review of empirical studies
From substitution to redefinition: A framework of machine learning‐based science assessment
This study develops a framework to conceptualize the use and evolution of machine learning (ML) in science assessment. We systematically reviewed 47 studies that applied ML in science assessment and classified them into five categories: (a) constructed response, (b) essay, (c) simulation, (d) educational game, and (e) inter‐discipline. We compared the ML‐based and conventional science assessments and extracted 12 critical characteristics to map t…
Evaluation of construct-irrelevant variance yielded by machine and human scoring of a science teacher PCK constructed response assessment
A Framework of Construct-Irrelevant Variance for Contextualized Constructed Response Assessment
Estimating and monitoring the construct-irrelevant variance (CIV) is of significant importance to validity, especially for constructed response assessments with rich contextualized information. To examine CIV in contextualized constructed response assessments, we developed a framework including a model accounting for CIV and a measurement that could differentiate the CIV. Specifically, the model includes CIV due to three factors: the variability …
Advancing automatic guidance in virtual science inquiry: From ease of use to personalization
Validating a partial-credit scoring approach for multiple-choice science items: An application of fundamental ideas in science
This study provides a partial-credit scoring (PCS) approach to awarding students’ performance on multiple-choice items in science education. The approach is built on fundamental ideas, the critical pieces of students’ understanding and knowledge to solve science problems. We link each option of the items to several specific fundamental ideas to capture their mastery patterns when an option is selected. Using these mastery patterns to order the op…
ChatGPT User Experience: Implications for Education
Developing effective and accessible activities to improve and assess computational thinking and engineering learning
Examining adults’ web navigation patterns in multi-layered hypertext environments
Applying machine learning to automatically assess scientific models
Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student‐developed models is time‐ and cost‐intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adj…
Assessing high‐school students' modeling performance on Newtonian mechanics
Assessing scientific modeling competence (SMC) is challenging because of the multi‐dimensionality of the construct and the potential variability of student performance across tasks. To deal with the challenges, this study applied Kane's validity framework to assess high‐school students' SMC in Newtonian mechanics and examined how students' performance depended on tasks. We first specified students' SMC in three dimensions: Conceptualization, Depl…
AI and formative assessment: The train has left the station
In response to Li, Reigh, He, and Miller's commentary, Can we and should we use artificial intelligence for formative assessment in science , we argue that artificial intelligence (AI) is already being widely employed in formative assessment across various educational contexts. While agreeing with Li et al.'s call for further studies on equity issues related to AI, we emphasize the need for science educators to adapt to the AI revolution that has…
Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework
In diverse classrooms, one of the challenges educators face is creating assessments that reflect the different cultural backgrounds of every student. This study presents a novel approach to the automatic generation of cultural and context-specific science assessments items for K-12 education using generative AI (GenAI). We first developed a GenAI Culturally Responsive Science Assessment (GenAI-CRSciA) framework that connects CRSciA, specifically …
Using educative learning progression to support novice science teachers’ lesson plan critiques
Learning progressions (LPs) are considered to have great potential to improve pedagogical practices. However, even with LPs, teachers may still be unaware of the barriers that keep students from progressing; many are struggling with essential pedagogical strategies to support students’ progression. This study thus proposed an educative LP (ELP), a framework that informs teachers about students’ cognitive development and provides pedagogical strat…
Using automated analysis to assess middle school students' competence with scientific argumentation
Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning—a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the measurement of knowledge and understanding to the measurement of performance and knowledge in use. Performance t…
From Campus to Market: The Algorithmic Influence on Academic Capitalism
A Framework for Designing an AI Chatbot to Support Scientific Argumentation
As large language models (LLMs) are increasingly used to support learning, there is a growing need for a principled framework to guide the design of LLM-based tools and resources that are pedagogically effective and contextually responsive. This study proposes a framework by examining how prompt engineering can enhance the quality of chatbot responses to support middle school students’ scientific reasoning and argumentation. Drawing on learning t…
Can Generative AI and ChatGPT Outperform Humans on Cognitive-Demanding Problem-Solving Tasks in Science
Collaborative Learning with Artificial Intelligence Speakers: Pre-service Elementary Science Teachers’ Responses to the Prototype
Realizing Visual Question Answering for Education: GPT-4V as a Multimodal AI
A Multimodal Interactive Framework for Science Assessment in the Era of Generative Artificial Intelligence
The rapid evolution of generative artificial intelligence (GenAI) is transforming science education by facilitating innovative pedagogical paradigms while raising substantial concerns about scholarly integrity. One particularly pressing issue is the growing risk of student use of GenAI tools to outsource assessment tasks, potentially compromising authentic learning and evaluations. Addressing these challenges requires reflection on existing asses…
Examining the impact of virtual reality on middle school students’ biology learning outcomes and experiences
Virtual reality (VR) is increasingly posited to transform biology education by visualising complex, microscopic, and abstract structures. However, prior research has yielded inconsistent findings regarding the comparative efficacy of different visualisation modalities, particularly when integrating cognitive, affective, and technological acceptance variables. This study conducted a six-week quasi-experimental investigation at a middle school, exa…
Transforming science teacher education in the era of generative artificial intelligence: Global perspectives
Since generative artificial intelligence (GenAI) has emerged as a transformative force in science teaching, learning, and evaluation, countries worldwide have launched initiatives and professional development programs to equip science teachers with essential AI competencies. This paper provides a comparative review of science teacher education on GenAI across eight countries: Australia, Canada, China, Germany, Ghana, Singapore, South Korea, and t…
Computer Science (18 works) · Psychology (17 works) · Mathematics education (14 works) · Artificial Intelligence (12 works) · Science education (9 works) · Artificial Intelligence (6 works) · Educational Strategies and Epistemologies (6 works) · Innovative Teaching and Learning Methods (6 works) · Science Education and Pedagogy (6 works) · Cognition (5 works)