Technology

Myths, Mis- and Preconceptions of Artificial Intelligence: A Review of the Literature

Artificial Intelligence (AI) is prevalent in nearly every aspect of our lives. However, recent studies have found a significant amount of confusion and misunderstanding surrounding AI. To develop effective educational programs in the field of AI, it is vital to examine and understand learners' pre- and misconceptions as well as myths about AI. This study examined a corpus of 591 studies.

Author/Presenter

Arne Bewersdorff

Xiaoming Zhai

Jessica Roberts

Claudia Nerdel

Lead Organization(s)
Year
2023
Short Description

Artificial Intelligence (AI) is prevalent in nearly every aspect of our lives. However, recent studies have found a significant amount of confusion and misunderstanding surrounding AI. To develop effective educational programs in the field of AI, it is vital to examine and understand learners' pre- and misconceptions as well as myths about AI. This study examined a corpus of 591 studies.

ChatGPT for Next Generation Science Learning

This article pilots ChatGPT in tackling the most challenging part of science learning and found it successful in automation of assessment development, grading, learning guidance, and recommendation of learning materials.

Zhai, X. (2023). ChatGPT for Next Generation Science Learning | XRDS: Crossroads, 29(3), 42-46. https://doi.org/10.1145/3589649

Author/Presenter
Xiaoming Zhai
Lead Organization(s)
Year
2023
Short Description

This article pilots ChatGPT in tackling the most challenging part of science learning and found it successful in automation of assessment development, grading, learning guidance, and recommendation of learning materials.

Leveraging Dynamically Linked Representations in a Semi-Structured Workspace to Cultivate Mathematical Modeling Competencies Among Secondary Students (M2Studio)

The need for mathematical modeling is vital in answering critical questions like disease spread and climate change, but beginners lack the necessary skills to plan, organize, and execute such tasks. Also, current tools are insufficient for optimal learning. To address these issues, we're developing a web-based technology (M2Studio) and a 10.5-hour curriculum to introduce students to mathematical modeling using dynamically linked representations. This three-year project aims to enhance students' modeling skills and understanding.

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Using Natural Language Processing to Inform Science Instruction (Collaborative Research: Linn)

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NLP-TIPS takes advantage of natural language processing (NLP) methods to detect students’ ideas in written science explanations. We design adaptive guidance that supports each student to consider their own ideas and pursue deeper understanding of phenomena. This work continues a successful partnership between University of California, Berkeley, Educational Testing Service (ETS), and science teachers from schools enrolling students from diverse racial, ethnic, and linguistic groups whose cultural experiences may be neglected in science instruction.

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Teaching Students to Reason about Variation and Covariation in Data: What Do We Know and What Do We Need to Find Out?

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The purpose of this project is to gather, analyze, and synthesize research studies that have investigated different approaches to supporting students in grades 6-14 in learning to analyze, interpret, and reason about data with a focus on variation and covariation. We will use Robust Variance Estimation (RVE) to examine how effect size estimates depend on intervention characteristics, study design, outcomes of interest, and demographic characteristics of participants in the studies.

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Supporting Teachers in Responsive Instruction for Developing Expertise in Science (Collaborative Research: Linn)

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STRIDES supports science teachers to rapidly respond to the diverse students in their classrooms. Leveraging advances in natural language processing, the project analyzes student written explanations of scientific phenomena to provide fine-grained summaries to teachers about student knowledge integration across NGSS dimensions. STRIDES suggests learning science-based customizations and studies how teachers use the summaries and customization suggestions to improve student progress. The researchers study how well the customizations address the learning needs of diverse students.

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Supporting Teacher Understanding of Emergent Computational Thinking in Early Elementary Students

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This project is conducting research on the modes of interaction that effectively prepare K-2 teachers to engage in reflective inquiry about their students’ emergent use of computational thinking strategies, and embed those learnings within a CoP platform to support scalable teacher professional development.

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Supporting Secondary Students’ Earth Science Knowledge and Engineering Design Skills with Mobile Design Studios

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This project seeks to support students to creatively combine science, engineering and social or community knowledge to address Earth Science problems in their local communities. Through a collaborative project based learning platform with built in scaffolding and an AI design mentor, students will engage in front-end design practices to understand and frame problems and explore solutions. We examine how this impacts science conceptual knowledge, design skills and creativity for middle and high school students.

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Supporting Science Learning and Teaching in Middle School Classrooms Through Automated Analysis of Students' Writing (Collaborative Research: Puntambekar)

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Written science explanations are central to learning and practicing science. However, there are two main challenges: first, students struggle to explain their ideas and use supporting data appropriately. Second, it is not realistic for teachers to provide real-time, comprehensive feedback to each student. We will present how PyrEval, a natural language processing (NLP) technology, provided students with timely, personalized, automated feedback. We will discuss opportunities and challenges for using automated feedback in classrooms.

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Strengthening STEM Teaching in Native American Serving Schools Through Long-Term, Culturally Responsive Professional Development

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Indigenous students experience persistent inequitable outcomes in schools, particularly in STEM. Since teacher quality is one of the most important school-based factors impacting K12 students’ learning and engagement in schools, our project provides long-term professional development to teachers in rural, Indigenous-serving schools to support teachers’ development of culturally responsive STEM curriculum units. We provide an overview of our professional development model, and share findings from our work with over 100 teachers during four program years.

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