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Students benefit from dialogs about their explanations of complex scientific phenomena, and middle school science teachers cannot realistically provide all the guidance they need. We study ways to extend generative teacher–student dialogs to more students by using AI tools. We compare Responsive web-based dialogs to General web-based dialogs by evaluating the ideas students add and the quality of their revised explanations. We designed the General guidance to motivate and encourage students to revise their explanations, similar to how an experienced classroom teacher might instruct the class. We designed the Responsive guidance to emulate a student–teacher dialog, based on studies of experienced teachers guiding individual students. The analyses comparing the Responsive and the General condition are based on a randomized assignment of a total sample of 507 pre-college students. These students were taught by five different teachers in four schools. A significantly higher proportion of students added new accurate ideas in the Responsive condition compared to the General condition during the dialog. This research shows that by using NLP to identify ideas and assign guidance, students can broaden and refine their ideas. Responsive guidance, inspired by how experienced teachers guide individual students, is more valuable than General guidance. 

Natural language processing (NLP) tools can score students’ written explanations, opening new opportunities for science education. Optimally, these scores offer designers opportunities to align guidance with tested pedagogical frameworks and to investigate alternative ways to personalize instruction. We report on research, informed by the knowledge integration (KI) pedagogical framework, using online authorable and customizable environments (ACEs), to promote a deep understanding of complex scientific topics. We study how to personalize guidance to enable students to make productive revisions to written explanations during instruction, where they conduct investigations with models, simulations, hands-on activities, and other materials. We describe how we iteratively refined our assessments and guidance to support students in revising their scientific explanations. We report on recent investigations of hybrid models of personalized guidance that combine NLP scoring with opportunities for teachers to continue the conversation. 

In successful peer discussions students respond to each other and benefit from supports that focus discussion on one another’s ideas. We explore using artificial intelligence (AI) to form groups and guide peer discussion for grade 7 students. We use natural language processing (NLP) to identify student ideas in science explanations. The identified ideas, along with Knowledge Integration (KI) pedagogy, informed the design of a question bank to support students during the discussion. We compare groups formed by maximizing the variety of ideas among participants to randomly formed groups. We embedded the chat tool in an earth science unit and tested it in two classrooms at the same school. We report on the accuracy of the NLP idea detection, the impact of maximized versus random grouping, and the role of the question bank in focusing the discussion on student ideas. We found that the similarity of student ideas limited the value of maximizing idea variety and that the question bank facilitated students’ use of knowledge integration processes. 

Abstract To promote a justice‐oriented approach to science education, we formed a research‐practice partnership between middle school science teachers, their students, curriculum designers, learning scientists, and experts in social justice to co‐design and test an environmental justice unit for middle school instruction. We examine teacher perspectives on the challenges and possibilities of integrating social justice into their standards‐aligned science teaching as they participate in co‐design and teach the unit. The unit supports students to investigate racially disparate rates of asthma in their community by examining pollution maps and historical redlining maps. We analyze interviews and co‐design artifacts from two teachers who participated in the co‐design and taught the unit in their classrooms. Our findings point to the benefits of a shared pedagogical framework and an initial unit featuring local historical content to structure co‐design. Findings also reveal that teachers can share similar goals for empowering students to use science knowledge for civic action while framing the local socio‐political factors contributing to the injustice differently, due in part to different institutional supports and constraints. Student interviews and a pre/postassessment illustrate how the unit facilitated students' progress in connecting socio‐political and science ideas to explain the impacts of particulate matter pollution and who is impacted most. Analyses illuminate how teachers' pedagogical choices may influence whether and how students discuss the impact of systemic racism in their explanations. The findings inform refinement of the unit and suggest supports needed for co‐design partnerships focused on integrating social justice and science. 

This study takes advantage of advances in Natural Language Processing (NLP) to build an idea detection model that can identify ideas grounded in students’ linguistic experiences. We designed adaptive, interactive dialogs for four explanation items using the NLP idea detection model and investigated whether they similarly support students from distinct language backgrounds. The curriculum, assessments, and scoring rubrics were informed by the Knowledge Integration (KI) pedagogy. We analyzed responses of 1,036 students of different language backgrounds taught by 10 teachers in five schools in the western United States. The adaptive dialog engages students from both monolingual English and multilingual backgrounds in incorporating additional relevant ideas into their explanations, resulting in a significant improvement in student responses from initial to revised explanations. The guidance supports students in both language groups to progress in integrating their scientific ideas. 

We explored how Natural Language Processing (NLP) adaptive dialogs that are designed following Knowledge Integration (KI) pedagogy elicit rich student ideas about thermodynamics and contribute to productive revision. We analyzed how 619 6-8th graders interacted with two rounds of adaptive dialog on an end-of-year inventory. The adaptive dialog significantly improved students’ KI levels. Their revised explanations are more integrated across all grades, genders, and prior thermodynamics experiences. The dialog elicited many additional ideas, including normative ideas and vague reasoning. In the first round, students refined their explanation to focus on their normative ideas. In the second round they began to elaborate their reasoning and add new normative ideas. Students added more mechanistic ideas about conductivity, equilibrium, and the distinction between how an object feels and its temperature after the dialog. Thus, adaptive dialogs are a promising tool for scaffolding science sense-making. 

Abstract Guiding teachers to customize curriculum has shown to improve science instruction when guided effectively. We explore how teachers use student data to customize a web-based science unit on plate tectonics. We study the implications for teacher learning along with the impact on student self-directed learning. During a professional development workshop, four 7th grade teachers reviewed logs of their students’ explanations and revisions. They used a curriculum visualization tool that revealed the pedagogy behind the unit to plan their customizations. To promote self-directed learning, the teachers decided to customize the guidance for explanation revision by giving students a choice among guidance options. They took advantage of the web-based unit to randomly assign students (N = 479) to either a guidance Choice or a no-choice condition. We analyzed logged student explanation revisions on embedded and pre-test/post-test assessments and teacher and student written reflections and interviews. Students in the guidance Choice condition reported that the guidance was more useful than those in the no-choice condition and made more progress on their revisions. Teachers valued the opportunity to review student work, use the visualization tool to align their customization with the knowledge integration pedagogy, and investigate the choice option empirically. These findings suggest that the teachers’ decision to offer choice among guidance options promoted aspects of self-directed learning.