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This study investigates small group collaborative learning with a technology-supported environment. We aim to reveal key aspects of collaborative learning by examining variations in interaction, the influence of small group collaboration on science knowledge integration, and the implications for individual knowledge mastery. Results underscore the importance of high-quality science discourse and user-friendly tools. The study also highlights that group-level negotiations may not always affect individual understanding. Overall, this research offers insights into the complexities of collaboration and its impact on science learning. 

Simulations are widely used to teach science in grade schools. These Ralph Knipper rak0035@auburn.edu Auburn University Auburn, Alabama, USA Sadhana Puntambekar puntambekar@education.wisc.edu University of Wisconsin-Madison Madison, Wisconsin, USA Large Language Models, Conversational AI, Meta-Conversation, simulations are often augmented with a conversational artificial intelligence (AI) agent to provide real-time scaffolding support for students conducting experiments using the simulations. AI agents are highly tailored for each simulation, with a predesigned set of Instructional Goals (IGs). This makes it difficult for teachers to adjust IGs as the agent may no longer align with the revised IGs. Additionally, teachers are hesitant to adopt new third-party simulations for the same reasons. In this research, we introduce SimPal, a Large Language Model (LLM) based meta-conversational agent, to solve this misalignment issue between a pre-trained conversational AI agent and the constantly evolving pedagogy of instructors. Through natural conversation with SimPal, teachers first explain their desired IGs, based on which SimPal identifies a set of relevant physical variables and their relationships to create symbolic representations of the desired IGs. The symbolic representations can then be leveraged to design prompts for the original AI agent to yield better alignment with the desired IGs. We empirically evaluated SimPal using two LLMs, ChatGPT-3.5 and PaLM 2, on 63 Physics simulations from PhET and Golabz. Additionally, we examined the impact of different prompting techniques on LLM’s performance by utilizing the TELeR taxonomy to identify relevant physical variables for the IGs. Our findings showed that SimPal can do this task with a high degree of accuracy when provided with a well-defined prompt. 

This study explored the Idea Wall, a collaborative knowledge-building tool to support students’ collaboration in small groups during a plant biology science curriculum. We examined the affordances and challenges of the Idea Wall and found the effective use of the tool's spatial organization capabilities by students, particularly the Yup Zone and the intermediary neutral spaces, for collaboratively organizing notes. But there's also a need for improvements in some features of the tool’s design and instructional guidance. 

Abstract As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and provide feedback on middle school science writing without linguistic discrimination. Linguistic discrimination in this study was operationalized as unfair assessment of scientific essays based on writing features that are not considered normative such as subject‐verb disagreement. Such unfair assessment is especially problematic when the purpose of assessment is not assessing English writing but rather assessing the content of scientific explanations. PyrEval was implemented in middle school science classrooms. Students explained their roller coaster design by stating relationships among such science concepts as potential energy, kinetic energy and law of conservation of energy. Initial and revised versions of scientific essays written by 307 eighth‐grade students were analyzed. Our manual and NLP assessment comparison analysis showed that PyrEval did not penalize student essays that contained non‐normative writing features. Repeated measures ANOVAs and GLMM analysis results revealed that essay quality significantly improved from initial to revised essays after receiving the NLP feedback, regardless of non‐normative writing features. Findings and implications are discussed. Practitioner notesWhat is already known about this topicAdvancement in AI has created a variety of opportunities in education, including automated assessment, but AI is not bias‐free.Automated writing assessment designed to improve students' scientific explanations has been studied.While limited, some studies reported biased performance of automated writing assessment tools, but without looking into actual linguistic features about which the tools may have discriminated.What this paper addsThis study conducted an actual examination of non‐normative linguistic features in essays written by middle school students to uncover how our NLP tool called PyrEval worked to assess them.PyrEval did not penalize essays containing non‐normative linguistic features.Regardless of non‐normative linguistic features, students' essay quality scores significantly improved from initial to revised essays after receiving feedback from PyrEval. Essay quality improvement was observed regardless of students' prior knowledge, school district and teacher variables.Implications for practice and/or policyThis paper inspires practitioners to attend to linguistic discrimination (re)produced by AI.This paper offers possibilities of using PyrEval as a reflection tool, to which human assessors compare their assessment and discover implicit bias against non‐normative linguistic features.PyrEval is available for use ongithub.com/psunlpgroup/PyrEvalv2.