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This study explored how researcher–coach dyads collaborated to create research-based briefs for classroom use. Data from a two-day workshop and dyads’ final products were analyzed using interaction analysis. The dyad’s work illuminated how products and relationships evolved together, fostering ownership and collaboration. These findings inform future researcher–practitioner partnerships and guide facilitation of effective collaborations by highlighting relationship building, agency, and ownership to shape joint work. 

This study examines researcher-practitioner collaborations in educational research, using Cultural-Historical Activity Theory (CHAT) to analyze interactions between STEM coaches and researchers. It explores a two-day workshop focused on practitioner-identified challenges. The research highlighted the need to shift from researcher-centric approaches to balanced, collaborative methods. This study provides insights for developing bidirectional learning models that center practitioners' perspectives, aiming to bridge the gap between research and educational practice through more equitable and transformative partnerships. 

Effective researcher-coach relationships need reciprocal learning, which allows practitioners to share valuable contextual knowledge while researchers share evidence-based ideas. Nevertheless, these collaborations encounter obstacles due to power imbalances, which frequently establish researchers as authorities and reduce the role of practitioners as co-creators. Therefore, this study examines power dynamics in researcher-coach partnerships within educational contexts, emphasizing equitable collaboration strategies. Using Cultural-Historical Activity Theory (CHAT) as a framework, this study analyzes video data from a writing intensive to explore interactions between two participants, Ashley and Russell. Findings reveal that initial tensions foster deeper understanding through negotiated power exchanges. The study underscores that openness, mutual trust, and reflective dialogue are essential for sustainable partnerships, advancing the understanding of power dynamics in researcher-coach collaborations. 

Disagreement is often perceived negatively, yet it can be beneficial for learning and scientific inquiry. However, students tend to avoid engaging in disagreement. Peer critique activities offer a promising way to encourage students to embrace disagreement, which supports learning as students articulate their ideas, making them available for discussion, revision, and refinement. This study aims to better understand how students express disagreement during peer critique within small groups and how that affects moving their inquiry forward. It explores 5th-grade students’ management of disagreement within a computer-supported collaborative modeling environment. Using conversation analysis, we identified various forms of disagreements employed by students when engaging with different audiences. We observed a tendency for students to disagree softly; that is, disagreement was implied and/or mitigated. Students’ resolution of both direct and soft disagreements effectively promoted their collective knowledge advancement, including building shared scientific understanding and improving their models, while maintaining a positive socio-emotional climate. These findings have implications for designing CSCL environments with respect to supporting students in providing and responding to peer critiques at the group level. 

This study highlights how middle schoolers discuss the benefits and drawbacks of AI-driven conversational agents in learning. Using thematic analysis of focus groups, we identified five themes in students’ views of AI applications in education. Students recognized the benefits of AI in making learning more engaging and providing personalized, adaptable scaffolding. They emphasized that AI use in education needs to be safe and equitable. Students identified the potential of AI in supporting teachers and noted that AI educational agents fall short when compared to emotionally and intellectually complex humans. Overall, we argue that even without technical expertise, middle schoolers can articulate deep, multifaceted understandings of the possibilities and pitfalls of AI in education. Centering student voices in AI design can also provide learners with much-desired agency over their future learning experiences. 

Abstract Understanding students’ multi-party epistemic and topic based-dialogue contributions, or how students present knowledge in group-based chat interactions during collaborative game-based learning, offers valuable insights into group dynamics and learning processes. However, manually annotating these contributions is labor-intensive and challenging. To address this, we develop an automated method for recognizing dialogue acts from text chat data of small groups of middle school students interacting in a collaborative game-based learning environment. Our approach utilizes dual contrastive learning and label-aware data augmentation to fine-tune large language models’ underlying embedding representations within a supervised learning framework for epistemic and topic-based dialogue act classification. Results show that our method achieves a performance improvement of 4% to 8% over baseline methods in two key classification scenarios. These findings highlight the potential for automated dialogue act recognition to support understanding of how meaning-making occurs by focusing on the development and evolution of knowledge in group discourse, ultimately providing teachers with actionable insights to better support student learning.