Artificial intelligence (AI) can enhance teachers' capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un‐planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers' voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co‐design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co‐orchestration tool, called Pair‐Up . We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co‐orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI‐based tutoring systems. We leveraged qualitative content analysis to examine teachers' views about sharing control with Pair‐Up , and we describe high‐level insights about the human‐AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human‐centred learning analytics can be applied to the design of human‐AI technologies and share reflections for human‐AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co‐orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems. Practitioner notes: What is already known about this topic: Artificial Intelligence (AI) can help teachers facilitate complex classroom activities, such as having students move between individual and collaborative learning in unplanned ways. Designers should use human‐centred design approaches to give teachers a voice in deciding what AI might do in the classroom and if or how they want to share control with it. What this paper adds: Presents teacher views about how they want to share control with AI to support students moving between individual and collaborative learning. Describes how we adapted six design methods to design AI features. Illustrates a complete, iterative process to create human‐AI interactions to support teachers as they facilitate students moving from individual to collaborative learning. Implications for practice: We share five implications for designers that teachers highlighted as necessary when designing AI‐features, including control, trust, responsibility, efficiency and accuracy. Our work also includes a reflection on our design process and implications for future design processes.

We present reflections on our experience using a generative AI model in qualitative research, to illuminate the AI's contributions to our analytic process. Our analytic focus was a segment of classroom transcript, which captured a teacher introducing scientific theory-building practices to middle school students. We used a grounded theory approach to produce a fine-grained characterization of the teacher's talk moves during the lesson implementation. Our eventual goal is to build a more nuanced conceptualization of responsive teaching in the context of theory-building activities. We involved GPT-4 during the initial exploratory and later focused coding stages. For our analysis of GPT-4’s contributions to the analytic process, we analyzed our notes and analytic memos, along with video recordings of meetings where we discussed insights in response to GPT-4’s input. We present vignettes to illustrate pivotal moments where AI contributed to the coding process, including code generation, comparison, and refinement. The paper presents our experiences of conducting qualitative research in partnership with generative AI, underscoring the role that emerging technologies can play in the analysis of data and the development of grounded theory.

Combining individual and collaborative learning is common, but dynamic combinations (which happen as-the-need arises, rather than in pre-planned ways, and may happen on an individual basis) are rare. This work reports findings from a technology probe study exploring alternative designs for classroom co-orchestration support for dynamically transitioning between individual and collaborative learning. The study involved 1) a technology-probe classroom study in an authentic, AI-supported classroom to understand teachers' and students' needs for co-orchestration support over dynamic transitions; and 2) workshops and interviews with students and teachers to get informed feedback about their lived experiences. 118 students and three teachers from a middle school in the US experienced a pairing policy – student, teacher and, AI-controlled pairing policy – (i.e., identifying students needing help and potential helpers) for switching from individual to a peer tutoring activity. This work aims to answer the following questions: 1) How did students and teachers react to these pairing policies?; and 2) What are students' and teachers' desires for sharing control over the orchestration of dynamic transitions? Findings suggest the need for a form of hybrid control between students, teachers, and AI systems over transitions, as well as for adaptivity and adaptability for different classroom characteristics, teachers, and students' prior knowledge.

Despite growing interest in applying human-centred design methods to create learning analytics (LA) systems, most efforts have concentrated on initial design phases, with limited exploration of how LA tools and practices can co-evolve during the actual learning and teaching activities. This paper examines how a human-centred LA dashboard can be further refined and adapted by teachers while actively using it in a real-world scenario (i.e., design-in-use), beyond its intended design (i.e., design-for-use). We use instrumental genesis as a theoretical lens to analyze the temporary and permanent instrumentalization of design features and individual and collective instrumentation of the LA dashboard. The analysis of semi-structured individual interviews with five nursing teachers who used an LA dashboard to guide team reflections with 224 students (56 teams) revealed technical and pedagogical changes that occurred in both the system’s features (instrumentalization) and teaching practices (instrumentation). We found that teachers adopted the LA dashboard beyond initially intended ways by (i) providing emotional support with the analytics, (ii) reducing details in AI-powered data visualizations for easier comprehension, (iii) creating data narratives to address data limitations, and (iv) collectively developing new practices to use the LA dashboard for co-teaching. Therefore, teachers’ design-in-use of the LA dashboard highlights the ongoing need for design improvements to address challenges posed by dynamic data and complex algorithms underlying AI and analytics interfaces.