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1. Multimodal analytics for collaborative teacher reflection of human-ai hybrid teaching: design opportunities and constraints

   https://doi.org/10.1007/978-3-031-42682-7_45  

   Karumbaiah, S.; Liu, P.; Maksimova, A.; De Vylder, L.; Rummel, N.; Aleven, V. (August 2023, Springer)

   Viberg, O.; Jivet, I.; Muñoz-Merino, P.; Perifanou, M.; Papathoma, T. (Ed.)

   Past research shows that teachers benefit immensely from reflecting on their classroom practices. At the same time, adaptive and artificially intelligent (AI) tutors are shown to be highly effective for students, especially when teachers are involved in supporting students’ learning. Yet, there is little research on how to support teachers to reflect on their practices around AI tutors. We posit that analytics built on multimodal data from the classroom (e.g., teacher position, student-AI interaction) would be beneficial in providing effective scaffolding and evidence for teachers’ collaborative reflection on human-AI hybrid teaching. To better understand the design opportunities and constraints of a future tool for teacher reflection, we conducted storyboarding sessions with seven in-service teachers. Our analysis revealed that certain modalities (e.g., position v. video) might be more beneficial and less constrained than others in identifying reflection-worthy moments and trends. We discuss teachers’ needs for reflection in classrooms with AI tutors and their boundaries in using multimodal analytics. 

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2. Surveying Teachers’ Preferences and Boundaries regarding Human-AI Control in Dynamic Pairing of Students for Collaborative Learning

   Yang, K.; Lawrence, L.; Echeverria, V.; Guo, B.; Holstein, K.; Rummel, N.; Aleven, V. (January 2021, European Conference on Technology Enhanced Learning, EC-TEL 2021)

   null (Ed.)

   Orchestration tools may support K-12 teachers in facilitating student learning, especially when designed to address classroom stakeholders’ needs. Our previous work revealed a need for human-AI shared control when dynamically pairing students for collaborative learning in the classroom, but offered limited guidance on the role each agent should take. In this study, we designed storyboards for scenarios where teachers, students and AI co-orchestrate dynamic pairing when using AI-based adaptive math software for individual and collaborative learning. We surveyed 54 math teachers on their co-orchestration preferences. We found that teachers would like to share control with the AI to lessen their orchestration load. As well, they would like to have the AI propose student pairs with explanations, and identify risky proposed pairings. However, teachers are hesitant to let the AI auto-pair students even if they are busy, and are less inclined to let AI override teacher-proposed pairing. Our study contributes to teachers’ needs, preference, and boundaries for how they want to share the task and control of student pairing with the AI and students, and design implications in human-AI co-orchestration tools. 

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3. A multimodal approach to support teacher, researcher and AI collaboration in STEM +C learning environments

   https://doi.org/10.1111/bjet.13518  

   Cohn, Clayton; Snyder, Caitlin; Fonteles, Joyce_Horn; T_S, Ashwin; Montenegro, Justin; Biswas, Gautam (September 2024, British Journal of Educational Technology)

   AbstractRecent advances in generative artificial intelligence (AI) and multimodal learning analytics (MMLA) have allowed for new and creative ways of leveraging AI to support K12 students' collaborative learning in STEM+C domains. To date, there is little evidence of AI methods supporting students' collaboration in complex, open‐ended environments. AI systems are known to underperform humans in (1) interpreting students' emotions in learning contexts, (2) grasping the nuances of social interactions and (3) understanding domain‐specific information that was not well‐represented in the training data. As such, combined human and AI (ie, hybrid) approaches are needed to overcome the current limitations of AI systems. In this paper, we take a first step towards investigating how a human‐AI collaboration between teachers and researchers using an AI‐generated multimodal timeline can guide and support teachers' feedback while addressing students' STEM+C difficulties as they work collaboratively to build computational models and solve problems. In doing so, we present a framework characterizing the human component of our human‐AI partnership as a collaboration between teachers and researchers. To evaluate our approach, we present our timeline to a high school teacher and discuss the key insights gleaned from our discussions. Our case study analysis reveals the effectiveness of an iterative approach to using human‐AI collaboration to address students' STEM+C challenges: the teacher can use the AI‐generated timeline to guide formative feedback for students, and the researchers can leverage the teacher's feedback to help improve the multimodal timeline. Additionally, we characterize our findings with respect to two events of interest to the teacher: (1) when the students cross adifficulty threshold,and (2) thepoint of intervention, that is, when the teacher (or system) should intervene to provide effective feedback. It is important to note that the teacher explained that there should be a lag between (1) and (2) to give students a chance to resolve their own difficulties. Typically, such a lag is not implemented in computer‐based learning environments that provide feedback. Practitioner notesWhat is already known about this topicCollaborative, open‐ended learning environments enhance students' STEM+C conceptual understanding and practice, but they introduce additional complexities when students learn concepts spanning multiple domains.Recent advances in generative AI and MMLA allow for integrating multiple datastreams to derive holistic views of students' states, which can support more informed feedback mechanisms to address students' difficulties in complex STEM+C environments.Hybrid human‐AI approaches can help address collaborating students' STEM+C difficulties by combining the domain knowledge, emotional intelligence and social awareness of human experts with the general knowledge and efficiency of AI.What this paper addsWe extend a previous human‐AI collaboration framework using a hybrid intelligence approach to characterize the human component of the partnership as a researcher‐teacher partnership and present our approach as a teacher‐researcher‐AI collaboration.We adapt an AI‐generated multimodal timeline to actualize our human‐AI collaboration by pairing the timeline with videos of students encountering difficulties, engaging in active discussions with a high school teacher while watching the videos to discern the timeline's utility in the classroom.From our discussions with the teacher, we define two types ofinflection pointsto address students' STEM+C difficulties—thedifficulty thresholdand theintervention point—and discuss how thefeedback latency intervalseparating them can inform educator interventions.We discuss two ways in which our teacher‐researcher‐AI collaboration can help teachers support students encountering STEM+C difficulties: (1) teachers using the multimodal timeline to guide feedback for students, and (2) researchers using teachers' input to iteratively refine the multimodal timeline.Implications for practice and/or policyOur case study suggests that timeline gaps (ie, disengaged behaviour identified by off‐screen students, pauses in discourse and lulls in environment actions) are particularly important for identifying inflection points and formulating formative feedback.Human‐AI collaboration exists on a dynamic spectrum and requires varying degrees of human control and AI automation depending on the context of the learning task and students' work in the environment.Our analysis of this human‐AI collaboration using a multimodal timeline can be extended in the future to support students and teachers in additional ways, for example, designing pedagogical agents that interact directly with students, developing intervention and reflection tools for teachers, helping teachers craft daily lesson plans and aiding teachers and administrators in designing curricula. 

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4. Co-designing AI-based orchestration tools to support dynamic transitions: Design narratives through conjecture mapping

   Lawrence, L.; Guo, B.; Echeverria, V.; Kang, Z.; Bathala, V.; Li, C.; Huang, W.; Aleven, V.; & Rummel, N. (January 2022, Computersupported collaborative learning)

   Weinberger, A.; Chen, W.; Hernández-Leo, D.; Chen, B. (Ed.)

   Dynamically transitioning between individual and collaborative learning has been hypothesized to have positive effects, such as providing the optimal learning mode based on students’ needs. There are, however, challenges in orchestrating these transitions in real-time while managing a classroom of students. AI-based orchestration tools have the potential to alleviate some of the orchestration load for teachers. In this study, we describe a sequence of three design sessions with teachers where we refine prototypes of an orchestration tool to support dynamic transitions. We leverage design narratives and conjecture mapping for the design of our novel orchestration tool. Our contributions include the orchestration tool itself; a description of how novel tool features were revised throughout the sessions with teachers, including shared control between teachers, students, and AI and the use of AI to support dynamic transitions, and a reflection of the changes to our design and theoretical conjectures. 

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5. Empowering Latine elementary school students with disabilities: computer programming through culturally sustaining curriculum

   https://doi.org/10.1080/08993408.2024.2396749  

   Baek, Clare; Saito-Stehberger, Dana; Nam, Adam; Lopez, Gerardo; Jacob, Sharin; Warschauer, Mark (September 2024, Computer Science Education)

   Background and Context: The study was conducted in a special education classroom in an elementary school with multilingual Latine students, utilizing a computer science curriculum focused on community-based environmental literacy. Objective: This study explores the experiences of diverse elementary students with disabilities in learning computer programming and identifies instructional strategies that enhance their learning within a culturally sustaining curriculum. Method: An exploratory case study approach was used to examine students’ learning experiences and teachers’ instructional strategies during curriculum implementation. Findings: Students who typically did not engage with peers collaborated effectively, and those with behavioral and performance difficulties exhibited heightened engagement. Instructional strategies included multisensory engagement and connecting environmental and computational concepts to real-life situations. Implications: The result underscore how a culturally sustaining computer science curriculum can empower diverse students, foster inclusivity, and leverage their strengths through effective teaching practices. 

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