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1. Pair-Up: Prototyping Human-AI Co-orchestration of Dynamic Transitions between Individual and Collaborative Learning in the Classroom

   https://doi.org/10.1145/3544548.3581398  

   Yang, Kexin Bella ; Echeverria, Vanessa ; Lu, Zijing ; Mao, Hongyu ; Holstein, Kenneth ; Rummel, Nikol ; Aleven, Vincent ( April 2023 , CHI ‘23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems)

   Schmidt, A. ; Väänänen, K. ; Goyal, T. ; Kristensson, P. O. ; Peters, A. ; Mueller, S. ; Williamson, J. R. ; Wilson, M. L. (Ed.)

   Enabling students to dynamically transition between individual and collaborative learning activities has great potential to support better learning. We explore how technology can support teachers in orchestrating dynamic transitions during class. Working with five teachers and 199 students over 22 class sessions, we conducted classroom-based prototyping of a co-orchestration technology ecosystem that supports the dynamic pairing of students working with intelligent tutoring systems. Using mixed-methods data analysis, we study the resulting observed classroom dynamics, and how teachers and students perceived and experienced dynamic transitions as supported by our technology. We discover a potential tension between teachers’ and students’ preferred level of control: students prefer more control over the dynamic transitions that teachers are hesitant to grant. Our study reveals design implications and challenges for future human-AI co-orchestration in classroom use, bringing us closer to realizing the vision of highly-personalized smart classrooms that can address the unique needs of each student. 

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2. Boxcars on Potatoes: Exploring the Design Language for Tangible Visualizations of Scalar Data Fields on 3D Surfaces

   Herman, Bridger ; Keefe, Daniel F. ( October 2018 , Proceedings of the IEEE VIS 2018 Workshop: Toward a Design Language for Data Physicalization)

   We present a design-based exploration of the potential to reinterpret glyph-based visualization of scalar fields on 3D surfaces, a traditional scientific visualization technique, as a data physicalization technique. Even with the best virtual reality displays, users often struggle to correctly interpret spatial relationships in 3D datasets; thus, we are motivated to understand the extent to which traditional scientific visualization methods can translate to physical media where users may simultaneously leverage their visual systems and tactile senses to, in theory, better understand and connect with the data of interest. This pictorial traces the process of our design for a specific user study experiment: (1) inspiration, (2) exploring the data physicalization design space, (3) prototyping with 3D printing, (4) applying the techniques to different synthetic datasets. We call our most recent and compelling visual/tactile design boxcars on potatoes, and the next step in the research is to run a user-based evaluation to elucidate how this design compares to several of the others pictured here. 

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3. How teachers conceptualise shared control with an AI co‐orchestration tool: A multiyear teacher‐centred design process

   https://doi.org/10.1111/bjet.13372  

   Lawrence, LuEttaMae ; Echeverria, Vanessa ; Yang, Kexin ; Aleven, Vincent ; Rummel, Nikol ( August 2023 , British Journal of Educational Technology)

   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, calledPair‐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 withPair‐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.

   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.

    

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4. Dissecting 3D Printing for Engineering Design Process Education of High School Preservice Teachers

   Weihang Zhu, Mariam Manuel ( June 2023 , American Society of Engineering Education Annual Conference 2023)

   3D printing (3DP) has been becoming more and more popular throughout the education system from Kindergarten to University. High school is a critical period for students to decide their imminent university major selection which in turn will impact their future career choices. High school students are usually intrigued by hands-on tool such as 3DP which is also an important contributor to other courses such as robotics. The recent years have seen more investment and availability of 3DP in high schools, especially Career and Technical Education (CTE) programs. However, mere availability of 3DP is not enough for teachers to fully utilize its potential in their classrooms. While basic 3DP skills can be obtained through a few hours of training, the basic training is insufficient to ensure effective teaching Engineering Design Process (EDP) at the high school level. To address this problem, this project develops an EDP course tightly integrated with 3DP for preservice teachers (PST) who are going to enter the workforce in high schools. Engineering design process (EDP) has become an essential part for preservice teachers (PST), especially for high school STEM. 3DP brought transformative change to EDP which is an iterative process that needs virtual/physical prototyping. The new PST course on EDP will be purposefully integrated with an in-depth discussion of 3DP. The approach is to dissect a 3D printer’s hardware, explain each component’s function, introduce each component’s manufacturing methods, describe possible defects, and elucidate what works and what does not. This has at least four benefits: 1) PSTs will know what is possibly wrong when a printer or printing process fails, 2) PSTs will learn more manufacturing processes besides 3DP that can be used to support engineering design prototyping, 3) PSTs will know how to design something that can meet the manufacturing constraints, i.e., can be actually fabricated, and 4) reduce errors and frustrations caused by failed design and failed prints which happen frequently to novices in 3DP. After graduation, PSTs will bring the knowledge to their future high schools and will be more confident in teaching engineering design, reverse engineering, prototype development, manufacturing, and technology. The developed course will be implemented and assessed in a future semester. 

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5. The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms

   https://doi.org/https://doi.org/10.1145/3313831.3376277  

   Pengcheng An, Kenneth Holstein ( April 2020 , CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   Recently, the HCI community has seen increased interest in the design of teaching augmentation (TA): tools that extend and complement teachers' pedagogical abilities during ongoing classroom activities. Examples of TA systems are emerging across multiple disciplines, taking various forms: e.g., ambient displays, wearables, or learning analytics dashboards. However, these diverse examples have not been analyzed together to derive more fundamental insights into the design of teaching augmentation. Addressing this opportunity, we broadly synthesize existing cases to propose the TA framework. Our framework specifies a rich design space in five dimensions, to support the design and analysis of teaching augmentation. We contextualize the framework using existing designs cases, to surface underlying design trade-offs: for example, balancing actionability of presented information with teachers' needs for professional autonomy, or balancing unobtrusiveness with informativeness in the design of TA systems. Applying the TA framework, we identify opportunities for future research and design. 

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