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1. Unpacking the Taxonomy of Wildland Fire Collaboratives in the United States West: Impact of Response Diversity on Social-Ecological Resilience

   https://doi.org/10.1007/s00267-025-02170-w  

   Srinivasan, Jaishri; Jones, Kelly; Morgan, Melinda (April 2025, Environmental Management)

   Abstract We offer the first study unpacking the taxonomy of collaboratives that undertake wildland fire management and how that taxonomy relates to resilience. We developed a comprehensive inventory totaling 133 collaboratives across twelve states in the western United States. We extracted each collaborative’s vision, mission, program goals, actions, and stakeholder composition. Based on this data we summarize temporal and spatial trends in collaborative formation and discuss formation drivers. Furthermore, we developed a cluster map of collaboratives based on patterns of co-occurrence of collaborative vision, mission, and goals. We identify distinct co-occurrence patterns of themes emerging from qualitative coding of collaborative missions, visions, and objectives, and define three distinct collaborative archetypes based on these. Finally, using theory-supported actions linked to basic, adaptive, and transformative social and ecological resilience, we code for presence or absence of these outcomes for each collaborative. We present the resilience outcomes by state and discuss how various collaborative typologies differentially impact levels of social and ecological resilience. Our study concludes that fire management actions for adaptive resilience such as fuels reduction, tree thinning, and revegetation are most numerous but that there is an emergent phenomenon of collaboratives engaging in transformative resilience that are mostly citizen-led networked organizations reshaping the social and ecological landscapes to include prescribed burning on a larger scale than present. 

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2. Intelligence Augmentation for Collaborative Learning

   Roschelle, Jeremy (January 2021, HCI International)

   null (Ed.)

   Today’s classrooms are remarkably different from those of yesteryear. In place of individual students responding to the teacher from neat rows of desks, one more typically finds students working in groups on projects, with a teacher circulating among groups. AI applications in learning have been slow to catch up, with most available technologies focusing on personalizing or adapting instruction to learners as isolated individuals. Meanwhile, an established science of Computer Supported Collaborative Learning has come to prominence, with clear implications for how collaborative learning could best be supported. In this contribution, I will consider how intelligence augmentation could evolve to support collaborative learning as well as three signature challenges of this work that could drive AI forward. In conceptualizing collaborative learning, Kirschner and Erkens (2013) provide a useful 3x3 framework in which there are three aspects of learning (cognitive, social and motivational), three levels (community, group/team, and individual) and three kinds of pedagogical supports (discourse-oriented, representation-oriented, and process-oriented). As they engage in this multiply complex space, teachers and learners are both learning to collaborate and collaborating to learn. Further, questions of equity arise as we consider who is able to participate and in which ways. Overall, this analysis helps us see the complexity of today’s classrooms and within this complexity, the opportunities for augmentation or “assistance to become important and even essential. An overarching design concept has emerged in the past 5 years in response to this complexity, the idea of intelligent augmentation for “orchestrating” classrooms (Dillenbourg, et al, 2013). As a metaphor, orchestration can suggest the need for a coordinated performance among many agents who are each playing different roles or voicing different ideas. Practically speaking, orchestration suggests that “intelligence augmentation” could help many smaller things go well, and in doing so, could enable the overall intention of the learning experience to succeed. Those smaller things could include helping the teacher stay aware of students or groups who need attention, supporting formation of groups or transitions from one activity to the next, facilitating productive social interactions in groups, suggesting learning resources that would support teamwork, and more. A recent panel of AI experts identified orchestration as an overarching concept that is an important focus for near-term research and development for intelligence augmentation (Roschelle, Lester & Fusco, 2020). Tackling this challenging area of collaborative learning could also be beneficial for advancing AI technologies overall. Building AI agents that better understand the social context of human activities has broad importance, as does designing AI agents that can appropriately interact within teamwork. Collaborative learning has trajectory over time, and designing AI systems that support teams not just with a short term recommendation or suggestion but in long-term developmental processes is important. Further, classrooms that are engaged in collaborative learning could become very interesting hybrid environments, with multiple human and AI agents present at once and addressing dual outcome goals of learning to collaborate and collaborating to learn; addressing a hybrid environment like this could lead to developing AI systems that more robustly help many types of realistic human activity. In conclusion, the opportunity to make a societal impact by attending to collaborative learning, the availability of growing science of computer-supported collaborative learning and the need to push new boundaries in AI together suggest collaborative learning as a challenge worth tackling in coming years. 

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3. A Framework to Study Human-AI Collaborative Design Space Exploration

   https://doi.org/10.1115/DETC2021-67619  

   Viros-i-Martin, Antoni; Selva, Daniel (August 2021, Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.)

   Abstract This paper presents a framework to describe and explain human-machine collaborative design focusing on Design Space Exploration (DSE), which is a popular method used in the early design of complex systems with roots in the well-known design as exploration paradigm. The human designer and a cognitive design assistant are both modeled as intelligent agents, with an internal state (e.g., motivation, cognitive workload), a knowledge state (separated in domain, design process, and problem specific knowledge), an estimated state of the world (i.e., status of the design task) and of the other agent, a hierarchy of goals (short-term and long-term, design and learning goals) and a set of long-term attributes (e.g., Kirton’s Adaption-Innovation inventory style, risk aversion). The framework emphasizes the relation between design goals and learning goals in DSE, as previously highlighted in the literature (e.g., Concept-Knowledge theory, LinD model) and builds upon the theory of common ground from human-computer interaction (e.g., shared goals, plans, attention) as a building block to develop successful assistants and interactions. Recent studies in human-AI collaborative DSE are reviewed from the lens of the proposed framework, and some new research questions are identified. This framework can help advance the theory of human-AI collaborative design by helping design researchers build promising hypotheses, and design studies to test these hypotheses that consider most relevant factors. 

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4. The Agony and Ecstasy of Extended Research on Computational Systems

   Lee, Charlotte P; Sutherland, Will; Alimohammadi, Negin; LeDoux, Ridley Jones; Neang, Andrew B (August 2025, ACM Digital Library)

   This collaborative "essay of essays" begins with an introduction by a professor of human centered design and engineering who has been working concurrently with PhD students to study collaborative system design. We undertake widely scoped qualitative research studies, that we categorize as "extended studies," that cut across units of analysis, organizations, or time. Our research explores how people create new ways to enact systems that support the knowledge work of different stakeholders. In response to an anchor essay, the students have written reflections about the multifaceted experience of doing extended studies. Many of these studies began by focusing on a particular project to develop a particular system or information infrastructure, and associated standards. Over time the studies came to center on collaborative dynamics per se, and also how collaborative dynamics shifted the scope and functionality of products, sometimes also affecting programmatic and infrastructural level changes. 

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5. Designing for learning during collaborative projects online: tools and takeaways

   https://doi.org/10.1108/ILS-04-2020-0095  

   Sankaranarayanan, Sreecharan; Kandimalla, Siddharth Reddy; Cao, Mengxin; Maronna, Ignacio; An, Haokang; Bogart, Chris; Murray, R. Charles; Hilton, Michael; Sakr, Majd; Penstein Rosé, Carolyn (June 2020, Information and Learning Sciences)

   Purpose In response to the evolving COVID-19 pandemic, many universities have transitioned to online instruction. With learning promising to be online, at least in part, for the near future, instructors may be thinking of providing online collaborative learning opportunities to their students who are increasingly isolated from their peers because of social distancing guidelines. This paper aims to provide design recommendations for online collaborative project-based learning exercises based on this research in a software engineering course at the university level. Design/methodology/approach Through joint work between learning scientists, course instructors and software engineering practitioners, instructional design best practices of alignment between the context of the learners, the learning objectives, the task and the assessment are actualized in the design of collaborative programming projects for supporting learning. The design, first segments a short real-time collaborative exercise into tasks, each with a problem-solving phase where students participate in collaborative programming, and a reflection phase for reflecting on what they learned in the task. Within these phases, a role-assignment paradigm scaffolds collaboration by assigning groups of four students to four complementary roles that rotate after each task. Findings By aligning each task with granular learning objectives, significant pre- to post-test learning from the exercise as well as each task is observed. Originality/value The roles used in the paradigm discourage divide-and-conquer tendencies often associated with collaborative projects. By requiring students to discuss conflicting ideas to arrive at a consensus implementation, their ideas are made explicit, thus providing opportunities for clarifying misconceptions through discussion and learning from the collaboration. 

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