In this paper, we report on our investigation of human-AI collaboration for mind-mapping. We specifically focus on problem exploration in pre-conceptualization stages of early design. Our approach leverages the notion of query expansion—the process of refining a given search query for improving information retrieval. Assuming a mind-map as a network of nodes, we reformulate its construction process as a sequential interaction workflow wherein a human user and an intelligent agent take turns to add one node to the network at a time. Our contribution is the design, implementation, and evaluation of algorithm that powers the intelligent agent (IA). This paper is an extension of our prior work (Chen et al., 2019, “Mini-Map: Mixed-Initiative Mind-Mapping Via Contextual Query Expansion,” AIAA Scitech 2020 Forum, p. 2347) wherein we developed this algorithm, dubbed Mini-Map, and implemented a web-based workflow enabled by ConceptNet (a large graph-based representation of “commonsense” knowledge). In this paper, we extend our prior work through a comprehensive comparison between human-AI collaboration and human-human collaboration for mind-mapping. We specifically extend our prior work by: (a) expanding on our previous quantitative analysis using established metrics and semantic studies, (b) presenting a new detailed video protocol analysis of the mind-mapping process, and (c) providing design implications for digital mind-mapping tools.

In this paper, we investigate blended physical-digital kinesthetic feedback (or blended haptics in short) as a means for controlled three-dimensional design ideation in mixed reality (MR) environments. We define blended haptics as a spatial interaction wherein a physical object (e.g. a 3D printed human head) provides a specific design context for a user to generate ideas through physical manipulation of and on the object (e.g. drawing a digital sketch of a helmet) in the virtual environment. Using 3D wire-frame modeling as a concrete digital prototyping context, we investigate this idea of blended haptics in terms of how it supports design cognition specifically in spatial user interfaces. For this, we implemented a modeling tool as an experimental setup that allows a user to directly create curve-networks (wire-frame models) on a physical object (i.e. a contextual proxy) with one hand while simultaneously controlling the object with the other hand using a tracked turn-table. The key idea is that the user can simultaneously experience kinesthetic feedback from both the physical objects as well as the digital wire-frame models. To systematically investigate our approach, we conducted a comparative user evaluation of conceptual design tasks performed by two groups of users, one with the blended haptics (e.g. physical head and digital helmet) and the other with purely digital haptic feedback (e.g. digital head and digital helmet). Our study shows that blended haptics required less physical effort in the design task and resulted in concepts with higher novelty score as compared to using purely digital haptic feedback.