A cautionary tale about the impact of AI on human design teams

Zhang, Guanglu; Raina, Ayush; Cagan, Jonathan; McComb, Christopher

doi:10.1016/j.destud.2021.100990

Cited by 65 publications

(39 citation statements)

References 16 publications

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“…Design. Five studies are related to design teams supported by AI systems, e.g., focusing on the creation of mood boards ("visual collages composed of images, text, and objects, that express concepts, ideas and emotions" [24]) or on engineers designing bridges [25] or drones [26]. While the humans are in the lead, the AI system provides suggestion for further ideas or improvements.…”

Section: Turn-based Cooperative Gamesmentioning

confidence: 99%

“…While the humans are in the lead, the AI system provides suggestion for further ideas or improvements. AI systems were chosen to support in those research contexts due to their high task-specific performance expected to be effective when combined with human creativity and agility [25] and their ability to support the entire creative process including inspiration and reflection [24].…”

Section: Turn-based Cooperative Gamesmentioning

confidence: 99%

“…mission success [38] PB: Protecting or sacrificing teammate [22,23] AP: Player identity (humanness) [31], perceived helpfulness, efficiency [37], trust, understandability [38] I-TC: AI vs. human [31,36] I-TC: AI vs. presumed-human [22,23] I-E: Score visible [23] M-TP: Communication: different modalities [36]/ explanation types [37,38]/ skill, reliability [38] Dependability: Visibility of score on treatment of AI teammate [23] Humans act differently in presumed social context [22] Online players value co-presence [36] Positive effect: Staying close to other player on correct identification (AI or human) [31] Positive effect: explanation on non-experts' performance [37]/on performance if facilitating decision-making [38] Design Mood board-, bridge-, drone-, accessory-, game level-design [24-26, 39, 40] PO: Strength mass ratio [25], quality, novelty [40], range, velocity, payload, cost [26] PB: Design effort, search strategy, mental workload [26], solution exploration [24] AP: Usefulness, inspirational [24], fun,…”

Section: Real-time Cooperative Games and Simulationsmentioning

confidence: 99%

See 2 more Smart Citations

Complex Problem Solving through Human-AI Collaboration: Literature Review on Research Contexts

Memmert¹,

Bittner²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Solving complex problems has been proclaimed as one major challenge for hybrid teams of humans and artificial intelligence (AI) systems. Human-AI collaboration brings immense opportunities in these complex tasks, in which humans struggle, but full automation is also impossible. Understanding and designing human-AI collaboration for complex problem solving is a wicked and multifaceted research problem itself. We contribute to this emergent field by reviewing to what extent existing research on instantiated human-AI collaboration already addresses this challenge. After clarifying the two key concepts (complex problem solving and human-AI collaboration), we perform a systematic literature review. We extract research contexts and assess them considering different complexity features. We thereby provide an overview of existing and guidance for designing new, suitable research contexts for studying complex problem solving through human-AI collaboration and present an outlook for further work on this research challenge.

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Section: Turn-based Cooperative Gamesmentioning

confidence: 99%

Section: Turn-based Cooperative Gamesmentioning

confidence: 99%

Section: Real-time Cooperative Games and Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

Complex Problem Solving through Human-AI Collaboration: Literature Review on Research Contexts

Memmert¹,

Bittner²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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show abstract

“…By controlling teams' access to the AI agents, the experiment consists of four experimental conditions: teams with no access to the AI agents (humanonly teams) and those with access to the AI agents (AI-assisted human teams), and whether the teams start with the open or restrictive communication structures in the initial design session. In total, participants are randomly assigned to 44 teams with 11 teams in each of the experimental conditions (Zhang et al, 2021).…”

Section: Drone Design Studymentioning

confidence: 99%

Communication in Ai-Assisted Teams During an Interdisciplinary Drone Design Problem

et al. 2021

Self Cite

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Human-artificial intelligent (AI) - assisted teaming is becoming a strategy for coalescing the complementary strengths of humans and computers to solve difficult tasks. Yet, there is still much to learn regarding how the integration of humans with AI agents into a team affects human behavior. Accordingly, this work begins to inform this research gap by focusing specifically on how the communication structure and interaction changes within AI-assisted human teams. The underlying discourse data for this work originates from a prior research study in which teams solve an interdisciplinary drone design and path-planning problem. Several metrics are employed in this work to study team discourse, including count, diversity, content richness, and semantic coherence. Results show significant differences in communication behavior in AI-assisted teams including more diversity and frequency in communication, more exchange of information regarding principal design parameters and problem-solving strategies, and more cohesion. Overall, this work takes meaningful steps towards understanding the effects of AI agents on human behavior in teams, critical for fully building effective human-AI hybrid teams in the future.

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“…A study by Zhang et al [46] reveals that AI negatively impacts the performance of high-performing design teams by lowering the mental demand of the human designers since they tend to overestimate their control over uncontrollable events.…”

Section: Limitations Of Ai In Engineering Designmentioning

confidence: 99%

A Pilot Study on the Prevalence of Artificial Intelligence in Canadian Engineering Design Curricula

Khanolkar

Gad

Liao

et al. 2021

PCEEA

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Recent advances in artificial intelligence (AI) have shed light on the potential uses and applications of AI tools in engineering design. However, the aspiration of a fully automated engineering design process still seems out of reach of AI’s current capabilities, and therefore, the need for human expertise and cognitive skills persists. Nonetheless, a collaborative design process that emphasizes and uses the strengths of both AI and human engineers is an appealing direction for AI in design. Touncover the current applications of AI, the authors review literature pertaining to AI applications in design research and engineering practice. This highlights the importance of integrating AI education into engineering design curricula in post-secondary institutions. Next, a pilot studyassessment of undergraduate mechanical engineering course descriptions at the University of Waterloo and University of Toronto reveals that only one out of a total of 153 courses provides both AI and design-related knowledge together in a course. This result identifies possible gaps in Canadian engineering curricula and potential deficiencies in the skills of graduating Canadianengineers.

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A cautionary tale about the impact of AI on human design teams

Cited by 65 publications

References 16 publications

Complex Problem Solving through Human-AI Collaboration: Literature Review on Research Contexts

Complex Problem Solving through Human-AI Collaboration: Literature Review on Research Contexts

Communication in Ai-Assisted Teams During an Interdisciplinary Drone Design Problem

A Pilot Study on the Prevalence of Artificial Intelligence in Canadian Engineering Design Curricula

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