Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistant

Wang, Qiaosi; Saha, Koustuv; Eric, Gregori,; Joyner, David; Goel, Ashok K.

doi:10.1145/3411764.3445645

Cited by 60 publications

(17 citation statements)

References 76 publications

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“…One consequence of the large-scale deployment of generative AI technologies is that they may come to replace, rather than augment human workers. Such concerns have been raised in related areas, such as the use of automated AI technologies in data science Wang et al [111,112]. Weidinger et al [113] specifically discuss the potential economic harms and inequalities that may arise as a consequence of widespread adoption of generative AI.…”

Section: Hazardous Model Outputsmentioning

confidence: 99%

Toward General Design Principles for Generative AI Applications

Weisz¹,

Müller²,

He³

et al. 2023

Preprint

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Fig. 1. Seven principles for the design of generative AI systems. Six of these principles are presented in overlapping circles, indicating their relationships to each other. One principle stands alone, the directive to design against potential harms that may be caused by a generative model's output, misuse, or other harmful effects. These principles are bounded in an environment of generative variability, in which the outputs of a generative AI application may vary in quantity, quality, character, or other characteristics.Generative AI technologies are growing in power, utility, and use. As generative technologies are being incorporated into mainstream applications, there is a need for guidance on how to design those applications to foster productive and safe use. Based on recent research on human-AI co-creation within the HCI and AI communities, we present a set of seven principles for the design of generative AI applications. These principles are grounded in an environment of generative variability. Six principles are focused on designing for characteristics of generative AI: multiple outcomes & imperfection; exploration & control; and mental models & explanations. In addition, we urge designers to design against potential harms that may be caused by a generative model's hazardous output, misuse, or potential for human displacement. We anticipate these principles to usefully inform design decisions made in the creation of novel human-AI applications, and we invite the community to apply, revise, and extend these principles to their own work.CCS Concepts: • Human-centered computing → HCI design and evaluation methods; Interaction paradigms; HCI theory, concepts and models.

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Section: Hazardous Model Outputsmentioning

confidence: 99%

Toward General Design Principles for Generative AI Applications

Weisz¹,

Müller²,

He³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…There is a growing body of work that is seeking to understand HCAI. Multiple authors (e.g., Shneiderman, 2020) and institutions (e.g., Stanford, 2022) agree on the aims of HCAI to empower rather than replace humans, for example, using conversational agents that support communities (Wang et al, 2021). However, so far, there has been little progress to operationalize Shneiderman's (2020) three key ideas to deliver more effective HCAI.…”

Section: Human-centred Ai Challengesmentioning

confidence: 99%

Designing new digital tools to augment human creative thinking at work: An application in elite sports coaching

et al. 2022

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Creative thinking is desirable in many professions. This article reports new research that followed a design science approach to develop and investigate a co-creative tool called Sport Sparks in one professionthe coaching of professional football players.In response to a coach entering a text description of a coaching challenge (e.g., struggling to maintain the fitness of an athlete) into the tool, the tool automatically generated potentially novel ideas (e.g., reducing game time and changing their nutrition) that the coach could select and/or adapt and evolve into a simple action plan (e.g., which links nutrition to increased game time). This Sport Sparks tool was designed to be an example of human-centred artificial intelligence that aspires to empower humans, to deliver high levels of human control as well as automation, and empower people rather than emulate their expertise. It was engineered with rulebased reasoning to automate the generation of potential new ideas that coaches could select and refine during interactions which provide high user control over this automation. The potential of such a co-creative tool, and value of the guidelines, were demonstrated during the tool's evaluation by coaching practitioners at a Premier League football club. The practitioners used the tool to generate new ideas to coaching challenges, and reported evidence of different forms of creative thinking, although some also reported the need for more support for creative collaborations and solution planning. The paper ends by discussing future directions for both the Sport Sparks tool and other co-creative AI tools.co-creative AI tools, creativity, design guidelines, elite coaching, human-centred AI, sports | INTRODUCTIONCreativity is defined as the ability to produce work that is novel and original, as well as appropriate and useful (Sternberg, 1999). The need for more creative thinking and outcomes is well-documented, for example, to solve complex problems (e.g., Isaksen et al., 2011) and facilitate innovation (e.g., Design Council, 2011). Digital tools to support human creativity and more creative engagement has become one of the means to meet this need (e.g., Candy, 2007). People use these tools to, for example, discover new information, synthesize novel content from existing material,

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“…Belpaeme et al (2017) argue that robots' appearance and behavior affect learning outcomes, explaining how robots have a distinct social character which influences the student responses that are conducive to learning. Wang et al (2021) suggest a "mutual theory of mind" as a theoretical framework for designing long-term human-AI interaction, again using Jill Watson as a case study. For instance, the Kruskall-Wallis tests how anthropomorphic the AI is.…”

Section: Education Technology and Tutoring In Chinamentioning

confidence: 99%

Virtual teaching assistants: A survey of a novel teaching technology

Audras

Isgar

et al. 2022

International Journal of Chinese Education

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This study centers around virtual teaching assistants (“VTA”) and the potential application of VTAs to reduce the burden on teachers across secondary schools in China exacerbated by the Double Reduction Policy. It seeks to understand how VTAs could be implemented in the classroom while ensuring student and teacher well-being, success, and happiness. Through interviews with fourteen different experts spanning industry, academia, and education, combined with extensive review of the existing literature and ecosystem of VTAs, research shows that VTAs are potentially a major aid in the context of exam review, after school tutoring, automated grading, and student performance reporting. However, they need to be created carefully with an attentive eye towards student support to ensure that the learning experience is not diminished. It must be clearly communicated to all stakeholders that the goal of a VTA is not to replace teachers, but rather to support them such that they can spend more time with students and less time on mundane tasks.

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Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistant

Cited by 60 publications

References 76 publications

Toward General Design Principles for Generative AI Applications

Toward General Design Principles for Generative AI Applications

Designing new digital tools to augment human creative thinking at work: An application in elite sports coaching

Virtual teaching assistants: A survey of a novel teaching technology

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