Zara Returns: Improved Personality Induction and Adaptation by an
            Empathetic Virtual Agent

Siddique, Farhad Bin; Kampman, Onno Pepijn; Yang, Yang; Dey, Anik; Fung, Pascale

doi:10.18653/v1/p17-4021

Cited by 15 publications

(6 citation statements)

References 32 publications

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“…Researchers have taken strides in this direction, exploring ways for software agents to mimic emotions [ 9 , 10 ], adjust personality style [ 11 ], and programmatically communicate expressions of concern [ 12 ]. Yet, more work is needed before these agents can pass anything resembling an empathic Turing test—that is, the ability to engage in empathic dialogue in ways that are indistinguishable from a real human.…”

Section: Introductionmentioning

confidence: 99%

Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions

Morris¹,

Kouddous²,

Kshirsagar³

et al. 2018

J Med Internet Res

189

104

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BackgroundConversational agents cannot yet express empathy in nuanced ways that account for the unique circumstances of the user. Agents that possess this faculty could be used to enhance digital mental health interventions.ObjectiveWe sought to design a conversational agent that could express empathic support in ways that might approach, or even match, human capabilities. Another aim was to assess how users might appraise such a system.MethodsOur system used a corpus-based approach to simulate expressed empathy. Responses from an existing pool of online peer support data were repurposed by the agent and presented to the user. Information retrieval techniques and word embeddings were used to select historical responses that best matched a user’s concerns. We collected ratings from 37,169 users to evaluate the system. Additionally, we conducted a controlled experiment (N=1284) to test whether the alleged source of a response (human or machine) might change user perceptions.ResultsThe majority of responses created by the agent (2986/3770, 79.20%) were deemed acceptable by users. However, users significantly preferred the efforts of their peers (P<.001). This effect was maintained in a controlled study (P=.02), even when the only difference in responses was whether they were framed as coming from a human or a machine.ConclusionsOur system illustrates a novel way for machines to construct nuanced and personalized empathic utterances. However, the design had significant limitations and further research is needed to make this approach viable. Our controlled study suggests that even in ideal conditions, nonhuman agents may struggle to express empathy as well as humans. The ethical implications of empathic agents, as well as their potential iatrogenic effects, are also discussed.

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Section: Introductionmentioning

confidence: 99%

Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions

Morris¹,

Kouddous²,

Kshirsagar³

et al. 2018

J Med Internet Res

189

104

View full text Add to dashboard Cite

show abstract

“…Hence, this study should be replicated or reproduced using a larger sample size of learners with diverse educational backgrounds and prior knowledge. Moreover, while this experiment obtained a homogeneous sample of novice learners drawn from business majors with no prior programming algorithm knowledge, we did not measure or control other learner characteristics such as motivation and goal orientation (Tan et al , 2020; Lallé et al , 2018), personality (Liew and Tan, 2016b; Siddique et al , 2017) and emotional states (Lallé et al , 2018) that might have influenced their responses to the pedagogical agent’s emotional expressions. Therefore, future works should assess whether these learner characteristics can confound the effects of pedagogical agents’ emotional expressions in a multimedia learning environment.…”

Section: Conclusion Limitations and Future Directionsmentioning

confidence: 99%

Can an angry pedagogical agent enhance mental effort and learning performance in a multimedia learning environment?

Liew

Tan

Kew

2022

ILS

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Purpose This study aims to examine if a pedagogical agent’s expressed anger, when framed as a feedback cue, can enhance mental effort and learning performance in a multimedia learning environment than expressed happiness. Design/methodology/approach A between-subjects experiment was conducted in which learners engaged with a multimedia learning material that taught programming algorithms, featuring a pedagogical agent who expressed anger or happiness as a feedback cue in response to the learners’ prior performance. Learners completed a self-reported scale and post-test for measuring mental effort and learning performance, respectively. Findings Female learners reported higher mental effort and had better learning performance when the pedagogical agent expressed anger than happiness. Male learners reported marginally lower mental effort when the pedagogical agent expressed anger than happiness. Originality/value This study focuses on a pedagogical agent’s expressed emotion as social information to learners. Extending from research advocating a pedagogical agent’s positive emotional expression, this study highlights the potential benefits of a pedagogical agent’s negative emotional expression, such as anger, as a cue for learners to enhance learning effort and performance in a multimedia learning environment.

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“…Artificial personality detection is also used in more novel domains where traditional assessments are not a viable option. Detection of personality can be one of the features of artificial agents such as customer assistant chatbots (Siddique et al, 2017), conversational Agents such as amazon's Alexa (Ram et al, 2018), trust-building virtual agents (Zhou et al, 2019), or interactive social robots (Celiktutan et al, 2019). Also, in this case, the integrated framework can help to improve and understand the performance of the models and to increase generalizability.…”

Section: Challenges To Integrationmentioning

confidence: 99%

Toward an integrative approach to nonverbal personality detection: Connecting psychological and artificial intelligence research.

Cannata¹,

Breil²,

Lepri³

et al. 2022

Technology, Mind, and Behavior

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Our first impressions of the people we meet are the subject of considerable interest, academic, and nonacademic. Such initial estimates of another's personality (e.g., their sociality or agreeableness) are vital, since they enable us to predict the outcomes of interactions (e.g., can we trust them?). Nonverbal behaviors are a key medium through which personality is expressed and detected. The character and reliability of these expression and detection processes have been investigated within two major fields: psychological research on personality judgments accuracy and Artificial Intelligence research on personality computing. Communication between these fields has, however, been infrequent. In the present perspective, we summarize the contributions and open questions of both fields and propose an integrative approach to combine their strengths and overcome their limitations. The integrated framework will enable novel research programs, such as (a), identifying which detection tasks better suit humans or computers, (b), harmonizing the nonverbal features extracted by humans and computers, and (c), integrating human and artificial agents in hybrid systems.

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Zara Returns: Improved Personality Induction and Adaptation by an Empathetic Virtual Agent

Cited by 15 publications

References 32 publications

Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions

Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions

Can an angry pedagogical agent enhance mental effort and learning performance in a multimedia learning environment?

Toward an integrative approach to nonverbal personality detection: Connecting psychological and artificial intelligence research.

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