Abstract

In the early stages of the design process, designers explore opportunities by discovering unmet needs and developing innovative concepts as potential solutions. From a human-centered design perspective, designers must develop empathy with people to truly understand their experiences and needs. However, developing empathy is a complex and subjective process that relies heavily on the designer's empathic capability, and is often subject to the experiences of a small group of people. Therefore, the development of empathic understanding is intuitive, and the discovery of underlying needs can be serendipitous and unrepresentative. This paper aims to provide insights from artificial intelligence research to indicate the future direction of AI-driven human-centered design, considering the essential role of empathy. Specifically, we conduct an interdisciplinary investigation of research areas such as data-driven user research, empathic design, and artificial empathy. Based on this foundation, we discuss the role that artificial empathy can play in human-centered design and propose an artificial empathy framework for human-centered design. Building on the mechanisms behind empathy and insights from empathic design research, the framework aims to break down the rather complex and subjective process of developing empathic understanding into modules and components that can potentially be modeled computationally. Furthermore, we discuss the expected benefits of developing such systems and identify research opportunities to suggest future research efforts.

References

1.
Brown
,
T.
,
2008
, “
Design Thinking
,”
Harv. Bus. Rev.
,
86
(
6
), p.
84
.
2.
Altshuller
,
G.
,
Shulyak
,
L.
,
Rodman
,
S.
, and
Fedoseev
,
U.
,
1998
,
40 Principles: TRIZ Keys to Innovation
, Vol.
1
,
Technical Innovation Center Inc.
,
Worcester, MA
.
3.
Yilmaz
,
S.
,
Daly
,
S. R.
,
Seifert
,
C. M.
, and
Gonzalez
,
R.
,
2016
, “
Evidence-Based Design Heuristics for Idea Generation
,”
Des Stud.
,
46
, pp.
95
124
.
4.
Luo
,
J.
,
2022
, “
Data-Driven Innovation: What Is It?
,”
IEEE Trans. Eng. Manag.
,
70
(
2
), pp.
784
790
.
5.
Jiang
,
S.
,
Hu
,
J.
,
Wood
,
K. L.
, and
Luo
,
J.
,
2022
, “
Data-Driven Design-by-Analogy: State-of-the-Art and Future Directions
,”
ASME J. Mech. Des.
,
144
(
2
), p.
020801
.
6.
Camburn
,
B.
,
He
,
Y.
,
Raviselvam
,
S.
,
Luo
,
J.
, and
Wood
,
K.
,
2020
, “
Machine Learning-Based Design Concept Evaluation
,”
ASME J. Mech. Des.
,
142
(
3
), p.
031113
.
7.
Luo
,
J.
,
Yan
,
B.
, and
Wood
,
K.
,
2017
, “
InnoGPS for Data-Driven Exploration of Design Opportunities and Directions: The Case of Google Driverless Car Project
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111416
.
8.
Zhu
,
Q.
, and
Luo
,
J.
,
2023
, “
Generative Transformers for Design Concept Generation
,”
ASME J. Comput. Inf. Sci. Eng.
,
23
(
4
), p.
041003
.
9.
Zhu
,
Q.
,
Zhang
,
X.
, and
Luo
,
J.
,
2023
, “
Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers
,”
ASME J. Mech. Des.
,
145
(
4
), p.
041409
.
10.
Luo
,
J.
,
Sarica
,
S.
, and
Wood
,
K. L.
,
2021
, “
Guiding Data-Driven Design Ideation by Knowledge Distance
,”
Knowl. Based Syst.
,
218
, p.
106873
.
11.
Sarica
,
S.
,
Song
,
B.
,
Luo
,
J.
, and
Wood
,
K. L.
,
2021
, “
Idea Generation With Technology Semantic Network
,”
AI EDAM
,
35
(
3
), pp.
265
283
.
12.
Kelley
,
D.
,
2015
,
The Field Guide to Human-Centered Design
, http://www.designkit.org/resources/1
13.
Siddharth
,
L.
,
Blessing
,
L.
, and
Luo
,
J.
,
2022
, “
Natural Language Processing In-and-for Design Research
,”
Des. Sci.
,
8
, p.
E21
.
14.
Yüksel
,
N.
,
Börklü
,
H. R.
,
Sezer
,
H. K.
, and
Canyurt
,
O. E.
,
2023
, “
Review of Artificial Intelligence Applications in Engineering Design Perspective
,”
Eng. Appl. Artif. Intell.
,
118
, p.
105697
.
15.
Sanders
,
E. B. N.
,
2002
, “From User-Centered to Participatory Design Approaches,”
Design and the Social Sciences
,
J.
Frascara
, ed.,
CRC Press
,
London, UK
, pp.
18
25
.
16.
Visser
,
F. S.
,
Stappers
,
P. J.
,
Van der Lugt
,
R.
, and
Sanders
,
E. B.
,
2005
, “
Contextmapping: Experiences From Practice
,”
CoDesign
,
1
(
2
), pp.
119
149
.
17.
Norman
,
D. A.
,
2013
,
The Design of Everyday Things: Revised and Expanded Edition
,
Basic Books
,
New York
.
18.
Kouprie
,
M.
, and
Visser
,
F. S.
,
2009
, “
A Framework for Empathy in Design: Stepping Into and Out of the User's Life
,”
J. Eng. Des.
,
20
(
5
), pp.
437
448
.
19.
McDonagh
,
D.
, and
Thomas
,
J.
,
2010
, “
Rethinking Design Thinking: Empathy Supporting Innovation
,”
Aust. Med. J.
,
3
(
8
), pp.
458
464
.
20.
Surma-Aho
,
A.
, and
Hölttä-Otto
,
K.
,
2022
, “
Conceptualization and Operationalization of Empathy in Design Research
,”
Des. Stud.
,
78
, p.
101075
.
21.
Mattelmäki
,
T.
,
2006
,
Design Probes
.
Doctoral dissertation
,
Aalto University
. https://aaltodoc.aalto.fi/bitstream/handle/123456789/11829/isbn9515582121.pdf
22.
Sanders
,
E. B. N.
, and
Stappers
,
P. J.
,
2008
, “
Co-Creation and the New Landscapes of Design
,”
CoDesign
,
4
(
1
), pp.
5
18
.
23.
Mattelmäki
,
T.
, and
Visser
,
F. S.
,
2011
, “
Lost in Co-X-Interpretations of Co-Design and Co-Creation
,”
Proceedings of IASDR'11, 4th World Conference on Design Research
,
Delft, The Netherlands
,
Oct. 31–Nov. 4
.
24.
Li
,
J.
, and
Hölttä-Otto
,
K.
,
2023
, “
Inconstant Empathy—Interpersonal Factors That Influence the Incompleteness of User Understanding
,”
ASME J. Mech. Des.
,
145
(
2
), p.
021403
.
25.
Mattelmäki
,
T.
,
Vaajakallio
,
K.
, and
Koskinen
,
I.
,
2014
, “
What Happened to Empathic Design?
,”
Des. Issues
,
30
(
1
), pp.
67
77
.
26.
Yalçın
,
Ӧ. N.
, and
DiPaola
,
S.
,
2018
, “
A Computational Model of Empathy for Interactive Agents
,”
Biol. Inspired Cogn. Archit.
,
26
, pp.
20
25
.
27.
Yalçın
,
Ö. N.
, and
DiPaola
,
S.
,
2020
, “
Modeling Empathy: Building a Link Between Affective and Cognitive Processes
,”
Artif. Intell. Rev.
,
53
(
4
), pp.
2983
3006
.
28.
Asada
,
M.
,
2015
, “
Development of Artificial Empathy
,”
Neurosci. Res.
,
90
, pp.
41
50
.
29.
Asada
,
M.
,
2015
, “
Towards Artificial Empathy: How Can Artificial Empathy Follow the Developmental Pathway of Natural Empathy?
,”
Int. J. Soc. Robot.
,
7
(
1
), pp.
19
33
.
30.
Rossi
,
P. G.
, and
Fedeli
,
L.
,
2015
, “
Empathy, Education and AI
,”
Int. J. Soc. Robot.
,
7
(
1
), pp.
103
109
.
31.
Pepito
,
J. A.
,
Ito
,
H.
,
Betriana
,
F.
,
Tanioka
,
T.
, and
Locsin
,
R. C.
,
2020
, “
Intelligent Humanoid Robots Expressing Artificial Humanlike Empathy in Nursing Situations
,”
Nurs. Philos.
,
21
(
4
), p.
e12318
.
32.
Liu-Thompkins
,
Y.
,
Okazaki
,
S.
, and
Li
,
H.
,
2022
, “
Artificial Empathy in Marketing Interactions: Bridging the Human-AI Gap in Affective and Social Customer Experience
,”
J. Acad. Mark. Sci.
,
50
(
6
), pp.
1198
1218
.
33.
Ma
,
L.
, and
Sun
,
B.
,
2020
, “
Machine Learning and AI in Marketing–Connecting Computing Power to Human Insights
,”
Int. J. Res. Mark.
,
37
(
3
), pp.
481
504
.
34.
Saura
,
J. R.
,
Ribeiro-Soriano
,
D.
, and
Palacios-Marqués
,
D.
,
2021
, “
From User-Generated Data to Data-Driven Innovation: A Research Agenda to Understand User Privacy in Digital Markets
,”
Int. J. Inf. Manag.
,
60
, p.
102331
.
35.
Timoshenko
,
A.
, and
Hauser
,
J. R.
,
2019
, “
Identifying Customer Needs From User-Generated Content
,”
Market. Sci.
,
38
(
1
), pp.
1
20
.
36.
Zhou
,
F.
,
Jiao
,
J. R.
,
Yang
,
X. J.
, and
Lei
,
B.
,
2017
, “
Augmenting Feature Model Through Customer Preference Mining by Hybrid Sentiment Analysis
,”
Expert Syst. Appl.
,
89
, pp.
306
317
.
37.
Salminen
,
J.
,
Guan
,
K.
,
Jung
,
S. G.
, and
Jansen
,
B. J.
,
2021
, “
A Survey of 15 Years of Data-Driven Persona Development
,”
Int. J. Hum. Comput. Interact.
,
37
(
18
), pp.
1685
1708
.
38.
Wang
,
X.
,
Liu
,
A.
, and
Kara
,
S.
,
2023
, “
Constructing Product Usage Context Knowledge Graph Using User-Generated Content for User-Driven Customization
,”
ASME J. Mech. Des.
,
145
(
4
), p.
041404
.
39.
Batbaatar
,
E.
,
Li
,
M.
, and
Ryu
,
K. H.
,
2019
, “
Semantic-Emotion Neural Network for Emotion Recognition From Text
,”
IEEE Access
,
7
, pp.
111866
111878
.
40.
Poria
,
S.
,
Cambria
,
E.
,
Hazarika
,
D.
,
Majumder
,
N.
,
Zadeh
,
A.
, and
Morency
,
L. P.
,
2017
, “
Context-dependent Sentiment Analysis in User-Generated Videos
,”
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
,
Vancouver, Canada
,
July 30–Aug. 4
, pp.
873
883
.
41.
Liu
,
A.
,
Wang
,
Y.
, and
Wang
,
X.
,
2022
,
“User-Generated Content Analysis for Customer Needs Elicitation,” Data-Driven Engineering Design, Springer, Cham
.
42.
Hu
,
N.
,
Pavlou
,
P. A.
, and
Zhang
,
J.
,
2017
,
On Self-Selection Biases in Online Product Reviews
.
MIS Q.
,
41
(
2
), pp.
449
475
.
43.
Bender
,
E. M.
, and
Friedman
,
B.
,
2018
, “
Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science
,”
Trans. ACL
,
6
, pp.
587
604
.
44.
Wang
,
G.
,
Zhang
,
X.
,
Tang
,
S.
,
Zheng
,
H.
, and
Zhao
,
B. Y.
,
2016
, “
Unsupervised Clickstream Clustering for User Behavior Analysis
,”
Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
,
San Jose, CA
,
May 7–12
, pp.
225
236
.
45.
García
,
M. D. M. R.
,
García-Nieto
,
J.
, and
Aldana-Montes
,
J. F.
,
2016
, “
An Ontology-Based Data Integration Approach for Web Analytics in e-Commerce
,”
Expert Syst. Appl.
,
63
, pp.
20
34
.
46.
Dixon
,
A.
,
Liu
,
Y.
, and
Setchi
,
R.
, “
Computer-Aided Ethnography in Engineering Design." Proceedings of the ASME 2016 IDETC-CIE. Volume 7
,”
28th International Conference on Design Theory and Methodology
,
Charlotte, NC
,
Aug. 21–24
,
ASME
, p. V007T06A007.
47.
Köppen
,
E.
, and
Meinel
,
C.
,
2015
, “Empathy via Design Thinking: Creation of Sense and Knowledge,”
Design Thinking Research. Understanding Innovation
,
H.
Plattner
,
C.
Meinel
, and
L.
Leifer
, eds.,
Springer
,
Cham
.
48.
Holtzblatt
,
K.
, and
Beyer
,
H.
,
1997
,
Contextual Design: Defining Customer-Centered Systems
,
Elsevier
,
New York
.
49.
Aktinson
,
P.
, and
Hammersley
,
M.
,
1998
, “Ethnography and Participant Observation. Strategies of Qualitative Inquiry,”
Strategies of Qualitative Inquiry
,
A.
Watson
, and
K. E.
Till
, eds.,
Sage
,
Thousand Oaks, CA
, pp.
248
261
.
50.
Laurans
,
G.
,
Desmet
,
P. M. A.
, and
Hekkert
,
P.
,
2009
, “
Assessing Emotion in Interaction: Some Problems and a New Approach
,”
Proceedings of the 4th International Conference on Designing Pleasurable Products and Interfaces
,
Compiegne, France
,
Oct. 13–16
, pp.
230
239
.
51.
Osgood
,
C. E.
,
1964
, “
Semantic Differential Technique in the Comparative Study of Cultures
,”
Am. Anthropol.
,
66
(
3
), pp.
171
200
.
52.
Boess
,
S.
,
2006
, “
Rationales for Role Playing in Design
,”
Proceedings of the Wonderground—DRS International Conference 2006
,
K.
Friedman
,
T.
Love
,
E.
Côrte-Real
, and
C.
Rust
, eds.,
Nov. 1–4
,
Lisbon, Portugal
.
53.
Asher
,
T.
,
Ogle
,
E.
,
Bailenson
,
J.
, and
Herrera
,
F. F.
,
2018
, “
Becoming Homeless: A Human Experience
,”
ACM SIGGRAPH 2018 Virtual, Augmented, and Mixed Reality
,
Vancouver, British Columbia, Canada
,
Aug. 12–16
, p.
1
.
54.
Hess
,
J. L.
, and
Fila
,
N. D.
,
2016
, “
The Manifestation of Empathy Within Design: Findings From a Service-Learning Course
,”
CoDesign
,
12
(
1–2
), pp.
93
111
.
55.
Walther
,
J.
,
Miller
,
S. E.
, and
Sochacka
,
N. W.
,
2017
, “
A Model of Empathy in Engineering as a Core Skill, Practice Orientation, and Professional Way of Being. J
,”
Eng. Educ.
,
106
(
1
), pp.
123
148
.
56.
Smeenk
,
W.
,
Tomico
,
O.
, and
van Turnhout
,
K.
,
2016
, “
A Systematic Analysis of Mixed Perspectives in Empathic Design: Not One Perspective Encompasses all
,”
Int. J. Des.
,
10
(
2
), pp.
31
48
.
57.
Oygür
,
I.
,
2018
, “
The Machineries of User Knowledge Production
,”
Des. Stud.
,
54
, pp.
23
49
.
58.
Fila
,
N. D.
, and
Hess
,
J. L.
,
2014
, “Exploring the Role of Empathy in a Service-Learning Design Project,”
Analyzing Design Review Conversations
,
R. S.
Adams
,
P.
Buzzanell
, and
J. A.
Siddiqui
, eds,
Purdue University Press
,
West Lafayette, IN
, pp.
135
154
.
59.
Sanders
,
E. B. N.
, and
Stappers
,
P. J.
,
2014
, “
Probes, Toolkits and Prototypes: Three Approaches to Making in Codesigning
,”
CoDesign
,
10
(
1
), pp.
5
14
.
60.
Norman
,
D. A.
,
2004
,
Emotional Design: Why we love (or hate) Everyday Things
,
Basic Books
,
New York
.
61.
Mattelmäki
,
T.
,
Routarinne
,
S.
, and
Ylirisku
,
S.
,
2011
, “
Triggering the Storytelling Mode
,”
Proceedings of the Participatory Innovation Conference
,
Sonderborg, Denmark
,
Jan. 13–15
, pp.
38
44
.
62.
Goldman
,
A. I.
,
2006
,
Simulating Minds: The Philosophy, Psychology, and Neuroscience of Mindreading
,
Oxford University Press
,
New York
.
63.
Omdahl
,
B. L.
,
2014
,
Cognitive Appraisal, Emotion, and Empathy
,
Psychology Press
,
New York
.
64.
Preston
,
S. D.
, and
De Waal
,
F. B.
,
2002
, “
Empathy: Its Ultimate and Proximate Bases
,”
Behav. Brain. Sci.
,
25
(
1
), pp.
1
20
.
65.
Nummenmaa
,
L.
,
Hirvonen
,
J.
,
Parkkola
,
R.
, and
Hietanen
,
J. K.
,
2008
, “
Is Emotional Contagion Special? An fMRI Study on Neural Systems for Affective and Cognitive Empathy
,”
Neuroimage
,
43
(
3
), pp.
571
580
.
66.
Lim
,
A.
, and
Okuno
,
H. G.
,
2015
, “
A Recipe for Empathy: Integrating the Mirror System, Insula, Somatosensory Cortex and Motherese
,”
Int. J. Soc. Robot.
,
7
(
1
), pp.
35
49
.
67.
Carr
,
L.
,
Iacoboni
,
M.
,
Dubeau
,
M. C.
,
Mazziotta
,
J. C.
, and
Lenzi
,
G. L.
,
2003
, “
Neural Mechanisms of Empathy in Humans: A Relay From Neural Systems for Imitation to Limbic Areas
,”
Proc. Nat. Acad. Sci.
,
100
(
9
), pp.
5497
5502
.
68.
Batson
,
C. D.
,
2009
, “These Things Called Empathy: Eight Related but Distinct Phenomena.”
The Social Neuroscience of Empathy
,
J.
Decety
, and
W.
Ickes
, eds.,
The MIT Press
,
Cambridge, MA
, pp.
3
15
.
69.
Harwood
,
M. D.
, and
Farrar
,
M. J.
,
2006
, “
Conflicting Emotions: The Connection Between Affective Perspective Taking and Theory of Mind
,”
Br. J. Dev. Psychol.
,
24
(
2
), pp.
401
418
.
70.
Frith
,
C.
, and
Frith
,
U.
,
2005
, “
Theory of Mind
,”
Curr. Biol.
,
15
(
17
), pp.
644
645
.
71.
De Waal
,
F. B.
,
2008
, “
Putting the Altruism Back Into Altruism: The Evolution of Empathy
,”
Annu. Rev. Psychol.
,
59
(
1
), pp.
279
300
.
72.
Zeng
,
Z.
,
Pantic
,
M.
,
Roisman
,
G. I.
, and
Huang
,
T. S.
,
2009
, “
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
,”
IEEE Trans. Pattern Anal. Mach. Intell.
,
31
(
1
), pp.
39
58
.
73.
Poria
,
S.
,
Cambria
,
E.
,
Bajpai
,
R.
, and
Hussain
,
A.
,
2017
, “
A Review of Affective Computing: From Unimodal Analysis to Multimodal Fusion
,”
Inf. Fusion
,
37
, pp.
98
125
.
74.
Busso
,
C.
,
Deng
,
Z.
,
Yildirim
,
S.
,
Bulut
,
M.
,
Lee
,
C. M.
,
Kazemzadeh
,
A.
,
Lee
,
S.
,
Neumann
,
U.
, and
Narayanan
,
S.
,
2004
, “
Analysis of Emotion Recognition Using Facial Expressions, Speech and Multimodal Information
,”
Proceedings of the 6th International Conference on Multimodal Interfaces
,
State College, PA
,
Oct. 13–15
, pp.
205
211
.
75.
He
,
Z.
,
Li
,
Z.
,
Yang
,
F.
,
Wang
,
L.
,
Li
,
J.
,
Zhou
,
C.
, and
Pan
,
J.
,
2020
, “
Advances in Multimodal Emotion Recognition Based on Brain–Computer Interfaces
,”
Brain Sci.
,
10
(
10
), p.
687
.
76.
Casas
,
J.
,
Spring
,
T.
,
Daher
,
K.
,
Mugellini
,
E.
,
Khaled
,
O. A.
, and
Cudré-Mauroux
,
P.
,
2021
, “
Enhancing Conversational Agents With Empathic Abilities
,”
Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents
,
Virtual Event, Japan
,
Sep. 14-17
, pp.
41
47
.
77.
Scherer
,
K. R.
,
2010
, “Emotion and Emotional Competence: Conceptual and Theoretical Issues for Modelling Agents,”
Blueprint for Affective Computing: A Sourcebook
,
K. R.
Scherer
,
T.
Bänziger
, and
E.
Roesch
, eds.,
Oxford University Press
,
New York
, pp.
3
20
.
78.
Healey
,
M. L.
, and
Grossman
,
M.
,
2018
, “
Cognitive and Affective Perspective-Taking: Evidence for Shared and Dissociable Anatomical Substrates
,”
Front. Neurol.
,
9
, p.
491
.
79.
Rabinowitz
,
N.
,
Perbet
,
F.
,
Song
,
F.
,
Zhang
,
C.
,
Eslami
,
S. M. A.
, and
Botvinick
,
M.
,
2018
, “
Machine Theory of Mind
,”
Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research
,
Stockholmsmässan, Stockholm Sweden
,
July 10–15
, Vol. 80, pp.
4218
4227
.
80.
Jara-Ettinger
,
J.
,
2019
, “
Theory of Mind as Inverse Reinforcement Learning
,”
Curr. Opin. Psychol. Sci.
,
29
, pp.
105
110
.
81.
Kosinski
,
M.
(
2023
).
Theory of Mind May Have Spontaneously Emerged in Large Language Models
. arXiv preprint arXiv:2302.02083.
82.
McQuiggan
,
S. W.
,
Robison
,
J. L.
,
Phillips
,
R.
, and
Lester
,
J. C.
,
2008
, “
Modeling Parallel and Reactive Empathy in Virtual Agents: An Inductive Approach
,”
Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008)
,
Estoril, Portugal
,
May 12–16
, pp.
167
174
.
83.
Bhat
,
G.
,
Danelljan
,
M.
,
Van Gool
,
L.
, and
Timofte
,
R.
,
2020
, “Your Surroundings: Exploiting Scene Information for Object Tracking,”
Computer Vision – ECCV 2020
,
A.
Vedaldi
,
H.
Bischof
,
T.
Brox
, and
J. M.
Frahm
, eds.,
Springer
,
Cham
, pp.
205
221
.
84.
Lin
,
W.
,
Liu
,
H.
,
Liu
,
S.
,
Li
,
Y.
,
Xiong
,
H.
,
Qi
,
G.
, and
Sebe
,
N.
,
2023
, “
HiEve: A Large-Scale Benchmark for Human-Centric Video Analysis in Complex Events
,”
Int. J. Comput. Vision
,
131
(
11
), pp.
2994
3018
.
85.
Kumar
,
H.
, and
Singh
,
P.
,
2015
, “
Neuromarketing: An Emerging Tool of Market Research
,”
Int. J. Eng. Manag. Res. (IJEMR)
,
5
(
6
), pp.
530
535
.
86.
Hsu
,
M.
,
2017
, “
Neuromarketing: Inside the Mind of the Consumer
,”
Calif. Manag. Rev.
,
59
(
4
), pp.
5
22
.
87.
Gero
,
J. S.
, and
Milovanovic
,
J.
,
2020
, “
A Framework for Studying Design Thinking Through Measuring Designers’ Minds, Bodies and Brains
,”
Des. Sci.
,
6
, p.
e19
.
88.
Hay
,
L.
,
Duffy
,
A. H. B.
,
Gilbert
,
S. J.
, and
Grealy
,
M. A.
,
2022
, “
Functional Magnetic Resonance Imaging (fMRI) in Design Studies: Methodological Considerations, Challenges, and Recommendations
,”
Des. Stud.
,
78
, p.
101078
.
89.
Krakovsky
,
M.
,
2018
, “
Artificial (Emotional) Intelligence
,”
Commun. ACM
,
61
(
4
), pp.
18
19
.
90.
Salmi
,
A.
,
Li
,
J.
, and
Hölttä-Otto
,
K.
,
2023
, “
Automatic Facial Expression Analysis as a Measure of User-Designer Empathy
,”
ASME. J. Mech. Des.
,
145
(
3
), p.
031403
.
91.
Mittal
,
T.
,
Guhan
,
P.
,
Bhattacharya
,
U.
,
Chandra
,
R.
,
Bera
,
A.
, and
Manocha
,
D.
,
2020
, “
Emoticon: Context-Aware Multimodal Emotion Recognition Using Frege's Principle
,”
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
,
Seattle, WA
,
June 13–19
, pp.
14234
14243
.
92.
Troiano
,
E.
,
Oberländer
,
L. A. M.
,
Wegge
,
M.
, and
Klinger
,
R.
,
2022
, “
x-enVENT: A Corpus of Event Descriptions With Experiencer-Specific Emotion and Appraisal Annotations
,”
Proceedings of the 13th Language Resources and Evaluation Conference
,
Marseille, France
,
June 20–25
, pp.
1365
1375
.
93.
Troiano
,
E.
,
Oberländer
,
L.
, and
Klinger
,
R.
,
2023
, “
Dimensional Modeling of Emotions in Text With Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction
,”
Comput. Linguist.
,
49
(
1
), pp.
1
72
.
94.
Wellman
,
H. M.
,
2018
, “
Theory of Mind: The State of the Art
,”
Eur. J. Dev. Psychol.
,
15
(
6
), pp.
728
755
.
95.
Chang-Arana
,
ÁM
,
Surma-Aho
,
A.
,
Li
,
J.
,
Yang
,
M. C.
, and
Hölttä-Otto
,
K.
,
2020
, “
Reading the User’s Mind: Designers Show High Accuracy in Inferring Design-Related Thoughts and Feelings
,”
Proceedings of the International Design Engineering Technical Conference and Computers and Information in Engineering Conference
, Vol.
83976
,
ASME
, p.
V008T08A029
.
96.
Zhou
,
F.
,
Jiao
,
J. R.
, and
Linsey
,
J. S.
,
2015
, “
Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews
,”
ASME J. Mech. Des.
,
137
(
7
), p.
071401
.
97.
Yu
,
M. H.
,
Li
,
J.
,
Liu
,
D.
,
Zhao
,
D.
,
Yan
,
R.
,
Tang
,
B.
, and
Zhang
,
H.
,
2020
, “
Draft and Edit: Automatic Storytelling Through Multi-Pass Hierarchical Conditional Variational Autoencoder
,”
Proceedings of the AAAI Conference on Artificial Intelligence
,
New York
,
Feb. 7–12
, Vol. 34, No. 2, pp.
1741
1748
.
98.
Han
,
A.
, and
Cai
,
Z.
,
2023
, “
Design implications of generative AI systems for visual storytelling for young learners
,”
Proceedings of the 22nd Annual ACM Interaction Design and Children Conference
,
Chicago, IL
,
June 19–23
.
99.
Brown
,
T.
,
Mann
,
B.
,
Ryder
,
N.
,
Subbiah
,
M.
,
Kaplan
,
J. D.
,
Dhariwal
,
P.
,
Neelakantan
,
A
, et al
,
2020
, “
Language Models Are Few-Shot Learners
,”
Adv. Neural Inf. Process. Syst.
,
33
, pp.
1877
1901
.
100.
Rombach
,
R.
,
Blattmann
,
A.
,
Lorenz
,
D.
,
Esser
,
P.
, and
Ommer
,
B.
,
2022
, “
High-Resolution Image Synthesis With Latent Diffusion Models
,”
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
,
Los Alamitos, CA
,
June 18–24
, pp.
10684
10695
.
101.
Le
,
M.
,
Vyas
,
A.
,
Shi
,
B.
,
Karrer
,
B.
,
Sari
,
L.
,
Moritz
,
R.
,
Williamson
,
M.
,
Manohar
,
V.
,
Adi
,
Y.
,
Mahadeokar
,
J.
, and
Hsu
,
W. N.
,
2023
, “
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale
,”
37th Conference on Neural Information Processing Systems
,
New Orleans, LA
,
Dec. 10–16
.
102.
Davis
,
E.
, and
Marcus
,
G.
,
2015
, “
Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence
,”
Commun. ACM
,
58
(
9
), pp.
92
103
.
103.
Jara-Ettinger
,
J.
,
Gweon
,
H.
,
Schulz
,
L. E.
, and
Tenenbaum
,
J. B.
,
2016
, “
The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology
,”
Trends Cogn. Sci.
,
20
(
8
), pp.
589
604
.
104.
Sarica
,
S.
,
Han
,
J.
, and
Luo
,
J.
,
2023
, “
Design Representation as Semantic Networks
,”
Comput. Ind.
,
144
, p.
103791
.
105.
OpenAI
. GPT-4 Technical Report. https://cdn.openai.com/papers/gpt-4.pdf
106.
Zellers
,
R.
,
Holtzman
,
A.
,
Bisk
,
Y.
,
Farhadi
,
A.
, and
Choi
,
Y.
,
2019
, “
HellaSwag: Can a Machine Really Finish Your Sentence?
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
,
Florence, Italy
,
July 28–Aug. 2
, pp.
4791
4800
.
107.
Kocaballi
,
A. B.
,
2023
,
Conversational AI-Powered Design: Chatgpt as Designer, User, And Product
. arXiv preprint arXiv:2302.07406.
108.
Alzayed
,
M. A.
,
McComb
,
C.
,
Menold
,
J.
,
Huff
,
J.
, and
Miller
,
S. R.
,
2021
, “
Are you Feeling me? An Exploration of Empathy Development in Engineering Design Education
,”
ASME J. Mech. Des.
,
143
(
11
), p.
112301
.
109.
Alzayed
,
M. A.
,
Starkey
,
E. M.
,
Ritter
,
S. C.
, and
Prabhu
,
R.
,
2022
, “
Am I Right? Investigating the Influence of Trait Empathy and Attitudes Towards Sustainability on the Accuracy of Concept Evaluations in Sustainable Design
,”
Proceedings of the International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
St. Louis, MO
,
Aug. 14–17
,
Vol. 86236, p. V03BT03A007
.
110.
Yalçın
,
Ö. N.
,
2019
, “
Evaluating Empathy in Artificial Agents
,”
Proceedings of the 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)
,
Cambridge, UK
, pp.
1
7
.
111.
Ickes
,
W.
,
1993
, “
Empathic Accuracy
,”
J. Pers.
,
61
(
4
), pp.
587
610
.
112.
Chang-Arana
,
Á. M.
,
Piispanen
,
M.
,
Himberg
,
T.
,
Surma-Aho
,
A.
,
Alho
,
J.
,
Sams
,
M.
, and
Hölttä-Otto
,
K.
,
2020
, “
Empathic Accuracy in Design: Exploring Design Outcomes Through Empathic Performance and Physiology
,”
Des. Sci.
,
6
, p.
e16
.
113.
McQuiggan
,
S. W.
, and
Lester
,
J. C.
,
2007
, “
Modeling and Evaluating Empathy in Embodied Companion Agents
,”
Int. J. Hum-Comput. Stud.
,
65
(
4
), pp.
348
360
.
114.
Li
,
J.
, and
Hölttä-Otto
,
K.
,
2022
, “
Does Empathising With Users Contribute to Better Need Finding?
Proceedings of the International Design Engineering Technical Conference and Computer and Information in Engineering Conference
,
St. Louis, MO
,
Aug. 14–17
,
Vol. 86267, p. V006T06A023
.
115.
Zhou
,
F.
,
Ayoub
,
J.
,
Xu
,
Q.
, and
Jessie Yang
,
X.
,
2020
, “
A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems
,”
ASME J. Mech. Des.
,
142
(
1
), p.
011101
.
116.
Kano
,
N.
,
Seraku
,
N.
,
Takahashi
,
F.
, and
Tsuji
,
S.
,
1984
, “
Attractive Quality and Must-Be Quality
,”
Japanese Soc. Qual. Control
,
14
(
2
), pp.
39
48
.
117.
Jiang
,
S.
,
Hu
,
J.
,
Magee
,
C. L.
, and
Luo
,
J.
,
2022
, “
Deep Learning for Technical Document Classification
,”
IEEE Trans. Eng. Manag.
, pp.
1
17
.
118.
Song
,
B.
,
Zhou
,
R.
, and
Ahmed
,
F.
,
2024
, “
Multi-Modal Machine Learning in Engineering Design: A Review and Future Directions
,”
ASME J. Comput. Inf. Sci. Eng.
,
24
(
1
), p.
010801
.
119.
Siddharth
,
L.
,
Blessing
,
L. T. M.
,
Wood
,
K. L.
, and
Luo
,
J.
,
2022
, “
Engineering Knowledge Graph From Patent Database
,”
ASME J. Comput. Inf. Sci. Eng.
,
22
(
2
), p.
021008
.
120.
Pileggi
,
S. F.
,
2021
, “
Knowledge Interoperability and Re-Use in Empathy Mapping: An Ontological Approach
,”
Expert Syst. Appl.
,
180
, p.
115065
.
121.
Desmet
,
P.
,
2003
, “
A Multilayered Model of Product Emotions
,”
Des. J.
,
6
(
2
), pp.
4
13
.
122.
Demir
,
E.
,
Desmet
,
P.
, and
Hekkert
,
P.
,
2009
, “
Appraisal Patterns of Emotions in Human-Product Interaction
,”
Int. J. Des.
,
3
(
2
), pp.
4
13
.
123.
Desmet
,
P.
, and
Hekkert
,
P.
,
2007
, “
Framework of Product Experience
,”
Int. J. Des.
,
1
(
1
).
124.
Bender
,
E. M.
,
Gebru
,
T.
,
McMillan-Major
,
A.
, and
Shmitchell
,
S.
,
2021
, “
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
,
Virtual Event, Canada
,
Mar. 3–10
, pp.
610
623
.
125.
Motoki
,
F.
,
Neto
,
V. P.
, and
Rodrigues
,
V.
,
2023
, “
More Human Than Human: Measuring ChatGPT Political Bias
,”
Public Choice
.
126.
Susser
,
D.
,
Roessler
,
B.
, and
Nissenbaum
,
H.
,
2019
, “
Online Manipulation: Hidden Influences in a Digital World
,”
Geo. L. Tech. Rev.
,
4
, p.
1
.
You do not currently have access to this content.