Abstract

The use of artificial intelligence (AI) to guide team dynamics has the potential to transform collaborative problem-solving processes. Existing approaches to doing so are trained on prior problem-specific data, limiting them to problems that have already been solved. This research aims to extend AI-based approaches to novel situations by eliminating the need for prior data. This is accomplished by focusing on team communication and collective intelligence (CI) rather than problem-specific strategies. CI is a team's general ability to work well across various tasks and is more predictive of team performance than individual intelligence. This work introduces an AI facilitator that monitors CI attributes—collective attention, equal participation, and consistent communication—in real time and intervenes as necessary to guide teams toward better collaboration and overall performance. Two human subjects studies are performed on teams working together to design a mechanical system to test the AI facilitator. The studies vary in the structure of the problem-solving environment (virtual or colocated) and communication modality (text-only or verbal). The studies' findings support that the AI facilitator leads teams to better performance than without using the facilitator, and equivalent performance when compared to a human facilitator. This contribution to the field of AI in team management is notable because of the elimination of the need for prior data, making it applicable to novel situations. This work lays the groundwork for a new approach to potentially transform the future of teamwork.

References

1.
Burke
,
A.
,
2011
, “
Group Work: How to Use Groups Effectively
,”
Group Work
,
11
(
2
), p.
9
.
2.
Faure
,
C.
,
2004
, “
Beyond Brainstorming: Effects of Different Group Procedures on Selection of Ideas and Satisfaction With the Process
,”
J. Creat. Behav.
,
38
(
1
), pp.
13
34
.
3.
Mayo
,
A. T.
,
2020
, “
Teamwork in a Pandemic: Insights From Management Research
,”
Leader
,
4
(
2
), pp.
53
56
.
4.
Cramton
,
C. D.
,
2001
, “
The Mutual Knowledge Problem and Its Consequences for Dispersed Collaboration
,”
Org. Sci.
,
12
(
3
), pp.
346
371
.
5.
Buffinton
,
K. W.
,
Jablokow
,
K. W.
, and
Martin
,
K. A.
,
2002
, “
Project Team Dynamics and Cognitive Style
,”
Eng. Manage. J.
,
14
(
3
), pp.
25
33
.
6.
Linsey
,
J. S.
,
Tseng
,
I.
,
Fu
,
K.
,
Cagan
,
J.
,
Wood
,
K. L.
, and
Schunn
,
C.
,
2010
, “
A Study of Design Fixation, Its Mitigation and Perception in Engineering Design Faculty
,”
ASME J. Mech. Des.
,
132
(
4
), p.
041003
.
7.
Gyory
,
J. T.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2019
, “
Are You Better Off Alone? Mitigating the Underperformance of Engineering Teams During Conceptual Design Through Adaptive Process Management
,”
Res. Eng. Des.
,
30
(
1
), pp.
85
102
.
8.
Harkins
,
S. G.
, and
Petty
,
R. E.
,
1982
, “
Effects of Task Difficulty and Task Uniqueness on Social Loafing
,”
J. Personal. Soc. Psychol.
,
43
(
6
), pp.
1214
1229
.
9.
Williams
,
K. D.
, and
Karau
,
S. J.
,
1993
, “
Social Loafing: A Meta-Analytic Review and Theoretical Integration
,”
J. Personal. Soci. Psychol.
,
65
(
4
), pp.
681
706
.
10.
Barron
,
B.
,
2003
, “
When Smart Groups Fail
,”
J. Learn. Sci.
,
12
(
3
), pp.
307
359
.
11.
Woolley
,
A. W.
,
Gerbasi
,
M. E.
,
Chabris
,
C. F.
,
Kosslyn
,
S. M.
, and
Hackman
,
J. R.
,
2008
, “
Bringing in the Experts: How Team Composition and Collaborative Planning Jointly Shape Analytic Effectiveness
,”
Small Group Res.
,
39
(
3
), pp.
352
371
.
12.
Gupta
,
P.
,
Nguyen
,
T. N.
,
Gonzalez
,
C.
, and
Woolley
,
A. W.
,
2023
, “
Fostering Collective Intelligence in Human–AI Collaboration: Laying the Groundwork for COHUMAIN
,”
Top. Cogn. Sci.
,
17
(
2
), pp.
189
216
.
13.
Gupta
,
P.
, and
Woolley
,
A. W.
,
2021
, “
Articulating the Role of Artificial Intelligence in Collective Intelligence: A Transactive Systems Framework
,”
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
,
Baltimore, MD
,
Oct. 4–7
, Vol.
65
(
1
), pp.
670
674
.
14.
Dow
,
S.
,
MacIntyre
,
B.
,
Lee
,
J.
,
Oezbek
,
C.
,
Bolter
,
J. D.
, and
Gandy
,
M.
,
2005
, “
Wizard of Oz Support Throughout an Iterative Design Process
,”
IEEE Pervasive Comput.
,
4
(
4
), pp.
18
26
.
15.
Schecter
,
A.
,
Hohenstein
,
J.
,
Larson
,
L.
,
Harris
,
A.
,
Hou
,
T. Y.
,
Lee
,
W. Y.
,
Lauharatanahirun
,
N.
,
DeChurch
,
L.
,
Contractor
,
N.
, and
Jung
,
M.
,
2023
, “
Vero: An Accessible Method for Studying Human–AI Teamwork
,”
Comput. Hum. Behav.
,
141
(
1
), pp.
1
12
.
16.
McNeese
,
N. J.
,
Flathmann
,
C.
,
O’Neill
,
T. A.
, and
Salas
,
E.
,
2023
, “
Stepping Out of the Shadow of Human-Human Teaming: Crafting a Unique Identity for Human-Autonomy Teams
,”
Comput. Hum. Behav.
,
148
(
1
), pp.
1
8
.
17.
Song
,
B.
,
Gyory
,
J. T.
,
Zhang
,
G.
,
Zurita
,
N. F.
,
Stump
,
G.
,
Martin
,
J.
,
Miller
,
S.
, et al.
,
2022
, “
Decoding the Agility of Artificial Intelligence-Assisted Human Design Teams
,”
Des. Stud.
,
79
(
1
), p.
101094
.
18.
Gyory
,
J. T.
,
Soria Zurita
,
N. F.
,
Martin
,
J.
,
Balon
,
C.
,
McComb
,
C.
,
Kotovsky
,
K.
, and
Cagan
,
J.
,
2022
, “
Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design
,”
ASME J. Mech. Des.
,
144
(
2
), pp.
021405
.
19.
Gyory
,
J. T.
,
Kotovsky
,
K.
,
McComb
,
C.
, and
Cagan
,
J.
,
2022
, “
Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams
,”
ASME J. Mech. Des.
,
144
(
10
), p.
104501
.
20.
Li
,
Q.
, and
Vasarhelyi
,
M.
,
2018
, “
Developing a Cognitive Assistant for the Audit Plan Brainstorming Session
,”
IJDAR
,
18
(
1
), pp.
119
140
.
21.
Coronado
,
M.
,
Iglesias
,
C. A.
,
Carrera
,
Á
, and
Mardomingo
,
A.
,
2018
, “
A Cognitive Assistant for Learning Java Featuring Social Dialogue
,”
Int. J. Hum. Comput. Stud.
,
117
(
1
), pp.
55
67
.
22.
Freire
,
S. K.
,
Panicker
,
S. S.
,
Ruiz-Arenas
,
S.
,
Rusák
,
Z.
, and
Niforatos
,
E.
,
2023
, “
A Cognitive Assistant for Operators: AI-Powered Knowledge Sharing on Complex Systems
,”
IEEE Pervasive Comput.
,
22
(
1
), pp.
50
58
.
23.
Shin
,
J. G.
,
Koch
,
J.
,
Lucero
,
A.
,
Dalsgaard
,
P.
, and
Mackay
,
W. E.
,
2023
, “
Integrating AI in Human-Human Collaborative Ideation
,”
Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
,
Hamburg, Germany
,
Apr. 23–28
,
ACM
,
2023
, pp.
1
5
.
24.
O’Neill
,
T. A.
,
Flathmann
,
C.
,
McNeese
,
N. J.
, and
Salas
,
E.
,
2023
, “
Human-Autonomy Teaming: Need for a Guiding Team-Based Framework?
Comput. Hum. Behav.
,
146
(
1
), pp.
1
7
.
25.
Woolley
,
A. W.
,
Chabris
,
C. F.
,
Pentland
,
A.
,
Hashmi
,
N.
, and
Malone
,
T. W.
,
2010
, “
Evidence for a Collective Intelligence Factor in the Performance of Human Groups
,”
Science
,
330
(
6004
), pp.
686
688
.
26.
Woolley
,
A. W.
, and
Gupta
,
P.
,
2023
, “
Understanding Collective Intelligence: Investigating the Role of Collective Memory, Attention, and Reasoning Processes
,”
Perspect. Psychol. Sci.
,
19
(
2
),
344
354
.
27.
Zhao
,
M.
,
Eadeh
,
F. R.
,
Nguyen
,
T. N.
,
Gupta
,
P.
,
Admoni
,
H.
,
Gonzalez
,
C.
, and
Woolley
,
A. W.
,
2023
, “
Teaching Agents to Understand Teamwork: Evaluating and Predicting Collective Intelligence as a Latent Variable via Hidden Markov Models
,”
Comput. Hum. Behav.
,
139
(
1
), p.
107524
.
28.
Engel
,
D.
,
Woolley
,
A. W.
,
Jing
,
L. X.
,
Chabris
,
C. F.
, and
Malone
,
T. W.
,
2014
, “
Reading the Mind in the Eyes or Reading Between the Lines? Theory of Mind Predicts Collective Intelligence Equally Well Online and Face-To-Face
,”
PLoS One
,
9
(
12
), pp.
1
16
.
29.
Engel
,
D.
,
Woolley
,
A. W.
,
Aggarwal
,
I.
,
Chabris
,
C. F.
,
Takahashi
,
M.
,
Nemoto
,
K.
,
Kaiser
,
C.
,
Kim
,
Y. J.
, and
Malone
,
T. W.
,
2015
, “
Collective Intelligence in Computer-Mediated Collaboration Emerges in Different Contexts and Cultures
,”
Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
,
Seoul, South Korea
,
Apr. 18–23
,
ACM
, pp.
3769
3778
.
30.
Orejudo
,
S.
,
Cano-Escoriaza
,
J.
,
Cebollero-Salinas
,
A. B.
,
Bautista
,
P.
,
Clemente-Gallardo
,
J.
,
Rivero
,
A.
,
Rivero
,
P.
, and
Tarancón
,
A.
,
2022
, “
Evolutionary Emergence of Collective Intelligence in Large Groups of Students
,”
Front. Psychol.
,
13
(
1
), pp.
1
16
.
31.
Kim
,
Y. J.
,
Engel
,
D.
,
Woolley
,
A. W.
,
Lin
,
J. Y.-T.
,
McArthur
,
N.
, and
Malone
,
T. W.
,
2017
, “
What Makes a Strong Team?: Using Collective Intelligence to Predict Team Performance in League of Legends
,”
Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
,
Portland, OR
,
Feb. 25–Mar. 1
,
ACM
, pp.
2316
2329
.
32.
Gregg
,
D.
,
2009
, “
Developing a Collective Intelligence Application for Special Education
,”
Decis. Support Syst.
,
47
(
4
), pp.
455
465
.
33.
Peeters
,
M. M. M.
,
Van Diggelen
,
J.
,
Van Den Bosch
,
K.
,
Bronkhorst
,
A.
,
Neerincx
,
M. A.
,
Schraagen
,
J. M.
, and
Raaijmakers
,
S.
,
2021
, “
Hybrid Collective Intelligence in a Human–AI Society
,”
AI Soc.
,
36
(
1
), pp.
217
238
.
34.
Cui
,
H.
, and
Yasseri
,
T.
,
2024
, “
AI-Enhanced Collective Intelligence
,”
Patterns
,
5
(
11
), p.
101074
.
35.
Woolley
,
A.
,
Aggarwal
,
I.
, and
Malone
,
T.
,
2015
, “Collective Intelligence in Teams and Organizations,”
Handbook on Collective Intelligence
,
T.
Malone
, and
M.
Bernstein
, eds.,
MIT Press
,
Cambridge, MA
, pp.
143
168
.
36.
Spearman
,
C.
,
1904
, “
‘General Intelligence,’ Objectively Determined and Measured
,”
Am. J. Psychol.
,
15
(
2
), pp.
201
292
.
37.
Riedl
,
C.
,
Kim
,
Y. J.
,
Gupta
,
P.
,
Malone
,
T. W.
, and
Woolley
,
A. W.
,
2021
, “
Quantifying Collective Intelligence in Human Groups
,”
Proc. Natl. Acad. Sci. U.S.A.
,
118
(
21
), pp.
1
5
.
38.
Dennis
,
A. S.
,
Barlow
,
J. B.
, and
Dennis
,
A. R.
,
2022
, “
The Power of Introverts: Personality and Intelligence in Virtual Teams
,”
J. Manage. Inf. Syst.
,
39
(
1
), pp.
102
129
.
39.
L.
Bender
,
G.
Walia
,
K.
Kambhampaty
,
K.
Nygard
, and
T.
Nygard
,
2012
, “
Social Sensitivity Correlations with the Effectiveness of Team Process Performance: an Empirical Study
,”
ICER'12: Proceedings of the Ninth Annual International Conference on International Computing Education Research
,
Auckland, New Zealand
,
Sept. 10–12
, pp.
39
46
.
40.
Baron-Cohen
,
S.
,
Wheelwright
,
S.
,
Hill
,
J.
,
Raste
,
Y.
, and
Plumb
,
I.
,
2001
, “
The ‘Reading the Mind in the Eyes’ Test Revised Version: A Study With Normal Adults, and Adults With Asperger Syndrome or High-Functioning Autism
,”
J. Child Psychol. Psychiatr.
,
42
(
2
), pp.
241
251
.
41.
Premack
,
D.
, and
Woodruff
,
G.
,
1978
, “
Does the Chimpanzee Have a Theory of Mind?
,”
Behav. Brain Sci.
,
1
(
4
), pp.
515
526
.
42.
Woolley
,
A. W.
,
Chow
,
R. M.
,
Mayo
,
A. T.
,
Riedl
,
C.
, and
Chang
,
J. W.
,
2023
, “
Collective Attention and Collective Intelligence: The Role of Hierarchy and Team Gender Composition
,”
Org. Sci.
,
34
(
3
), pp.
1315
1331
.
43.
De Domenico
,
M.
, and
Altmann
,
E. G.
,
2020
, “
Unraveling the Origin of Social Bursts in Collective Attention
,”
Sci. Rep.
,
10
(
1
), pp.
1
9
.
44.
Gyory
,
J. T.
,
Kotovsky
,
K.
, and
Cagan
,
J.
,
2021
, “
The Influence of Process Management: Uncovering the Impact of Real-Time Managerial Interventions via a Topic Modeling Approach
,”
ASME J. Mech. Des.
,
143
(
11
), p.
111401
.
45.
Tseng
,
I.
,
Moss
,
J.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2008
, “
The Role of Timing and Analogical Similarity in the Stimulation of Idea Generation in Design
,”
Des. Stud.
,
29
(
3
), pp.
203
221
.
46.
Riedl
,
C.
, and
Woolley
,
A. W.
,
2017
, “
Teams vs. Crowds: A Field Test of the Relative Contribution of Incentives, Member Ability, and Emergent Collaboration to Crowd-Based Problem Solving Performance
,”
AMD
,
3
(
4
), pp.
382
403
.
47.
DiMicco
,
J. M.
,
Hollenbach
,
K. J.
,
Pandolfo
,
A.
, and
Bender
,
W.
,
2017
, “
The Impact of Increased Awareness While Face-to-Face
.”
Hum. Comput. Interact.
,
22
(
1–2
), pp.
47
96
.
48.
Dong
,
A.
,
Sep. 2005
, “
The Latent Semantic Approach to Studying Design Team Communication
,”
Des. Stud.
,
26
(
5
), pp.
445
461
.
49.
Dong
,
A.
,
2004
, “Quantifying Coherent Thinking in Design: A Computational Linguistics Approach,”
Design Computing and Cognition ‘04
,
J. S.
Gero
, ed.,
Springer Netherlands
,
Dordrecht
, pp.
521
540
.
50.
Dong
,
A.
,
Hill
,
A. W.
, and
Agogino
,
A. M.
,
2004
, “
A Document Analysis Method for Characterizing Design Team Performance
,”
ASME J. Mech. Des.
,
126
(
3
), pp.
378
385
.
51.
Explosion AI
,
2015
, “spaCy: Industrial-Strength Natural Language Processing in Python.” https://spacy.io, Accessed August 22, 2023.
52.
Miller
,
J.
, and
Ulrich
,
R.
,
2019
, “
The Quest for an Optimal Alpha
,”
PLoS One
,
14
(
1
), p.
e0208631
.
53.
Kerby
,
D. S.
,
2014
, “
The Simple Difference Formula: An Approach to Teaching Nonparametric Correlation
,”
Compreh. Psychol.
,
3
(
1
), p.
11.IT.3.1
.
54.
Cohen
,
J.
,
2009
,
Statistical Power Analysis for the Behavioral Sciences
, 2nd ed.,
Psychology Press
,
New York, NY
.
55.
Morrison
,
E. W.
, and
Milliken
,
F. J.
,
2024
, “Organizational Silence: A Barrier to Change and Development in a Pluralistic World.”
56.
Fu
,
K.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2010
, “
Design Team Convergence: The Influence of Example Solution Quality
,”
ASME J. Mech. Des.
,
132
(
1
), p.
111005
.
57.
Goucher-Lambert
,
K.
, and
Cagan
,
J.
,
2019
, “
Crowdsourcing Inspiration: Using Crowd Generated Inspirational Stimuli to Support Designer Ideation
,”
Des. Stud.
,
61
(
1
), pp.
1
29
.
58.
Olson
,
G. M.
, and
Olson
,
J. S.
,
2000
, “
Distance Matters
,”
Hum. Comput. Interact.
,
15
(
2–3
), pp.
139
178
.
59.
Dennis
,
A. R.
,
Fuller
,
R. M.
, and
Valacich
,
J. S.
,
2008
, “
Media, Tasks, and Communication Processes: A Theory of Media Synchronicity
,”
MIS Quarterly
,
32
(
3
), p.
575
.
60.
Fussell
,
S. R.
,
Kraut
,
R. E.
, and
Siegel
,
J.
,
2000
, “
Coordination of Communication: Effects of Shared Visual Context on Collaborative Work
,”
Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work
,
Philadelphia, PA
,
Dec. 2–6
,
ACM
, pp.
21
30
.
61.
Graetz
,
K. A.
,
Boyle
,
E. S.
,
Kimble
,
C. E.
,
Thompson
,
P.
, and
Garloch
,
J. L.
,
1998
, “
Information Sharing in Face-to-Face, Teleconferencing, and Electronic Chat Groups
,”
Small Group Res.
,
29
(
6
), pp.
714
743
.
62.
Deepgram
,
2024
, “The Voice AI Platform for Enterprise Use Cases.” https://deepgram.com/. Accessed April 15, 2024.
63.
OpenAI
,
2020
, “GPT-3,” OpenAI. https://openai.com/research/gpt-3, Accessed April 3, 2024.
64.
Fu
,
K.
,
Chan
,
J.
,
Cagan
,
J.
,
Kotovsky
,
K.
,
Schunn
,
C.
, and
Wood
,
K.
,
2013
, “
The Meaning of ‘Near’ and ‘Far’: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output
,”
ASME J. Mech. Des.
,
135
(
2
), p.
021007
.
65.
Chan
,
J.
,
Fu
,
K.
,
Schunn
,
C.
,
Cagan
,
J.
,
Wood
,
K.
, and
Kotovsky
,
K.
,
2011
, “
On the Benefits and Pitfalls of Analogies for Innovative Design: Ideation Performance Based on Analogical Distance, Commonness, and Modality of Examples
,”
ASME J. Mech. Des.
,
133
(
8
), p.
081004
.
66.
Shah
,
J. J.
,
Smith
,
S. M.
, and
Vargas-Hernandez
,
N.
,
2003
, “
Metrics for Measuring Ideation Effectiveness
,”
Des. Stud.
,
24
(
2
), pp.
111
134
.
67.
Gimpel
,
H.
,
Lahmer
,
S.
,
Wöhl
,
M.
, and
Graf-Drasch
,
V.
,
2023
, “
Digital Facilitation of Group Work to Gain Predictable Performance
,”
Group Decis. Negot.
,
33
(
1
), pp.
113
145
.
68.
Gupta
,
P.
,
Ji Kim
,
Y.
,
Glikson
,
E.
, and
Woolley
,
A.
,
2024
, “
Using Digital Nudges to Enhance Collective Intelligence in Online Collaboration: Insights From Unexpected Outcomes
,”
MISQ
,
48
(
1
), pp.
393
408
.
69.
Meslec
,
N.
,
Aggarwal
,
I.
, and
Curseu
,
P. L.
,
2016
, “
The Insensitive Ruins It All: Compositional and Compilational Influences of Social Sensitivity on Collective Intelligence in Groups
,”
Front. Psychol.
,
7
(
1
), pp.
1
7
.
70.
Brownell
,
E.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2021
, “
Only as Strong as the Strongest Link: The Relative Contribution of Individual Team Member Proficiency in Configuration Design
,”
ASME J. Mech. Des.
,
143
(
8
), p.
081402
.
71.
Sunstein
,
C. R.
,
2017
, “
Nudges That Fail
,”
Behav. Public Policy
,
1
(
1
), pp.
4
25
.
72.
Hu
,
W.-L.
,
Akash
,
K.
,
Jain
,
N.
, and
Reid
,
T.
,
2016
, “
Real-Time Sensing of Trust in Human-Machine Interactions
,”
IFAC-PapersOnLine
,
49
(
32
), pp.
48
53
.
73.
Bao
,
Y.
,
Cheng
,
X.
,
De Vreede
,
T.
, and
De Vreede
,
G.-J.
,
2021
, “
Investigating the Relationship Between AI and Trust in Human-AI Collaboration
,”
Presented at the Hawaii International Conference on System Sciences
,
Kauai, HI
,
Jan. 5
, pp.
607
616
.
74.
Chong
,
L.
,
Raina
,
A.
,
Goucher-Lambert
,
K.
,
Kotovsky
,
K.
, and
Cagan
,
J.
,
2023
, “
The Evolution and Impact of Human Confidence in Artificial Intelligence and in Themselves on AI-Assisted Decision-Making in Design
,”
ASME J. Mech. Des.
,
145
(
3
), p.
031401
.
75.
Hu
,
W.-L.
,
Akash
,
K.
,
Reid
,
T.
, and
Jain
,
N.
,
2019
, “
Computational Modeling of the Dynamics of Human Trust During Human–Machine Interactions
,”
IEEE Trans. Hum. Mach. Syst.
,
49
(
6
), pp.
485
497
.
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