Eternagram: Probing Player Atitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure

Suifang Zhou; Latisha Besariani Hendra; Qinshi Zhang; Jussi Holopainen; Ray LC

doi:10.1145/3613904.3642850

Eternagram: Probing Player Atitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure

Suifang Zhou, Latisha Besariani Hendra, Qinshi Zhang, Jussi Holopainen, Ray LC^*

^*Corresponding author for this work

School of Creative Media

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

7 Citations (Scopus)

Abstract

Conventional methods of assessing attitudes towards climate change are limited in capturing authentic opinions, primarily stemming from a lack of context-specific assessment strategies and an overreliance on simplistic surveys. Game-based Assessments (GBA) have demonstrated the ability to overcome these issues by immersing participants in engaging gameplay within carefully crafted, scenario-based environments. Concurrently, advancements in AI and Natural Language Processing (NLP) show promise in enhancing the gamified testing environment, achieving this by generating context-aware, human-like dialogues that contribute to a more natural and effective assessment. Our study introduces a new technique for probing climate change attitudes by actualizing a GPT-driven chatbot system in harmony with a game design depicting a futuristic climate scenario. The correlation analysis reveals an assimilation effect, where players' post-game climate awareness tends to align with their in-game perceptions. Key predictors of pro-climate attitudes are identified as traits like 'Openness' and 'Agreeableness', and a preference for democratic values.

© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Original language	English
Title of host publication	CHI '24
Subtitle of host publication	Proceedings of the CHI Conference on Human Factors in Computing Systems
Editors	Florian Floyd Mueller, Penny Kyburz, Julie R. Williamson, Corina Sas, Max L. Wilson, Phoebe Toups Dugas, Irina Shklovski
Publisher	Association for Computing Machinery
ISBN (Print)	979-8-4007-0330-0
DOIs	https://doi.org/10.1145/3613904.3642850
Publication status	Published - May 2024
Event	2024 CHI Conference on Human Factors in Computing Sytems (CHI 2024): "Surfing the World" - Hawaiʻi Convention Center, Honolulu, United States Duration: 11 May 2024 → 16 May 2024 https://chi2024.acm.org/for-attendees/

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings

Conference

Conference	2024 CHI Conference on Human Factors in Computing Sytems (CHI 2024)
Place	United States
City	Honolulu
Period	11/05/24 → 16/05/24
Internet address	https://chi2024.acm.org/for-attendees/

Research Keywords

Conversation Analysis
Games/Play
HCI for Development
Interaction Design
Interactive Storytelling
Quantitative Methods
Survey
Text Entry

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access Output

10.1145/3613904.3642850

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

Zhou, S., Hendra, L. B., Zhang, Q., Holopainen, J., & LC, R. (2024). Eternagram: Probing Player Atitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure. In Florian Floyd Mueller, P. Kyburz, J. R. Williamson, C. Sas, M. L. Wilson, P. T. Dugas, & I. Shklovski (Eds.), CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems Article 281 (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3613904.3642850

Zhou, Suifang ; Hendra, Latisha Besariani ; Zhang, Qinshi et al. / Eternagram : Probing Player Atitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure. CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems. editor / Florian Floyd Mueller, ; Penny Kyburz ; Julie R. Williamson ; Corina Sas ; Max L. Wilson ; Phoebe Toups Dugas ; Irina Shklovski. Association for Computing Machinery, 2024. (Conference on Human Factors in Computing Systems - Proceedings).

@inproceedings{028f4848d4da4d76a8034c609daa0ee6,

title = "Eternagram: Probing Player Atitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure",

abstract = "Conventional methods of assessing attitudes towards climate change are limited in capturing authentic opinions, primarily stemming from a lack of context-specific assessment strategies and an overreliance on simplistic surveys. Game-based Assessments (GBA) have demonstrated the ability to overcome these issues by immersing participants in engaging gameplay within carefully crafted, scenario-based environments. Concurrently, advancements in AI and Natural Language Processing (NLP) show promise in enhancing the gamified testing environment, achieving this by generating context-aware, human-like dialogues that contribute to a more natural and effective assessment. Our study introduces a new technique for probing climate change attitudes by actualizing a GPT-driven chatbot system in harmony with a game design depicting a futuristic climate scenario. The correlation analysis reveals an assimilation effect, where players' post-game climate awareness tends to align with their in-game perceptions. Key predictors of pro-climate attitudes are identified as traits like 'Openness' and 'Agreeableness', and a preference for democratic values.{\textcopyright} 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.",

keywords = "Conversation Analysis, Games/Play, HCI for Development, Interaction Design, Interactive Storytelling, Quantitative Methods, Survey, Text Entry",

author = "Suifang Zhou and Hendra, {Latisha Besariani} and Qinshi Zhang and Jussi Holopainen and Ray LC",

note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s); 2024 CHI Conference on Human Factors in Computing Sytems (CHI 2024) : {"}Surfing the World{"} ; Conference date: 11-05-2024 Through 16-05-2024",

year = "2024",

month = may,

doi = "10.1145/3613904.3642850",

language = "English",

isbn = "979-8-4007-0330-0",

series = "Conference on Human Factors in Computing Systems - Proceedings",

publisher = "Association for Computing Machinery",

editor = "{Florian Floyd Mueller,} and Penny Kyburz and Williamson, {Julie R.} and Corina Sas and Wilson, {Max L.} and Dugas, {Phoebe Toups} and Irina Shklovski",

booktitle = "CHI '24",

address = "United States",

url = "https://chi2024.acm.org/for-attendees/",

}

Zhou, S, Hendra, LB, Zhang, Q, Holopainen, J & LC, R 2024, Eternagram: Probing Player Atitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure. in Florian Floyd Mueller, P Kyburz, JR Williamson, C Sas, ML Wilson, PT Dugas & I Shklovski (eds), CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems., 281, Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, 2024 CHI Conference on Human Factors in Computing Sytems (CHI 2024), Honolulu, United States, 11/05/24. https://doi.org/10.1145/3613904.3642850

Eternagram: Probing Player Atitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure. / Zhou, Suifang; Hendra, Latisha Besariani; Zhang, Qinshi et al.
CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems. ed. / Florian Floyd Mueller,; Penny Kyburz; Julie R. Williamson; Corina Sas; Max L. Wilson; Phoebe Toups Dugas; Irina Shklovski. Association for Computing Machinery, 2024. 281 (Conference on Human Factors in Computing Systems - Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Eternagram

T2 - 2024 CHI Conference on Human Factors in Computing Sytems (CHI 2024)

AU - Zhou, Suifang

AU - Hendra, Latisha Besariani

AU - Zhang, Qinshi

AU - Holopainen, Jussi

AU - LC, Ray

PY - 2024/5

Y1 - 2024/5

N2 - Conventional methods of assessing attitudes towards climate change are limited in capturing authentic opinions, primarily stemming from a lack of context-specific assessment strategies and an overreliance on simplistic surveys. Game-based Assessments (GBA) have demonstrated the ability to overcome these issues by immersing participants in engaging gameplay within carefully crafted, scenario-based environments. Concurrently, advancements in AI and Natural Language Processing (NLP) show promise in enhancing the gamified testing environment, achieving this by generating context-aware, human-like dialogues that contribute to a more natural and effective assessment. Our study introduces a new technique for probing climate change attitudes by actualizing a GPT-driven chatbot system in harmony with a game design depicting a futuristic climate scenario. The correlation analysis reveals an assimilation effect, where players' post-game climate awareness tends to align with their in-game perceptions. Key predictors of pro-climate attitudes are identified as traits like 'Openness' and 'Agreeableness', and a preference for democratic values.© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

AB - Conventional methods of assessing attitudes towards climate change are limited in capturing authentic opinions, primarily stemming from a lack of context-specific assessment strategies and an overreliance on simplistic surveys. Game-based Assessments (GBA) have demonstrated the ability to overcome these issues by immersing participants in engaging gameplay within carefully crafted, scenario-based environments. Concurrently, advancements in AI and Natural Language Processing (NLP) show promise in enhancing the gamified testing environment, achieving this by generating context-aware, human-like dialogues that contribute to a more natural and effective assessment. Our study introduces a new technique for probing climate change attitudes by actualizing a GPT-driven chatbot system in harmony with a game design depicting a futuristic climate scenario. The correlation analysis reveals an assimilation effect, where players' post-game climate awareness tends to align with their in-game perceptions. Key predictors of pro-climate attitudes are identified as traits like 'Openness' and 'Agreeableness', and a preference for democratic values.© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

KW - Conversation Analysis

KW - Games/Play

KW - HCI for Development

KW - Interaction Design

KW - Interactive Storytelling

KW - Quantitative Methods

KW - Survey

KW - Text Entry

UR - http://www.scopus.com/inward/record.url?scp=85194849195&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85194849195&origin=recordpage

U2 - 10.1145/3613904.3642850

DO - 10.1145/3613904.3642850

M3 - RGC 32 - Refereed conference paper (with host publication)

AN - SCOPUS:85194849195

SN - 979-8-4007-0330-0

T3 - Conference on Human Factors in Computing Systems - Proceedings

BT - CHI '24

A2 - Florian Floyd Mueller,,

A2 - Kyburz, Penny

A2 - Williamson, Julie R.

A2 - Sas, Corina

A2 - Wilson, Max L.

A2 - Dugas, Phoebe Toups

A2 - Shklovski, Irina

PB - Association for Computing Machinery

Y2 - 11 May 2024 through 16 May 2024

ER -

Zhou S, Hendra LB, Zhang Q, Holopainen J , LC R. Eternagram: Probing Player Atitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure. In Florian Floyd Mueller, Kyburz P, Williamson JR, Sas C, Wilson ML, Dugas PT, Shklovski I, editors, CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. 2024. 281. (Conference on Human Factors in Computing Systems - Proceedings). Epub 2024 May 11. doi: 10.1145/3613904.3642850

Eternagram: Probing Player Atitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure

Abstract

Publication series

Conference

Research Keywords

UN SDGs

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this