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AI and Sustainability Communication: Research and Education Perspectives

Holmberg, Nils LU orcid and Eksell, Jörgen LU orcid (2026)
Abstract
Purpose & Strategic Communication Relevance: This conceptual chapter examines how artificial intelligence can support strategic sustainability communication research and education. Integrating communication theory, prior studies, and illustrative cases, it shows how AI can analyze organizational messages, examine audience responses, and support the training of future communicators. It connects AI methods to core strategic communication concerns, including legitimacy, stakeholder relations, framing, credibility, and communication ethics.

Design, Methods, and Scope: The research follows a sequential mixed-methods logic in which computational content analysis identifies textual and visual message features, while cognitive... (More)
Purpose & Strategic Communication Relevance: This conceptual chapter examines how artificial intelligence can support strategic sustainability communication research and education. Integrating communication theory, prior studies, and illustrative cases, it shows how AI can analyze organizational messages, examine audience responses, and support the training of future communicators. It connects AI methods to core strategic communication concerns, including legitimacy, stakeholder relations, framing, credibility, and communication ethics.

Design, Methods, and Scope: The research follows a sequential mixed-methods logic in which computational content analysis identifies textual and visual message features, while cognitive communication effects designs examine how those features influence attention, trust, and behavioral engagement. Established analytical applications of AI, such as text classification, computer vision, and human-subject experiments, are treated as comparatively mature, whereas synthetic audiences and fully generative stimulus design are discussed more cautiously.

Main Arguments and Educational Implications: The chapter argues that AI is most valuable when it supports transparent, theory-led inquiry rather than replacing human interpretation. Authenticity is associated with specific claims, organization-specific evidence, and consistency between text, visuals, and documented practice, while greenwashing is linked to vague claims and generic eco-symbolism. The chapter translates these insights into teaching themes focused on strategic framing, audience effects, stakeholder relations, and ethical reflexivity. (Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
in press
subject
keywords
Sustainability Communication, Computational Content Analysis (CCA), Cognitive Communication Effects (CCE), Artificial Intelligence, Communication Education, Research Methods
host publication
Artificial Intelligence and Strategic Communication: Practices, Challenges and Discussions
editor
Telles, Alda ; Falkheimer, Jesper and Fernández Muñoz, Cristóbal
publisher
ICNOVA Books
language
English
LU publication?
yes
id
d0106d41-9965-4fcb-8a88-2f1117990842
date added to LUP
2026-05-20 08:16:08
date last changed
2026-05-27 11:34:47
@inbook{d0106d41-9965-4fcb-8a88-2f1117990842,
  abstract     = {{Purpose &amp; Strategic Communication Relevance: This conceptual chapter examines how artificial intelligence can support strategic sustainability communication research and education. Integrating communication theory, prior studies, and illustrative cases, it shows how AI can analyze organizational messages, examine audience responses, and support the training of future communicators. It connects AI methods to core strategic communication concerns, including legitimacy, stakeholder relations, framing, credibility, and communication ethics.<br/><br/>Design, Methods, and Scope: The research follows a sequential mixed-methods logic in which computational content analysis identifies textual and visual message features, while cognitive communication effects designs examine how those features influence attention, trust, and behavioral engagement. Established analytical applications of AI, such as text classification, computer vision, and human-subject experiments, are treated as comparatively mature, whereas synthetic audiences and fully generative stimulus design are discussed more cautiously.<br/><br/>Main Arguments and Educational Implications: The chapter argues that AI is most valuable when it supports transparent, theory-led inquiry rather than replacing human interpretation. Authenticity is associated with specific claims, organization-specific evidence, and consistency between text, visuals, and documented practice, while greenwashing is linked to vague claims and generic eco-symbolism. The chapter translates these insights into teaching themes focused on strategic framing, audience effects, stakeholder relations, and ethical reflexivity.}},
  author       = {{Holmberg, Nils and Eksell, Jörgen}},
  booktitle    = {{Artificial Intelligence and Strategic Communication: Practices, Challenges and Discussions}},
  editor       = {{Telles, Alda and Falkheimer, Jesper and Fernández Muñoz, Cristóbal}},
  keywords     = {{Sustainability Communication; Computational Content Analysis (CCA); Cognitive Communication Effects (CCE); Artificial Intelligence; Communication Education; Research Methods}},
  language     = {{eng}},
  publisher    = {{ICNOVA Books}},
  title        = {{AI and Sustainability Communication: Research and Education Perspectives}},
  year         = {{2026}},
}