AI-DRIVEN LANGUAGE TOOLS AND DIGITAL STORYTELLING IN CLIMATE EDUCATION: BRIDGING SCIENCE, COMMUNICATION, AND BEHAVIORAL CHANGE

Climate change presents a multifaceted challenge that demands interdisciplinary solutions, particularly in education, language, and digital innovation. This study explores how integrating emerging technologies—such as Artificial Intelligence (AI), Natural Language Processing (NLP), and digital storytelling—can revolutionize climate communication and education. By bridging the gap between environmental science and linguistic innovation, this research examines how AI-driven language tools can enhance public awareness, policy engagement, and behavioral change. Through a case study approach, it highlights the role of interactive digital platforms in simplifying complex climate data, making it more accessible to diverse audiences. Additionally, it investigates the power of creative writing and narrative framing in fostering emotional connections to climate issues. The findings underscore the importance of interdisciplinary collaboration between educators, technologists, and environmental researchers in developing innovative strategies that not only inform but inspire action. This research contributes to the evolving discourse on climate change solutions by demonstrating how linguistic and technological advancements can drive sustainable transformation.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-06
DOI
https://doi.org/10.5281/zenodo.18505618
Primary Topic
Digital Storytelling and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-DRIVEN LANGUAGE TOOLS AND DIGITAL STORYTELLING IN CLIMATE EDUCATION: BRIDGING SCIENCE, COMMUNICATION, AND BEHAVIORAL CHANGE

VITALIS CHINEMEREM ILOANWUSI, O.J (Ph. D) Okeke
Zenodo (CERN European Organization for Nuclear Research)
Digital Storytelling and Education
article

AI-DRIVEN LANGUAGE TOOLS AND DIGITAL STORYTELLING IN CLIMATE EDUCATION: BRIDGING SCIENCE, COMMUNICATION, AND BEHAVIORAL CHANGE

VITALIS CHINEMEREM ILOANWUSI, O.J (Ph. D) Okeke
article en

Abstract

Climate change presents a multifaceted challenge that demands interdisciplinary solutions, particularly in education, language, and digital innovation. This study explores how integrating emerging technologies—such as Artificial Intelligence (AI), Natural Language Processing (NLP), and digital storytelling—can revolutionize climate communication and education. By bridging the gap between environmental science and linguistic innovation, this research examines how AI-driven language tools can enhance public awareness, policy engagement, and behavioral change. Through a case study approach, it highlights the role of interactive digital platforms in simplifying complex climate data, making it more accessible to diverse audiences. Additionally, it investigates the power of creative writing and narrative framing in fostering emotional connections to climate issues. The findings underscore the importance of interdisciplinary collaboration between educators, technologists, and environmental researchers in developing innovative strategies that not only inform but inspire action. This research contributes to the evolving discourse on climate change solutions by demonstrating how linguistic and technological advancements can drive sustainable transformation.

Zenodo (CERN European Organization for Nuclear Research)
Godfrey Okoye University (NG)
Climate action
Openalex Percentile: Top 63%
Digital Storytelling and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.