Comparative evaluation of ChatGPT and google gemini in creative content generation

This study presents a systematic comparative evaluation of two leading large language models — OpenAI’s ChatGPT and Google’s Gemini — in the domain of creative content generation. As generative artificial intelligence tools proliferate across industries including media, marketing, education, and entertainment, understanding their relative capabilities in producing imaginative, stylistically varied, and contextually coherent text becomes increasingly essential. The evaluation employs a structured multi-criteria framework assessing originality, narrative coherence, stylistic adaptability, emotional resonance, and contextual relevance across five standardised creative task categories: short fiction, poetry, advertising copy, dialogue writing, and metaphor generation. Results indicate that both models demonstrate considerable creative competence, yet exhibit distinct qualitative profiles. The study contributes an empirical methodology for AI creative benchmarking with implications for practitioner selection, model development, and policy on AI-generated content attribution.

Authors

Publication Details

Journal
E-Learning and Digital Media
Published
2026-10-26
DOI
https://doi.org/10.1177/20427530261498358
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparative evaluation of ChatGPT and google gemini in creative content generation

S Gnanapriya, M Sengaliappan, K Anandan
E-Learning and Digital Media
Topic Modeling
article

Comparative evaluation of ChatGPT and google gemini in creative content generation

S Gnanapriya, M Sengaliappan, K Anandan
article en

Abstract

This study presents a systematic comparative evaluation of two leading large language models — OpenAI’s ChatGPT and Google’s Gemini — in the domain of creative content generation. As generative artificial intelligence tools proliferate across industries including media, marketing, education, and entertainment, understanding their relative capabilities in producing imaginative, stylistically varied, and contextually coherent text becomes increasingly essential. The evaluation employs a structured multi-criteria framework assessing originality, narrative coherence, stylistic adaptability, emotional resonance, and contextual relevance across five standardised creative task categories: short fiction, poetry, advertising copy, dialogue writing, and metaphor generation. Results indicate that both models demonstrate considerable creative competence, yet exhibit distinct qualitative profiles. The study contributes an empirical methodology for AI creative benchmarking with implications for practitioner selection, model development, and policy on AI-generated content attribution.

E-Learning and Digital Media
Openalex Percentile: Top 14%
Topic Modeling
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.

Laika access coming soon