Artificial Intelligence (AI)-Augmented “Living” Meta-Analyses toward Critical Thinking Engagement in Chemical Education and Research: A Case Study of Nanocellulose-Stabilized Pickering Emulsions

As research on sustainable and advanced materials accelerates
(e.g.,
cellulose-based composites and functionalized nanomaterials), research
output has expanded rapidly, increasing complexity and making it challenging
for industrial and engineering chemistry researchers to maintain comprehensive,
up-to-date data compilation and analysis. Therefore, traditional meta-analyses,
even when attempting to adhere to systematic methodologies such as
the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA), face challenges with manual data curation, risking oversights
and impacting reproducibility in the face of such volume. Updated
meta-analysis methodologies are necessary to critically assess advances
and new technologies for the upscaling processes and innovation in
the advanced materials field. To address this, we propose a novel
framework for creating “living” meta-analyses augmented
by artificial intelligence (AI). We explore these concepts via a tutorial
case study on nanocellulose-stabilized Pickering emulsions, illustrating
how the integration of AI-based extraction with bibliometric mapping
reveals patterns, identifies research gaps, and enables the deployment
of informed decisions for future research and accelerated product
development in academia and industry. Integrating Large Language Models
(LLMs), such as ChatGPT and Gemini, with bibliometric platforms (e.g.,
ACS CAS SciFinder, Scopus, Dimensions) and VOSviewer, allowed one
to systematically curate and synthesize data from over 50 publications.
The resulting interactive platform reveals complex relationships among
nanocellulose properties (e.g., type, modification, concentration),
processing conditions, and emulsion characteristics (e.g., droplet
size, stability). The database and software are available at 10.5281/zenodo.15808694. We critically discuss the current limitations of LLMs in performing
meta-analyses in the chemical engineering and advanced materials fields
and emphasize the role of “human-in-the-loop” expertise
in interpretation.