Translating UNESCO Artificial Intelligence Guidelines to Chemical Education and Its Intersection with Sustainable Development Goals

As the utilization of artificial intelligence (AI) and generative AI (GenAI) is expanding in the educational field, it presents profound implications for STEM disciplines, particularly chemistry and chemical engineering. This Perspective explores the integration of AI in education, drawing from UNESCO guidelines and global recommendations from 2022 to 2025, underscoring the imperative of a human-centered pedagogical approach. The analysis highlights the transformative potential of AI in educational practices, focusing on enhanced personalized learning, teacher training, and academic management, all of which are seen as possibly contributing to advancing sustainable development goal 4 (SDG 4, quality education). It also discusses the risk of epistemic drift, where reliance on opaque algorithms may detach scientific inquiry from a causal understanding. We show examples of prompt engineering techniques for scientific illustration generation in the fields of chemistry and physical chemistry, and discuss its advantages, and limitations. Furthermore, the rapid development of AI technologies has outpaced the policy debates in most academic institutions, creating a significant policy gap in higher education. This is coupled with global disparity, where most academic institutions in high-income countries have implemented AI-driven tools by 2025, while access in low-income regions remains constrained. We argue that to harness the potential benefits of AI, the chemical education community must move beyond technical adoption to foster critical AI chemical literacy. This involves targeted investments in digital infrastructure and the development of assessments that prioritize human reasoning over algorithmic output. We conclude that the responsible integration of AI requires a shift from a content delivery model to a knowledge creation model guided by the high-level ethical frameworks proposed by UNESCO.

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Publication Details

Journal
Journal of Chemical Education
Published
2026-02-12
DOI
https://doi.org/10.1021/acs.jchemed.5c00819
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

Translating UNESCO Artificial Intelligence Guidelines to Chemical Education and Its Intersection with Sustainable Development Goals

Laura Tolosa, Yali Li, Ronald Marquez, Francklin Rivas Echeverria
Journal of Chemical Education
Machine Learning in Materials Science
article

Translating UNESCO Artificial Intelligence Guidelines to Chemical Education and Its Intersection with Sustainable Development Goals

Laura Tolosa, Yali Li, Ronald Marquez, Francklin Rivas Echeverria
article en

Abstract

As the utilization of artificial intelligence (AI) and generative AI (GenAI) is expanding in the educational field, it presents profound implications for STEM disciplines, particularly chemistry and chemical engineering. This Perspective explores the integration of AI in education, drawing from UNESCO guidelines and global recommendations from 2022 to 2025, underscoring the imperative of a human-centered pedagogical approach. The analysis highlights the transformative potential of AI in educational practices, focusing on enhanced personalized learning, teacher training, and academic management, all of which are seen as possibly contributing to advancing sustainable development goal 4 (SDG 4, quality education). It also discusses the risk of epistemic drift, where reliance on opaque algorithms may detach scientific inquiry from a causal understanding. We show examples of prompt engineering techniques for scientific illustration generation in the fields of chemistry and physical chemistry, and discuss its advantages, and limitations. Furthermore, the rapid development of AI technologies has outpaced the policy debates in most academic institutions, creating a significant policy gap in higher education. This is coupled with global disparity, where most academic institutions in high-income countries have implemented AI-driven tools by 2025, while access in low-income regions remains constrained. We argue that to harness the potential benefits of AI, the chemical education community must move beyond technical adoption to foster critical AI chemical literacy. This involves targeted investments in digital infrastructure and the development of assessments that prioritize human reasoning over algorithmic output. We conclude that the responsible integration of AI requires a shift from a content delivery model to a knowledge creation model guided by the high-level ethical frameworks proposed by UNESCO.

Journal of Chemical EducationVol. 103(3)
The University of Texas at Arlington (US), Universitat de Girona (ES), Universidad de Los Andes (BO), Jiangxi University of Science and Technology (CN)
Quality Education
Openalex Percentile: Top 58%
Machine Learning in Materials Science
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