转化式学习
工程伦理学
可持续发展
交叉口(航空)
社会学
生成语法
质量(理念)
计算机科学
化学
管理科学
知识管理
政治学
透视图(图形)
科学教育
科学知识社会学
课程
工程类
专业发展
教育技术
人工智能
工程教育
主流
课程开发
高等教育
范式转换
作者
Yali Li,Laura I. Tolosa,Francklin Iván Rivas Echeverría,Ronald Márquez
标识
DOI:10.1021/acs.jchemed.5c00819
摘要
High Resolution Image Download MS PowerPoint Slide 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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