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<p>The potential for artificial intelligence (AI) to make a transformative impact in the chemistry research and education field begins with the contributions of university administrators, instructors, and students. As a team of international authors from 9 global regions representing these 3 roles in the university, we use public data to argue for a re-thinking and update to the chemistry curriculum to train the next generation of scientists. To prepare researchers for the future with AI, chemistry education must be designed for new approaches to problem-solving and ways of thinking. Analysis of higher education AI chemistry courses was extracted from publicly available information based on the 2024 QS World University Ranking and separated into two categories: “computational coursework” and “AI/ML coursework”. The data revealed that institutions with existing computational coursework also tend to offer AI/machine learning coursework. The paucity of these courses may reflect different funding priorities or a shortage of faculty trained in AI chemistry. Overall, immediate users such as university teachers can apply the Technological Pedagogical Content Knowledge (TPACK)-AI framework, originally proposed by Mishra and Koehler (2006) to describe the synergistic intersection of teachers’ technological, pedagogical, and content knowledge, and recently extended to encompass AI (Mishra et al., 2023), for instructional design, inspiring curriculum material and pedagogies to be conducive to learning chemistry. While novel approaches to teaching and learning can face significant implementation challenges due to various cultures, pedagogical innovation must continue to remain a priority to ensure our training remains relevant for future students. In this paper, we encourage the development of normative frameworks within higher education that avoid unequal workloads on academic staff and ensure all stakeholders have a voice in the creation of curriculum and training of our students for the future.</p>

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Tipping Point in the Future Readiness of Artificial Intelligence and Chemistry Education

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月18日

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  • Jamie Z. Y. Khoo,
  • Fabian Hollinger,
  • Jia Yi Han,
  • Changxu Lin,
  • Chin-Chung Tsai,
  • Jennifer Kathleen Schwartz Poehlmann,
  • David Anzola,
  • Yvonne S. L. Choo,
  • Fun Man Fung

摘要

The potential for artificial intelligence (AI) to make a transformative impact in the chemistry research and education field begins with the contributions of university administrators, instructors, and students. As a team of international authors from 9 global regions representing these 3 roles in the university, we use public data to argue for a re-thinking and update to the chemistry curriculum to train the next generation of scientists. To prepare researchers for the future with AI, chemistry education must be designed for new approaches to problem-solving and ways of thinking. Analysis of higher education AI chemistry courses was extracted from publicly available information based on the 2024 QS World University Ranking and separated into two categories: “computational coursework” and “AI/ML coursework”. The data revealed that institutions with existing computational coursework also tend to offer AI/machine learning coursework. The paucity of these courses may reflect different funding priorities or a shortage of faculty trained in AI chemistry. Overall, immediate users such as university teachers can apply the Technological Pedagogical Content Knowledge (TPACK)-AI framework, originally proposed by Mishra and Koehler (2006) to describe the synergistic intersection of teachers’ technological, pedagogical, and content knowledge, and recently extended to encompass AI (Mishra et al., 2023), for instructional design, inspiring curriculum material and pedagogies to be conducive to learning chemistry. While novel approaches to teaching and learning can face significant implementation challenges due to various cultures, pedagogical innovation must continue to remain a priority to ensure our training remains relevant for future students. In this paper, we encourage the development of normative frameworks within higher education that avoid unequal workloads on academic staff and ensure all stakeholders have a voice in the creation of curriculum and training of our students for the future.