Utilization of artificial intelligence and machine learning in chemistry education: a critical review

The current study aimed to criticize the existing literature on the utilization of artificial intelligence (AI) and machine learning (ML) in teaching and learning chemistry. A comprehensive critical literature review was conducted using electronic databases such as Scopus, PubMed, ISI, Google Scholar, ERIC, Web of Science, and JSTOR. In this regard, 62 articles were extracted from these electronic databases. During the selection of the literature inclusion and exclusion criteria were applied. The inclusion criteria include empirical and theoretical studies examining the effectiveness, challenges, and opportunities of AI/ML, and articles from 2018 to 2024 and written in English. On the other side, the exclusion criteria include literature that unrelated to education, lacking empirical evidence, or not peer-reviewed, as well as non-English publications, and published before 2018. This was done to gain insights into the current implementation status of AI and ML as well as critical issues of using these approaches in chemistry education. The study employed a critical review of the literature, which involves a critical analysis of the themes and concepts that emerge from the selected literature and identifies the opportunities and challenges surrounding the utilization of these technologies. The results revealed that there are opportunities for the integration of AI and ML in chemistry education, including personalized learning experiences, teacher assistance, and accessibility to learning materials. In this regard, intelligent tutoring systems and adaptive learning platforms were identified as potential aides for teachers in various aspects of teaching. The study also revealed the limitations and challenges surrounding AI and ML, such as the dependence on preexisting data, potential biases in models, and concerns around data privacy and security. Moreover, the findings also indicated that the implementation of AI and ML in chemistry education is still in its juvenile stage. Thus, teacher training programs are needed to equip teachers with the necessary skills for the use of these technologies effectively in the classroom. In addition, more efforts should be made to facilitate research, collaboration, and the development of policies and regulations that ensure responsible use of these technologies in the teaching and learning process.