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82 UEC Int’l Mini-Conference No.54

Exploring Effective AI Applications in Teaching Molecular
Structures for Secondary Chemistry Education

Jordão Laurindo Manuel Supervisor: Prof: Dr. Kayoko Yamamoto
The University of Electro-Communications The University of Electro-Communications
jordao.manuel@go.wt-jdpsr.jp Kayoko.yamamoto@uec.ac.jp

Abstract
Given the exponential growth of technologies with advancements in artificial intelligence (AI), AI chatbots have become effective
in interactive learning environments. Therefore, this study aimed to identify effective algorithms that can be used for teaching
molecular structures in secondary chemistry education.
Background Methodology Results
Ø Advances in artificial intelligence (AI) have Ø To develop the present study scoping Ø A careful analysis of the selected
transformed several fields, including review was done in the following studies revealed that chatbots and
education. AI can offer innovative terms: virtual laboratories are algorithms
approaches to help teachers and students Ø Scoping Review used in chemistry teaching.
in chemistry teaching. Ø Database: Google Scholar and Eric
Ø However, there is still little Ø Analysis period: Last 10 years
systematization of the applications of Identification of studies via databases
these technologies. This review Records identified Records removed before
screening:
through Databases
investigated the challenges and Identification (Google Scholar Duplicate records
opportunities providing an overview of n=160, Eric n=5 ) removed ( n = 0)
existing applications and their implications. Records screened Records excluded
Ø AI in education (n = 165 ) (n = 115)
Ø The use of artificial intelligence (AI) in
education has grown significantly, leading Screening Reports sought for Reports not retrieved
to research into the benefits and retrieval (n = 50 ) (n = 19 ) Figure 4: Benefits of AI in Education.
disadvantages it can bring to the Reports assessed Reports excluded: 8 Gaps
education sector [1& 2]. for eligibility (n= 31 ) Ø The scoping analysis led to the
Ø The integration of AI into education Studies included in identification of the following gaps:
enables personalized learning by Included review (n = 23) Ø Studies addressing the feedback
analyzing large datasets, identifying Figure 2: Flow chart diagram of identification teachers can receive from AI
teaching patterns, and improving studies. regarding their students'
instruction and inclusivity. However, it also performance are lacking.
raises concerns about data privacy, Table 1: Criteria used for inclusion and exclusion Ø A few studies have mentioned the
transparency, and ethics, necessitating a of articles. need to develop specific
balanced approach to ensure that AI Criteria algorithms for the subject of
enhances, rather than replaces, human Inclusion Exclusion chemistry.
teaching [3, 4 & 5 ]. Last 10 years 2014 and below Ø Few studies have addressed AI
Ø AI in Chemistry Education AI applications for AI applications in Higher adaptation within the context of
Secondary Education Education
Ø Visualization of molecular models and AI studies for AI studies related to each region.
experiment simulations. Chemistry Education other sciences Conclusion
Ø This research will seek to align its results English and Other languages Ø AI trends in chemistry education
with SDG 4 (Quality of Education), since Portuguese include virtual labs, molecular
empowering teachers to use AI is one of publications modeling, personalized learning,
the aspects that deserve attention. automated assessments, and virtual
assistants for continuous support.
Number of articles/Year Ø It is essential to consider the training
12 of teachers in the effective use of AI
tools while critically addressing the
9
associated ethical considerations.
Figure 1: SDG number 4 and subdivisions. Next Steps
Develop an interactive AI chatbot algorithm
Ø The purpose of this study is to explore AI in 0 0 0 0 0 1 1 to enhance secondary education learning in
secondary chemistry education and molecular structures. The algorithm focuses
analyze its uses, benefits, challenges, and 2016 2017 2018 2019 2020 2021 2022 2023 2024 on the following aspects.
impacts while identifying research gaps for Figure 3: Number of available articles in Efficient response time
Error awareness
future advancements. 10 years. Personalized teaching suggestions
References
[1] Baum, Z. J., Yu, X., Ayala, P. Y., Zhao, Y., Watkins, S. P., & Zhou, Q. (2021). Artificial intelligence in chemistry: current trends and future directions. Journal of Chemical Information and
Modeling, 61(7), 3197-3212.
[2] Feldman-Maggor, Y., Blonder, R., & Alexandron, G. (2024). Perspectives of generative AI in chemistry education within the TPACK framework. Journal of Science Education and Technology, 1-12.
[3] Abbas, T., Javed, U., Mehmood, F., Raza, M., & Li, H. (2024, September). ChemGenX: AI in the Chemistry Classroom. In Proceedings of the 2024 International Symposium on Artificial Intelligence for
Education (pp. 224-230).
[4] Onoyima, E. O., Orji, C. M., & GodBless, E. Unlocking The Power of Generative and Multimodal Artificial Intelligence in Chemistry Education: Redefining Learning in the Digital Age.
[5] Chiu, W. K. (2021). Pedagogy of emerging technologies in chemical education during the era of digitalization and artificial intelligence: A systematic review. Education sciences, 11(11), 709.