Descriptive Evaluation of Artificial Intelligence in Solving Problems Related to Basic Concepts of Population Genetics
Abstract
Population genetics is a study area in genetics that requires not only knowledge of genetic concepts, but also the numerical ability to predict the condition of allele frequencies in the population. In recent years, artificial intelligence (AI) has become part of the technological advances that humans utilize, including in studying population genetics. Therefore, it is necessary to ascertain that the information provided by AI has high accuracy and consistency. This research aimed to analyzed accuracy and consistency of several number of AI to solved the problem related to population topics. This research has been conducted using five different AIs (ChatGPT, Gemini AI, Deep AI, ChatSonix AI, and Perplexity AI). These five different AIs tested on two different laptops that connected to different accounts with different search histories. Each AI on the two laptops was asked by questions at six different levels of Bloom's taxonomy. All responses given by the AIs were manually corrected by two separate genetics lecturers from different institutions independently. Results showed that all AIs were highly accurate in their responses to all questions. On to the other side, each type of AI on two different laptops gave responses with different sentence structures, however, they had the same meaning.
Keywords
Full Text:
PDFReferences
Abdelaal, N. M., & Al Sawi, I. (2024). Perceptions, Challenges, and Prospects: University Professors' Use of Artificial Intelligence in Education. Australian Journal of Applied Linguistics, 7(1), n1.
Barthélémy, M., Claudia, C. (2023). 2. ChatGPT, a brand-new tool to strengthen timeless competencies. Frontiers in Education, doi: 10.3389/feduc.2023.1251163
Bendre, N., Desai, K., & Najafirad, P. (2021, October). Show why the answer is correct! towards explainable ai using compositional temporal attention. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 3006-3012). IEEE.
Bobula, M. (2024). Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications. Journal of Learning Development in Higher Education, (30).
Cano, C. A. G., & Troya, A. L. C. (2023). Artificial Intelligence applied to teaching and learning processes. LatIA, 1, 2-2.
Charlesworth, B., & Charlesworth, D. (2010). Elements of evolutionary genetics.
Crowe, A., Dirks, C., & Wenderoth, M. P. (2008). Biology in bloom: implementing Bloom's taxonomy to enhance student learning in biology. CBE—Life Sciences Education, 7(4), 368-381.
Duque, R. D. C. S., da Silva, J. S., Loureiro, V. J. S., Darcanchy, M., Eccard, A. F. C., Durigon, S., ... & de Oliveira, E. A. R. (2024). Tecnologias digitais associadas a ia na formação docente. Caderno Pedagógico, 21(4), e3651-e3651.
Ekellem, E. A. F. (2023, October). Conversational AI in Academia: A Practical Exploration with ChatGPT. In 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-4). IEEE.
Esplugas, M. (2023). The use of artificial intelligence (AI) to enhance academic communication, education and research: a balanced approach. Journal of Hand Surgery (European Volume), 48(8), 819-822.
Garant, D. (2007). Hartl, DL & AG Clark, 2007. Principles of Population Genetics. Écoscience, 14(4), 544–545.
Goodfellow, I. (2016). Deep learning. MIT press.
Gunjan, D., Rapheal, M., Asif, M., & Usha, R. (2024). P-256 AI-enhanced precision: unveiling the future of blastocyst quality assessment. Human Reproduction, 39(Supplement_1), deae108-626.
Grimes, M., Von Krogh, G., Feuerriegel, S., Rink, F., & Gruber, M. (2023). From scarcity to abundance: Scholars and scholarship in an age of generative artificial intelligence. Academy of Management Journal, 66(6), 1617-1624.
Hirosawa, T., Harada, Y., Tokumasu, K., Ito, T., Suzuki, T., & Shimizu, T. (2024). Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study. JMIR Medical Informatics, 12, e63010.
Imran, M., & Almusharraf, N. (2024). Google Gemini as a next generation AI educational tool: a review of emerging educational technology. Smart Learning Environments, 11(1), 22.
Islam, R., & Ahmed, I. (2024, May). Gemini-the most powerful LLM: Myth or Truth. In 2024 5th Information Communication Technologies Conference (ICTC) (pp. 303-308). IEEE.
Kelleher, J. D., & Tierney, B. (2018). Data science. MIT press.
Lai, K., Twine, N., O’brien, A., Guo, Y., & Bauer, D. (2018). Artificial intelligence and machine learning in bioinformatics. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 1(3).
Laland, K. N., Uller, T., Feldman, M. W., Sterelny, K., Müller, G. B., Moczek, A., ... & Odling-Smee, J. (2015). The extended evolutionary synthesis: its structure, assumptions and predictions. Proceedings of the royal society B: biological sciences, 282(1813), 20151019.
Láruson, Á. J., & Reed, F. A. (2021). Population genetics with R: an introduction for life scientists. Oxford University Press, USA.
Lazaridis, I., Patterson, N., Mittnik, A., Renaud, G., Mallick, S., Kirsanow, K., ... & Krause, J. (2014). Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature, 513(7518), 409-413.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321-332.
Lim, B., Lirios, G., Sakalkale, A., Satheakeeerthy, S., Hayes, D., & Yeung, J. M. (2024). Can AI answer my questions? Using Artificial Intelligence to help provide information for patients with a stoma.
Lin, Z. (2023). Why and how to embrace AI such as ChatGPT in your academic life. Royal Society open science, 10(8), 230658.
Marcus, G., & Davis, E. (2019). Rebooting AI: Building artificial intelligence we can trust. Vintage.
Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans.
Mulally, T. (2024). An experiential journey: A year of a professor using AI in the classroom and research. International Journal of Studies in Education and Science, 5(3), 246-256.
Nielsen, R., & Slatkin, M. (2013). An introduction to population genetics: theory and applications (p. 298). Sunderland, MA: Sinauer Associates.
Pinzolits, R. (2023). AI in academia: An overview of selected tools and their areas of application. MAP Education and Humanities, 4, 37–50.
Qurbonova, B., & Yusupova, A. (2024, May). The Deep Investigation of the Part of AI Integ in The Field of Education: A Technical Review. In 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 503-507). IEEE.
Sanchez, T., Cury, J., Charpiat, G., & Jay, F. (2021). Deep learning for population size history inference: Design, comparison and combination with approximate Bayesian computation. Molecular Ecology Resources, 21(8), 2645-2660.
Sánchez, L.J.L. (2023). Inteligencia artificial en la academia. Acta Médica Costarricense, 65(3), 111-112.
Sant’Anna, I. C., Tomaz, R. S., Silva, G. N., Nascimento, M., Bhering, L. L., & Cruz, C. D. (2015). Superiority of artificial neural networks for a genetic classification procedure.
Sarode, V., & K., Bhamare. (2023). Chat GPT and its Capabilities. International Journal for Research in Applied Science and Engineering Technology, doi: 10.22214/ijraset.2023.56355
Schrider, D. R., & Kern, A. D. (2018). Supervised machine learning for population genetics: a new paradigm. Trends in Genetics, 34(4), 301-312.
Schulze-Balhorn, L., Weber, J. M., Buijsman, S., Hildebrandt, J. R., Ziefle, M., & Schweidtmann, A. M. (2024). Empirical assessment of ChatGPT’s answering capabilities in natural science and engineering. Scientific Reports, 14(1), 4998.
Serdaliyev, Y. (2023). Applications of «chatgpt»: where it can be used and what can we solve with chatgpt. Қ.А. Ясауи атындағы Халықаралық қазақ-түрік университетінің хабарлары, doi: 10.47526/2023-1/2524-0080.12
Sheehan, S., & Song, Y. S. (2016). Deep learning for population genetic inference. PLoS computational biology, 12(3), e1004845.
Silva-Sánchez, C. A. (2022). Psychometric properties of an instrument to assess the level of knowledge about artificial intelligence in university professors. Metaverse Basic and Applied Research, 1, 14-14.
Singh, S., Manchekar, O., Patwardhan, A., Rote, U., Jagtap, S., & Chavan, D. H. (2021, May). Computer application for assessing subjective answers using AI. In Proceedings of the 4th International Conference on Advances in Science & Technology (ICAST2021).
Laurentin Táriba, H. E. (2023). Introduction to Genetics. In Agricultural Genetics: From the DNA Molecule to Population Management (pp. 3-7). Cham: Springer Nature Switzerland.
Tibbetts, J. H. (2018). The Frontiers of Artificial Intelligence: Deep learning brings speed, accuracy to the life sciences. Bioscience, 68(1), 5-10.
Triberti, S., Di Fuccio, R., Scuotto, C., Marsico, E., & Limone, P. (2024). “Better than my professor?” How to develop artificial intelligence tools for higher education. Frontiers in Artificial Intelligence, 7, 1329605.
Yang, Y., Yu, L., Bai, Y., Wang, J., Zhang, W., Wen, Y., & Yu, Y. (2017). An empirical study of AI population dynamics with million-agent reinforcement learning. CoRR.
Yang, X., Li, H., Ni, L., & Li, T. (2021). Application of artificial intelligence in precision marketing. Journal of Organizational and End User Computing (JOEUC), 33(4), 209-219.
Zekaj, R. (2023). AI language models as educational allies: Enhancing instructional support in higher education. International Journal of Learning, Teaching and Educational Research, 22(8), 120-134.
DOI: http://dx.doi.org/10.21043/jobe.v8i1.30260
Refbacks
- There are currently no refbacks.
Jobe Journal Indexed by :
