Large Language Models (LLMs) are increasingly being used to enhance teaching and learning. This work focused on the Effectiveness of Meta(A), Gemini (B), and ChatGPT (C) AI in Life Science Library and Teaching Material. A prompt was used to generate AI data in the various LLMs, and the data was analyzed for validity, engagement and timeliness to measure the effectiveness and efficiency. 10 students were engaged, and a well-qualified, experienced processional teacher was engaged in the assessment. The personnel assessed the validity and appropriateness, and the students assessed the engagement capacity as well as the comprehensiveness of the generated data. Both used a 5-point Likert scale. Further analysis, which includes the time of generation. Examples and question count, descriptive statistics, and inferential statistics(Friedman ranking) were done with the aid of AI-assisted Excel software. Apart from the count, the results showed that ChatGPT and Gemini AI proved more effective. Most of the tested data proved that there is no significant difference between the values(p<0,05). This information is crucial for students, lecturers, Liberians and teachers, especially where resources and the environment are limited for learning.
Keywords: Artificial Intelligence, Large Language Models, Life Science Education, Comparative Analysis, Educational Technology
How to Cite
APA
Ehoche, E. E., Emmanuel, I. J., Johnson, A., Eban, N. J., Eko, E. W., & Elijah, D. (2026). Effectiveness of Meta AI, Chat GPT and Gemini AI in Life Science Technology, Teaching and Library Material, A Novice-Friendly Comparison. British Journal of Advanced Research, 1(2). https://doi.org/10.68263/BJAR-DZP76H4G
MLA
Ehoche, Elijah Edache, Ita Joseph Emmanuel, Adejoh Johnson, Nneayeba Josephine Eban, Elizabeth William Eko, and Dorcas Elijah. "Effectiveness of Meta AI, Chat GPT and Gemini AI in Life Science Technology, Teaching and Library Material, A Novice-Friendly Comparison." British Journal of Advanced Research, vol. 1, no. 2, 2026. DOI: https://doi.org/10.68263/BJAR-DZP76H4G
Chicago
Ehoche, Elijah Edache, Ita Joseph Emmanuel, Adejoh Johnson, Nneayeba Josephine Eban, Elizabeth William Eko, and Dorcas Elijah. "Effectiveness of Meta AI, Chat GPT and Gemini AI in Life Science Technology, Teaching and Library Material, A Novice-Friendly Comparison." British Journal of Advanced Research 1, no. 2 (2026). https://doi.org/10.68263/BJAR-DZP76H4G
References
Alkaissi, A., & McFarlane, S. I. (2023). Artificial hallucinations in ChatGPT: Implications in scientific writing. _Cureus, 15_(2), e35179. https://doi.org/10.7759/cureus.35179
Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. _Journal of AI, 7_(1), 52–62. https://doi.org/10.61969/jai.1337500
Bhat, S., Singh, S., & Kumar, R. (2024). Chat GPT, Gemini or Meta AI: A comparison of AI platforms as a tool for answering higher-order questions in microbiology. _Journal of Postgraduate Medicine_. https://doi.org/10.4103/jpgm.jpgm_775_24
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. _International Journal of Educational Technology in Higher Education, 20_(1), 38. https://doi.org/10.1186/s41239-023-00408-3
Chiu, T. K. F. (2023). The impact of generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. _Interactive Learning Environments_. https://doi.org/10.1080/10494820.2023.2253861
Dai, Y., Liu, A., & Lim, C. P. (2023). Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education. _Procedia CIRP, 119_, 84–90. https://doi.org/10.1016/j.procir.2023.05.002
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI. _International Journal of Information Management, 71_, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. _Learning and Individual Differences, 103_, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kaur, G., & Sharma, P. (2025). Comparative performance of ChatGPT, Gemini, and final-year emergency medicine clerkship students in answering multiple-choice questions. _International Journal of Emergency Medicine, 18_, 42. https://doi.org/10.1186/s12245-025-00789-1
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., ... & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education. _PLOS Digital Health, 2_(2), e0000198. https://doi.org/10.1371/journal.pdig.0000198
Okonkwo, C., & Patel, R. (2024). Comparison of Large Language Models in Generating Machine Learning Curricula in High Schools. _Education Sciences, 14_(8), 845. https://doi.org/10.3390/educsci14080845
Salvagno, M., Taccone, F. S., & Gerli, A. G. (2023). Can artificial intelligence help for scientific writing? _Critical Care, 27_(1), 75. https://doi.org/10.1186/s13054-023-04380-2
Zhu, Y., Wu, Y., Zhao, L., & Zheng, Y. (2023). A comparative study of ChatGPT and other AI tools in educational applications. _Education and Information Technologies_. https://doi.org/10.1007/s10639-023-12185-2