Mon, May 19, 2025
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University of Cape Coast
Abstract: (35 Views)
It has been debated that to enhance data interpretation, and improve knowledge of environmental phenomena, Artificial Intelligence (AI) and Mixed Method Research (MMR) implementation and integration is of great value. However, not much is known about its actual use with environmental science data. Hence, the main aim of this study was to demonstrate how MMR and AI can improve environmental science data interpretation and analysis. The study adopted a case study titled “Willingness to Accept and Use Biogas Generated from Animal Manure and Agricultural Residue among University Students in Ghana” as the research that generated the data. A sequential explanatory MMR design was adopted. The survey collected data from (N= 231) and 5 in-depth interviews from Ghanaian University students. Through this study, it was found that majority of university students 112 (48.3%) are willing to install biogas in their future and homes and also 101(43.5%) are currently willing to use the energy. Again, the results showed that to integrate AI for a better understanding of environmental science research, researchers must first have a solid understanding of how to conduct MMR and obtain a reasonable picture of the main findings of the research conducted using statistical tools. Furthermore, the study found that AI was able to establish a relationship between both qualitative and quantitative data in an innovative way that provided answers to environmental issues, based on the results of the case study that was used. Additionally, the researcher must endeavor to supply appropriate prompts, the dataset, provide the framework that will guide the AI for enhanced data interpretation
Keywords: Artificial Intelligence, Mixed Methods Research, Environmental Science, Biogas, environmental science
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