Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies
<|reference_start|>Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies: This study examines the impact of Generative Artificial Intelligence (GenAI) on academic research, focusing on its application to qualitative and quantitative data analysis. As GenAI tools evolve rapidly, they offer new possibilities for enhancing research productivity and democratising complex analytical processes. However, their integration into academic practice raises significant questions regarding research integrity and security, authorship, and the changing nature of scholarly work. Through an examination of current capabilities and potential future applications, this study provides insights into how researchers may utilise GenAI tools responsibly and ethically.   We present case studies that demonstrate the application of GenAI in various research methodologies, discuss the challenges of replicability and consistency in AI-assisted research, and consider the ethical implications of increased AI integration in academia. This study explores both qualitative and quantitative applications of GenAI, highlighting tools for transcription, coding, thematic analysis, visual analytics, and statistical analysis. By addressing these issues, we aim to contribute to the ongoing discourse on the role of AI in shaping the future of academic research and provide guidance for researchers exploring the rapidly evolving landscape of AI-assisted research tools and research.<|reference_end|>
@article{perkins2024generative,
    title={Generative AI Tools in Academic Research: Applications and Implications
  for Qualitative and Quantitative Research Methodologies},
    author={Mike Perkins (1), Jasper Roe (2) ((1) British University Vietnam, (2)
  James Cook University Singapore)},
    journal={arXiv preprint arXiv:2408.06872},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06872},
    primaryClass={cs.HC cs.AI}
}