Overview
Artificial intelligence, specifically generative AI, presents both significant challenges and promising opportunities for qualitative research. On the opportunity side, AI can enhance data analysis by processing large volumes of qualitative data—such as interview transcripts, focus group discussions, and open-ended survey responses—arguably faster and more efficient than human researchers. It can identify patterns, themes, and insights that might be overlooked, thus enabling richer analysis. Additionally, AI-driven tools can aid in generating interview questions, synthesizing literature, and even drafting preliminary reports, saving researchers substantial time.
However, challenges remain prominent. One major concern is the potential for bias in AI outputs. If the underlying data used to train generative models are biased or unrepresentative, the AI can perpetuate or even amplify these biases, leading to skewed results. Transparency and interpretability of AI models are also problematic. Many generative AI systems function as “black boxes,” making it difficult for researchers to understand how specific outputs are produced, which undermines trust and academic rigor. Ethical concerns arise, especially when using AI to handle sensitive data; ensuring participant confidentiality and navigating consent issues in automated analysis require careful consideration.
Furthermore, while AI can aid in identifying patterns, it lacks the nuanced human understanding required to contextualize and interpret complex human behavior. Researchers must, therefore, strike a balance, using AI as a supplementary tool rather than a replacement for human insight. Ultimately, leveraging generative AI in qualitative research demands rigorous oversight, ethical diligence, and a complementary human-AI partnership to maximize benefits and mitigate risks.
The projects I’m pursuing seek to address many of these issues by conducting scoping reviews and researching how generative AI aligns with or runs counter to norms and accepted theory in qualitative research.
Funding
- Not seeking funding
Timeline
- Started: August 2024
- Ended: Ongoing
