Gonzalez et al. (2026) — Virginia Tech / University of Texas at El Paso. arXiv preprint.
LLM-Assisted Sentiment Analysis for Mixed-Methods Education Research demonstrates how LLMs can serve as scalable qualitative research assistants, enabling researchers to investigate multiple demographic variables simultaneously rather than being limited to simple binary comparisons. Using 151 longitudinal written reflections from a study abroad program, the authors show that LLM-assisted sentiment analysis combined with statistical testing can uncover granular patterns: prior experience living abroad was the only personal variable that significantly impacted students' sentiments about their language and communication behaviors. This workflow bridges computational and qualitative methods, suggesting that LLMs can reduce the bottleneck of manual qualitative coding without replacing the interpretive depth of thematic analysis. The approach has implications for higher-ed research methodology, complementing existing learning-analytics pipelines and extending mixed-methods capabilities beyond what has been possible with automated-grading and formative-assessment systems alone. The paper connects to discussions about faculty-development in equipping researchers with AI literacy for methodological innovation, and relates to ai-literacy as both a tool for researchers and a consideration in how computational methods change the practice of qualitative inquiry.
Key Findings
- LLM-assisted sentiment analysis enables comparison across 7 identity/lived-experience variables simultaneously
- Only prior experience living abroad significantly impacted students' communication sentiments
- The workflow preserves qualitative depth while adding statistical power
- Implications for learning-analytics and student-experience research methodology
Related Pages
- automated-grading — AI systems for scoring student work
- intelligent-tutoring — AI tutoring systems and architectures
- ai-literacy — Frameworks for understanding and using AI
- formative-assessment — Assessment for learning and feedback
- llm — Large language models in education
- generative-ai — Generative AI applications and implications
- higher-ed — AI in higher education contexts
- a4l-analytics-pipeline — A4L modular analytics pipeline for cross-domain educational AI data