LLM-assisted sentiment analysis for integrated computational and qualitative mixed methods education research: A case study of students' written reflection assignments

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.

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

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Citation

Xiomara Gonzalez, Gabriella Coloyan Fleming, Andrew Katz, Maya Denton, Jessica Deters (2026). LLM-assisted sentiment analysis for integrated computational and qualitative mixed methods education research: A case study of students' written reflection assignments. arXiv:2605.27403. arXiv preprint.