Artificial Intelligence in Educational Research

Abstract

The proliferation of textual data in academic literature necessitates accelerating qualitative research methodologies. Text mining, underpinned by artificial intelligence and natural language processing, emerges as a transformative solution. This study analyzes how AI-integrated qualitative data analysis software such as ATLAS.ti and MAXQDA have streamlined processes like automatic coding and summarization since early 2023. These tools now facilitate rapid preliminary reviews through summarization features and obviate programming expertise through intuitive interfaces. Key advantages include drastic reductions in manual coding time through AI coding, enrichment of inductive coding systems via semantic analysis-based sub-code suggestions, and insights-driving code commenting summaries. Deep learning models unlocked by such tools will enable discernment of increasingly intricate patterns, improving educational interventions through real-time strategies informed by empirical findings. However, responsible use requires human oversight to refine coding and interpret nuanced results. While propelling qualitative research to unprecedented scales and depths, text mining also poses challenges around potential oversight neglect and lack of ethical guidelines. Optimizing these tools ensures accurate, responsible analyses that revolutionize understanding complex educational processes. AI ultimately enhances social science and education research outcomes through large-scale textual data analysis.