Abstract

This TREO explores the use of Artificial Intelligence (GenAI) to enhance research question generation in the field of Location Analytics, particularly in educational settings. With the growing accessibility of public geospatial datasets such as US Census TigerLine files, American Community Survey (ACS) data, Homeland Infrastructure Foundation-Level Data (HILFD) database, ESRI Business Analyst datasets, and Social Explorer resources, students and researchers have unprecedented opportunities to conduct impactful, data-driven spatial research. The presentation will briefly illustrate the categories and types of data publicly available, such as demographics, economics, crime, and geographic boundaries and demonstrate how AI tools can assist in generating research questions. For example, using ACS and crime datasets, AI might propose exploring the relationship influence of educational attainment or income level or both on crime at a county or census tract level. For example: • How does the proximity of public libraries (HIFLD) relate to educational attainment levels (census tract, ACS)? • Does household income ACS) affect retail density (ESRI Business Analyst)? AI-assisted research question development is designed to enhance student experience in class by fostering higher-quality, more creative inquiry. In a location analytics course, students can use AI tools to generate and refine research questions, with outcomes evaluated based on quality, originality, and user satisfaction. Manual development methods can serve as a comparison to highlight the added value AI brings to the research design process. The talk will conclude with a discussion on AI usage concerns in this context. Key questions for audience engagement include: - In which settings is it appropriate to use AI for research question generation? - Is it also appropriate to use AI for suggesting research methods? - Can a novel AI-generated question guide student research for conference or journal submission? The goal is to foster a critical dialogue about balancing innovation, ethics, and educational rigor when integrating AI into research practices.

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