Dataset and Code for Automating Systematic Literature Review with Responsible AI: A PRISMA + NLP Framework Applied to Intergenerational Pedestrian Environments

This dataset accompanies a systematic literature review that investigates how pedestrian environments support intergenerational exchange, with a focus on extracting design-relevant features using Natural Language Processing (NLP) tools. The study integrates the PRISMA framework with AI-assisted techniques such as semantic similarity filtering, question answering (QA) models, and TF-IDF-based keyword analysis. The dataset includes: Metadata of 5,571 academic articles retrieved from Scopus A filtered list of 60 semantically relevant articles Extracted QA responses for five thematic domains (physical environment, social perception, design strategies, behavioral aspects, and barriers) TF-IDF keyword importance scores and categorized outputs Python scripts that implement the entire NLP-based analysis pipeline The data helps identify key spatial, perceptual, and behavioral elements that facilitate or inhibit intergenerational interaction in urban pedestrian spaces. It can be reused for benchmarking NLP-based literature analysis, urban design research, or Responsible AI implementation in systematic reviews. All intermediate results are traceable, and the analysis is reproducible using the provided source code and instructions.