Automating Systematic Literature Review with Responsible AI:

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 ScopusA filtered list of 60 semantically relevant articlesExtracted QA responses for five thematic domains (physical environment, social perception, design strategies, behavioral aspects, and barriers)TF-IDF keyword importance scores and categorized outputsPython scripts that implement the entire NLP-based analysis pipelineThe 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.