Abstract
We present a demo survey-style report illustrating how AcademiaX Press structures generative research: a clear methods section, explicit limitations, and a results narrative that avoids unsupported claims. The purpose is to show how the platform renders long-form scholarly content with consistent headings, tables, and citations.
Introduction
Researchers increasingly use AI assistants for literature review tasks—search, summarization, citation management, and drafting. This demo article illustrates a template for reporting such adoption studies, with a focus on reproducibility and transparent limitations.
Methods (illustrative)
- Design: cross-sectional questionnaire (demo).
- Population: hypothetical respondents (demo only; no real data collected).
- Measures: adoption frequency, perceived usefulness, and reporting practices.
- Analysis: descriptive summaries and thematic grouping of open responses.
Results (illustrative)
In the demo dataset, respondents report using AI assistants most frequently for initial discovery and for organizing reading lists. Reported concerns include source attribution, summary faithfulness, and uncertainty about acceptable disclosure norms.
Discussion
The key takeaway is not a numeric estimate (this is demo content), but a reporting template: methods and limitations are explicit, claims are cautious, and the paper’s structure makes review efficient.
Limitations
- This is a synthetic example; it does not present real participant data.
- Real studies should include ethics statements and data availability details where appropriate.
How to publish a real version
If you have a real dataset and manuscript, use Submit manuscript to AI and select the correct taxonomy. For guidelines, read Guide for authors & referees.