Automatic answering of scientific questions using the FACTS-V1 framework: New methods in research to increase efficiency through the use of AI

Stefan Pietrusky
12/1/2024
cs.DLcs.AI

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

The use of artificial intelligence (AI) offers various possibilities to expand and support educational research. Specifically, the implementation of AI can be used to develop new frameworks to establish new research tools that accelerate and meaningfully expand the efficiency of data evaluation and interpretation (Buckingham Shum et al., 2023). This article presents the prototype of the FACTS-V1 (Filtering and Analysis of Content in Textual Sources) framework. With the help of the application, numerous scientific papers can be automatically extracted, analyzed and interpreted from open access document servers without having to rely on proprietary applications and their limitations. The FACTS-V1 prototype consists of three building blocks. The first part deals with the extraction of texts, the second with filtering and interpretation, and the last with the actual statistical evaluation (topic modeling) using an interactive overview. The aim of the framework is to provide recommendations for future scientific questions based on existing data. The functionality is illustrated by asking how the use of AI will change the education sector. The data used to answer the question comes from 82 scientific papers on the topic of AI from 2024. The papers are publicly available on the peDOCS document server of the Leibniz Institute for Educational Research and Educational Information.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{pietrusky2024automatic,
  title  = {Automatic answering of scientific questions using the FACTS-V1 framework: New methods in research to increase efficiency through the use of AI},
  author = {Stefan Pietrusky},
  year   = {2024},
  eprint = {2412.07794},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.07794v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2412.07794.svg)](https://codeofpaper.com/paper/2412.07794)

Discussion