Abstract:

This paper presents a structured, two-stage method for conducting literature reviews, illustrated with a case study on 3D printing in university engineering education. The methodology replaces traditional, intuition-driven approaches with a formalized process. We outline the taxonomy and describe the technical aspects, including the integration of AI tools for scalability and Python scripting for data organization. The approach offers practical strategies for managing complex, interdisciplinary literature surveys. Our findings highlight an efficient and transparent methodology that can support academic projects in educational technology.

This paper presents a rigorous, two-stage methodological procedure, developed during the preparation of a comprehensive literature review on the integration of 3D printing in university education.

The core of the methodology is a systematic coding and labeling protocol. In the first stage, an extensive body of literature on the subject was rigorously categorized using a set of thematic questions (e.g., pedagogical approaches, challenges, and curriculum integration) and then AI was used to obtain summary of individual papers. This structured approach, realized through set of precisely defined questions for content assessment, allowed us to convert qualitative research content into a structured database (JSON format for individual papers, Excel for larger data sets) for rigorous, reproducible analysis. Some elements of input-output operations were conducted using Python scripts and filtered output can be generated in a BibTeX format.

The second stage used the structured data from the first stage to perform a planned synthesis of the reviewed material. With the assistance of AI tools, this stage enabled a multidimensional analysis of key findings, emerging themes, and existing research gaps.

Keywords:

Literature Review Methodology, 3D Printing in Education, Higher Education, Content Analysis, Educational Technology.