Generative AI vs. Traditional Databases: Insights from Industrial Engineering Applications
This study evaluates the efficiency and accuracy of Generative AI (GAI) tools, specifically ChatGPT and Gemini, in comparison with traditional academic databases for industrial engineering research. It was conducted in two phases. First, a survey was administered to 101 students to assess their fami...
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| Format: | Article |
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2304-6775/13/2/14 |
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| author | Jose E. Naranjo Maria M. Llumiquinga Washington D. Vaca Cristian X. Espin |
| author_facet | Jose E. Naranjo Maria M. Llumiquinga Washington D. Vaca Cristian X. Espin |
| author_sort | Jose E. Naranjo |
| collection | DOAJ |
| description | This study evaluates the efficiency and accuracy of Generative AI (GAI) tools, specifically ChatGPT and Gemini, in comparison with traditional academic databases for industrial engineering research. It was conducted in two phases. First, a survey was administered to 101 students to assess their familiarity with GAIs and the most commonly used tools in their academic field. Second, an assessment of the quality of the information provided by GAIs was carried out, in which 11 industrial engineering professors participated as evaluators. The study focuses on the query process, response times, and information accuracy, using a structured methodology that includes predefined prompts, expert validation, and statistical analysis. A comparative assessment was conducted through standardized search workflows developed using the Bizagi tool, ensuring consistency in the evaluation of both approaches. Results demonstrate that GAIs significantly reduce query response times compared to conventional databases, although the accuracy and completeness of responses require careful validation. A Chi-Square analysis was performed to statistically assess accuracy differences, revealing no significant disparities between the two AI tools. While GAIs offer efficiency advantages, conventional databases remain essential for in-depth literature searches requiring high levels of precision. These findings highlight the potential and limitations of GAIs in academic research, providing insights into their optimal application in industrial engineering education. |
| format | Article |
| id | doaj-art-f7d87fe3adfb4a8fb7c39d8a12baafc8 |
| institution | Kabale University |
| issn | 2304-6775 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Publications |
| spelling | doaj-art-f7d87fe3adfb4a8fb7c39d8a12baafc82025-08-20T03:29:35ZengMDPI AGPublications2304-67752025-03-011321410.3390/publications13020014Generative AI vs. Traditional Databases: Insights from Industrial Engineering ApplicationsJose E. Naranjo0Maria M. Llumiquinga1Washington D. Vaca2Cristian X. Espin3Facultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de Cotopaxi, Ave. Simón Rodríguez, Latacunga 050102, EcuadorFacultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de Cotopaxi, Ave. Simón Rodríguez, Latacunga 050102, EcuadorFacultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de Cotopaxi, Ave. Simón Rodríguez, Latacunga 050102, EcuadorFacultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de Cotopaxi, Ave. Simón Rodríguez, Latacunga 050102, EcuadorThis study evaluates the efficiency and accuracy of Generative AI (GAI) tools, specifically ChatGPT and Gemini, in comparison with traditional academic databases for industrial engineering research. It was conducted in two phases. First, a survey was administered to 101 students to assess their familiarity with GAIs and the most commonly used tools in their academic field. Second, an assessment of the quality of the information provided by GAIs was carried out, in which 11 industrial engineering professors participated as evaluators. The study focuses on the query process, response times, and information accuracy, using a structured methodology that includes predefined prompts, expert validation, and statistical analysis. A comparative assessment was conducted through standardized search workflows developed using the Bizagi tool, ensuring consistency in the evaluation of both approaches. Results demonstrate that GAIs significantly reduce query response times compared to conventional databases, although the accuracy and completeness of responses require careful validation. A Chi-Square analysis was performed to statistically assess accuracy differences, revealing no significant disparities between the two AI tools. While GAIs offer efficiency advantages, conventional databases remain essential for in-depth literature searches requiring high levels of precision. These findings highlight the potential and limitations of GAIs in academic research, providing insights into their optimal application in industrial engineering education.https://www.mdpi.com/2304-6775/13/2/14generative artificial intelligencecomparisonprocessBizagiChatGPTGemini |
| spellingShingle | Jose E. Naranjo Maria M. Llumiquinga Washington D. Vaca Cristian X. Espin Generative AI vs. Traditional Databases: Insights from Industrial Engineering Applications Publications generative artificial intelligence comparison process Bizagi ChatGPT Gemini |
| title | Generative AI vs. Traditional Databases: Insights from Industrial Engineering Applications |
| title_full | Generative AI vs. Traditional Databases: Insights from Industrial Engineering Applications |
| title_fullStr | Generative AI vs. Traditional Databases: Insights from Industrial Engineering Applications |
| title_full_unstemmed | Generative AI vs. Traditional Databases: Insights from Industrial Engineering Applications |
| title_short | Generative AI vs. Traditional Databases: Insights from Industrial Engineering Applications |
| title_sort | generative ai vs traditional databases insights from industrial engineering applications |
| topic | generative artificial intelligence comparison process Bizagi ChatGPT Gemini |
| url | https://www.mdpi.com/2304-6775/13/2/14 |
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