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The study aimed to enhance students’ investigative competencies and transform research training by integrating Artificial Intelligence (AI) technologies in business administration and engineering education. The research explores the application of AI tools to optimize scientific research, automate repetitive tasks, and improve data analysis in university education. A mixed-method, descriptive research approach was employed, involving three phases: (1) designing an assessment battery to measure investigative competencies, (2) evaluating AI tools relevant to research processes, and (3) implementing an active learning-based training program with AI integration. The study was conducted with postgraduate students in a virtual format. The integration of AI tools, including SciSpace, Consensus, and Claude, significantly improved investigative competencies. The pilot program demonstrated that 83.27% of students achieved a high level of competence. The applied assessment scale showed high reliability (Cronbach’s Alpha = 0.966). AI has great potential to transform research training by improving efficiency and analytical capabilities. However, ethical considerations, such as data integrity and fairness, must be addressed. Future research should focus on validating methodologies across diverse academic disciplines.

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Integration of Artificial Intelligence Technologies for Training in Scientific Research in Business Administration and Engineering at the University Level

  • 章节

  • 发表日期：

    2026年

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  • Ariel Adolfo Rodríguez-Hernández,
  • Fanny Avella-Forero,
  • Fredy Yesid Mesa

摘要

The study aimed to enhance students’ investigative competencies and transform research training by integrating Artificial Intelligence (AI) technologies in business administration and engineering education. The research explores the application of AI tools to optimize scientific research, automate repetitive tasks, and improve data analysis in university education. A mixed-method, descriptive research approach was employed, involving three phases: (1) designing an assessment battery to measure investigative competencies, (2) evaluating AI tools relevant to research processes, and (3) implementing an active learning-based training program with AI integration. The study was conducted with postgraduate students in a virtual format. The integration of AI tools, including SciSpace, Consensus, and Claude, significantly improved investigative competencies. The pilot program demonstrated that 83.27% of students achieved a high level of competence. The applied assessment scale showed high reliability (Cronbach’s Alpha = 0.966). AI has great potential to transform research training by improving efficiency and analytical capabilities. However, ethical considerations, such as data integrity and fairness, must be addressed. Future research should focus on validating methodologies across diverse academic disciplines.