Proposed Solutions for Improving the Effectiveness of AI Use in Learning and Research among Industrial Systems Engineering Students

This paper analyzes the current use of artificial intelligence (AI) in learning and research among students majoring in Industrial Systems Engineering in the context of the increasing integration of AI into higher education. Through a literature review and qualitative analysis of discipline-specific learning practices, the study indicates that students are increasingly using AI in academic activities; however, many still lack structured methods aligned with the characteristics of Industrial Systems Engineering. This situation may lead to overreliance on AI and a decline in active and independent thinking. In response to these challenges, the study proposes a six-step AI usage model that positions AI as a cognitive support tool rather than a substitute for human thinking. The model consists of selecting appropriate AI tools, structured AI dialogue, guided AI critique, information refinement, fact-checking, and synthesis combined with lecturer interaction. The proposed framework emphasizes learner-centered interaction, integrates AI with critical thinking, and incorporates a feedback mechanism that allows users to revisit earlier steps when results do not meet learning objectives. The proposed model is expected to guide students in using AI more effectively and responsibly in alignment with the system-oriented thinking required in Industrial Systems Engineering, while reducing the risks associated with inappropriate or uncritical use of AI in academic learning and research.