Presentation Information
[PL-02]From AI Infrastructure to Sustainable Student Competence: An XR-Based Semiconductor Equipment Learning Case
Jung hoon Joo (Korean Society for Engineering Education (KSEE))
Keywords:
generative AI,XR education,metaverse,semiconductor equipment,low-achieving students,sustainable competence,higher education
Since the public release of ChatGPT in November 2022, the rapid expansion of generative artificial intelligence has transformed not only digital services but also the scale and direction of technological investment. Massive GPU-based data centers are being planned and constructed in gigawatt-scale units, while semiconductor companies are accelerating investment in HBM technologies, manufacturing facilities, and AI infrastructure. These developments raise a fundamental question for higher education: Are universities cultivating students' substantive and sustainable capabilities, or merely equipping them with increasingly powerful technological tools?
This presentation examines an educational case in which extended reality (XR) and metaverse technologies were used to provide students with access to virtual learning environments simulating high-cost semiconductor mass-production equipment. The case focuses particularly on a low-achieving student who learned to design, train, and apply an artificial intelligence model through a year-long project and achieved a meaningful level of performance. The presentation analyzes how the student's learning process developed, what forms of support enabled the outcome, and whether the competencies demonstrated during the project can be consolidated as sustainable capabilities beyond the immediate learning environment.
The analysis is informed by interdisciplinary discussions among faculty members specializing in semiconductor equipment, computer science, physics education, and analytic philosophy. Particular attention is given to the distinction between temporary performance enhancement through digital tools and the formation of durable abilities such as conceptual understanding, problem-solving, self-directed learning, and reflective judgment. The presentation concludes by discussing the educational possibilities and limitations of XR-based semiconductor education in the era of AI infrastructure expansion, as well as the conditions under which emerging technologies can contribute to inclusive and sustainable university education.
This presentation examines an educational case in which extended reality (XR) and metaverse technologies were used to provide students with access to virtual learning environments simulating high-cost semiconductor mass-production equipment. The case focuses particularly on a low-achieving student who learned to design, train, and apply an artificial intelligence model through a year-long project and achieved a meaningful level of performance. The presentation analyzes how the student's learning process developed, what forms of support enabled the outcome, and whether the competencies demonstrated during the project can be consolidated as sustainable capabilities beyond the immediate learning environment.
The analysis is informed by interdisciplinary discussions among faculty members specializing in semiconductor equipment, computer science, physics education, and analytic philosophy. Particular attention is given to the distinction between temporary performance enhancement through digital tools and the formation of durable abilities such as conceptual understanding, problem-solving, self-directed learning, and reflective judgment. The presentation concludes by discussing the educational possibilities and limitations of XR-based semiconductor education in the era of AI infrastructure expansion, as well as the conditions under which emerging technologies can contribute to inclusive and sustainable university education.
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