Abstract:

As Large Language Models (LLMs) become integral to higher education, many educators without technical backgrounds approach them with uncertainty or hesitation. This paper presents a minimal-risk, program-evaluation pilot study through a practitioner-led demonstration designed to help faculty experience firsthand how generative AI can shift learning from mechanical execution to creative interpretation. Using the metaphor of the “knife” and the “food processor,” 48 multidisciplinary educators completed five specific data-analysis tasks—first manually and then with AI assistance. Quantitative data for this study were collected from a follow-up survey (n=8), where participants provided explicit consent for anonymous data sharing. These results were supplemented by qualitative observations of the live session and a retrospective review of session recordings. To maintain privacy, all session insights were summarized at an aggregate level to identify broad pedagogical themes and technical challenges. The contrast between methods was immediate: manual approaches led to documented confusion and cognitive fatigue, while AI-assisted workflows produced accurate results within seconds. These aggregate observations suggest that AI can "regenerate" instructional time, allowing a focus on high-level discussion and pedagogical insight. By moving ‘beyond the knife,’ this demonstration positioned generative AI as a regenerative partner in education, lowering technical barriers while raising the ceiling for inquiry. This pilot focuses on immediate practitioner reflection rather than broad generalization.

Keywords:

Technology, education, development, LLM, STEM, AI in Education, Faculty Development, Pedagogical Innovation, Regenerative Teaching, Practitioner Reflection.