ScholarWorks@숙명여자대학교

초록

· Research topics: This study aims to diagnose the instructional implementation characteristics and competency vulnerabilities of lower-performing faculty identified through course evaluations from the perspective of AI-based future teaching competencies, and to develop a customized teaching–learning support model. · Research background: With the advancement of AI technologies and the transformation of educational paradigms, future-oriented teaching competencies have become increasingly essential. However, current university faculty development systems are largely operated through standardized programs, resulting in insufficient systematic support for lower-performing faculty members. · Differences from prior research: This study reconceptualizes lower-performing faculty identified through course evaluations as a vulnerable group in competency transition and applies an AI-based future teaching competency framework as an analytical lens. In doing so, it proposes a cyclical faculty support model grounded in AI-driven diagnosis and intervention. · Research method: A mixed-methods approach was employed, targeting 111 faculty members classified as lower-performing based on the 2025 academic year course evaluations at University A. · Research results: The findings indicate that lower-performing faculty exhibited structural vulnerabilities in instructional preparation, student interaction, and feedback practices. Significant differences in self-perceived instructional implementation were also identified according to appointment type, leading to the proposal of an AI-based faculty support model. · Contribution points and expected effects: This study expands the role of course evaluation data as a diagnostic indicator of future teaching competency and presents an AI-driven personalized support framework. The findings contribute to the development of sustainable and inclusive faculty development systems in AI-integrated higher education environments.

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