From Algorithmic Output to Academic Judgment: the Relational Role of Librarians in AI-Mediated Learning

Abstract

The increasing use of generative artificial intelligence in higher education has changed how students conduct research, write assignments, and engage with course content. While AI tools can support efficiency and access to information, they also present challenges related to academic integrity, equity, and the development of critical judgment. This presentation examines the role of academic librarians in supporting relational teaching within AI-mediated learning environments. Using a relational teaching framework, the paper highlights how librarians work with students—and in collaboration with faculty—to move learning beyond algorithmic output toward thoughtful academic judgment. Through research consultations, classroom instruction, and course-integrated support, librarians help students interpret AI-generated content, evaluate sources, and understand disciplinary expectations. These interactions emphasize dialogue, context, and ethical reasoning, reinforcing skills that are essential to academic success but cannot be automated. The presentation also considers equity and student success in the context of AI use. Students arrive at the university with varied levels of access to technology, research experience, and familiarity with academic norms. Relational, personalized support from librarians helps address these gaps by providing guidance that complements classroom instruction and supports responsible engagement with AI tools. Rather than focusing on punitive approaches to academic integrity, this relational model emphasizes transparency, learning, and skill development. By positioning the academic library as a key site of relational teaching, this presentation aligns with institutional commitment to inclusive, student-centered learning. It contributes to ongoing campus conversations about teaching, learning, and responsible AI use by highlighting how librarians support academic judgment, equity, and student success in the age of AI.

Ähnliche Arbeiten