• Teaching CRAAP to Robots: Artificial Intelligence, False Binaries, and Implications for Information Literacy

      Seeber, Kevin; University of Colorado Denver (The University of Arizona, 2018-11)
      Researchers studying artificial intelligence and semantic computing are developing algorithms capable of processing large amounts of textual data and rendering judgment on its contents. Specifically, the field of sentiment analysis is focused on creating code that applies what programmers call “common sense” to evaluate whether writing is factual or opinionated, as well as how emotional the author was. This presentation will argue that these algorithms rely on false binaries, over-simplification, and poorly-constructed checklists, similar to the approach often used when discussing information literacy with first-year college students. Instead of employing this approach, this session will argue that librarians must recognize that human interpretation lies at the core of information literacy, and that we need to embrace that complexity rather than depend on algorithmic evaluation.
    • Using Synchronous Posting to Locate Student Pain Points

      Binnie, Naomi; University of Michigan (The University of Arizona, 2018-11)
      The nature of undergraduate library instruction sessions means we often do not see the same students more than once. We rarely begin the class knowing students’ names, their majors, their confidence levels or how they’re feeling that day. I will discuss beginning my instruction sessions with a student activity featuring synchronous, anonymous posting in an effort to create a safe space and to empower students by centering their voices, particularly the voices of students from marginalized communities who may not feel safe in the typical classroom environment. I will discuss how I assess student needs, pain points, and confidence levels in the beginning of class rather than at the end.