Exploring Trustworthiness of AI in Higher Education Teaching, Learning, and Assessment: A Systematic Review of the Literature

• In Event: Thursday Roundtable Session 2:30 pm
  In Roundtable Session: Learning and Assessment (Table 37)

Abstract

While trust is an essential aspect of implementing AI in Higher Education courses, the field is still grappling with what makes an AI technology trustworthy to students, faculty, administrators, or policymakers. To understand how trust is being considered, we conducted a systematic review of 127 peer-reviewed articles focused on AI in Higher Education teaching, learning, and assessment. Finding that few articles discuss trust, and - when they do - it is to tout the trustworthiness of their novel AI design, we compare how trustworthiness is defined in other fields (e.g., health care, computer science) to highlight gaps for future research.

Authors

• Virginia Byrne, Morgan State University

• Keshiyena Pieters, Morgan State University