Learner experience in artificial intelligence-scaffolded argumentation

Min Kyu Kim, Nam Ju Kim, Ali Heidari

Research output: Contribution to journalArticlepeer-review

Abstract

Writing academic arguments is a complex and demanding task, even for proficient tertiary students. At the same time, providing prompt support for individual students working on discipline-specific arguments is often challenging for instructors. Artificial Intelligence (AI) techniques enable automated and adaptive educational scaffolding. In this study, we leveraged Natural Language Processing (NLP) AI techniques in an AI-Supported Scaffolding (AISS) system to evaluate written arguments and present alternative writing examples that human experts might write. To evaluate a pilot version of AISS, we gathered mixed-method data from 14 students enrolled in two sections of the same graduate-level online course (6 students in Cohort 1 and 8 students in Cohort 2). We used the Tool for the Automatic Analysis of Cohesion (TAACO) to track revisions in written arguments. TAACO indices demonstrated that the students used AI-generated scaffolding to build stronger claims with more elaborate and cohesive ideas. In written reflections, students revealed the perceived value of AISS, and visual inspection of their writing indicates that students used AISS feedback to improve their arguments. The findings demonstrate the potential of AI to provide personalised scaffolding for academic argument composition.

Original languageEnglish (US)
JournalAssessment and Evaluation in Higher Education
DOIs
StateAccepted/In press - 2022

Keywords

  • Artificial intelligence
  • academic argument
  • expert model
  • personalised learning
  • scaffolding

ASJC Scopus subject areas

  • Education

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