Automated Essay scoring has been explored as a research and industry problem
for over 50 years. It has drawn a lot of attention from the NLP community
because of its clear educational value as a research area that can engender the
creation of valuable time-saving tools for educators around the world. Yet,
these tools are generally focused on detecting good grammar, spelling mistakes,
and organization quality but tend to fail at incorporating persuasiveness
features in their final assessment. The responsibility to give actionable
feedback to the student to improve the strength of their arguments is left
solely on the teacher's shoulders. In this work, we present a transformer-based
architecture capable of achieving above-human accuracy in annotating
argumentative writing discourse elements for their persuasiveness quality and
we expand on planned future work investigating the explainability of our model
so that actionable feedback can be offered to the student and thus potentially
enable a partnership between the teacher's advice and the machine's advice.

This research paper presents a transformer-based architecture capable of achieving above-human accuracy in annotating argumentative writing discourse elements for their persuasiveness quality, with planned future work investigating the explainability of the model to provide actionable feedback and enable a partnership between the teacher's advice and the machine's advice.