Published Content
Generative AI Can Revitalize College Writing
Sean Pears
There is a broad decision architecture in conversations about Generative AI that breaks down into enthusiasts about the technology and pessimists about its cultural, political, and economic impacts. This breakdown has impeded the theorization of the emergent value and purpose of College Writing courses in a world of Generative AI chatbots. The article presents 12 theses that attempt to offer a broad theoretical framework for educators teaching writing-intensive college courses, and a sample course syllabus framework that models how these could be applied. The theses address the stress points that the technology presents for both students and educators, discuss where and how productivity gains should be conceived, and outline how class time and assessment should be redesigned.
AI Conceptualization and Policy Permissibility
Zea Miller, Kashish Sachdeva, and Jake Walker
When universities create AI policies, they often conceptualize AI as something, such as a tool or a resource. This study questions whether such policies are affected by how they envision AI. In other words, is the permissible a function of conceptualization? To answer, R1 university policies were rated independently by three raters on two axes: conceptualization and permissibility. When visualized, the ratings clearly show that while AI qua TOOL does not inherently attach either to the restrive or permissive, AI qua RESOURCE does not attach to the restrictive. Ultimately, this study shows that universities are unlikely at this time to conceptualize AI as a resource and simultaneously ban it.
Developing Thinking through LLM-Assisted Writing: Hegelian Synthesis and Critical Thinking
Robert Deacon, PhD
Students can bypass much of the writing process and the critical thinking that comes with it when using Large Language Models (LLMs) such as ChatGPT. Single-stage writing assignments may have no value for students who use LLMs. This paper proposes Hegelian synthesis writing (dialectic writing) as a solution for this problem. Dialectic writing requires students to develop arguments in stages over time. The stages deepen perspective, lead to discovery, and may produce original conclusions composed of conflicting viewpoints. While students can use ChatGPT to brainstorm and practice thesis, antithesis, and synthesis essay form, this study shows ChatGPT does not evaluate texts truthfully and often fails to produce strong thesis/synthesis statements. Instructors who want to promote critical thinking must have students critically evaluate and revise ChatGPT outputs. Survey results from classwork using ChatGPT to produce synthesis essays show students are receptive to using ChatGPT to brainstorm and learn essay structure. The results also suggest students need more support to identify ChatGPT deficiencies in creativity, particularly with synthesis conclusions. LLMs can model dialectic writing, but students need clear expectations for their role in the writing process. In the age of LLMs, we must look to synthesize student and AI writing and have students emerge as better thinkers. Assignments that require students to evaluate and revise ChatGPT outputs and to create new conclusions appear best suited to produce this outcome.