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.