Published Content
Artificial Intelligence in a Writing Intensive Lifespan Development Course: Rates and Functions of Student Usage
Ashlie Lester and Wenting Sun
Artificial intelligence (AI) has transformed usual processes of higher education, including teaching with writing. Grounded in sociocultural theory, this study considers the role of generative AI (GenAI) within students’ zone of proximal development, where learning is supported through scaffolding that bridges what students can do independently and what they can achieve with guidance. Students (n = 114) enrolled in a writing intensive, lifespan development course in Fall 2024 completed eight writing assignments and disclosed any GenAI use; specifically, they could submit no more than a paragraph of their writing to a GenAI tool to solicit feedback that could be applied throughout the papers. Required AI disclosure statements revealed infrequent GenAI use, with only 22 unique users of GenAI for less than 8% of the assignments. Thematic analysis of the disclosure statements revealed three themes of use: writing mechanics, higher-order writing concerns, and content understanding. Despite students reporting that GenAI feedback was helpful, the impact on writing scores and final grades were mixed. Findings highlight the complex role of GenAI as a potential scaffolding learning assistant and suggest implications for instructors seeking to strategically incorporate GenAI into teaching with writing.
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.