Developing a Micro-Curriculum for Critical AI Literacy in a PhD-Level Academic Writing Course

Other conference contribution, 2024

Rapidly advancing and easily accessible AI language tools, especially the large language models (LLMs) like ChatGPT, are reshaping academic writing landscape. Despite the growing prevalence of AI-assisted writing, the education of academic writing seems to be lagging. This gap leaves students without systematic support and guidance on harnessing the full potential of AI to enhance their academic writing. Current research focuses mainly on the
ability to detect AI use in academic writing (e.g., Casal and Kessler, 2023). Emerging studies do explore creative, professional, and ethical use of AI in classrooms (e.g., Vee et al., 2023). Our pilot study adds to this growing body of works. The aim of the study is to design and improve the teaching of a micro-curriculum for critical AI literacy in the context of a PhD-level academic writing course. Our working definition of “Critical AI Literacy” is a specialized skill set that is essential for academic writers to engage with AI tools effectively and ethically. The curriculum design is grounded in self-regulated learning theory (Zimmerman, 2002), which underscores how learning can be enhanced by scaffolding students’ goal setting, strategic planning, monitoring, and evaluation. Thus, in our microcurriculum, the teaching/learning activities guide students to self-direct their learning. Students complete personalised AI-assisted writing task and evaluate their writing processes, focusing on the effectiveness of prompts and the ethical aspects of using AI-generated text. These activities are embedded in a flipped-classroom design (Bishop & Verleger, 2013): pre-class learning (self-paced study of learning materials), in-class activities, and a post-class assignment.

The data have been collected from 70 PhD students from diverse disciplinary backgrounds enrolled in an introductory-level writing for publication course. We collected various forms of textual data before, during, and after the pedagogical intervention, which encompassed students' experiences of utilizing AI tools for writing in their initial self-assessment assignments, their documentation of AI-assisted writing processes during in-class activities, and their reflections on these activities within their final assignments. Furthermore, we obtained anonymous student feedback from course evaluations to assess their perceptions of the new micro-curriculum's effectiveness in enhancing critical AI literacy. We will discuss findings related to (i) PhD students’ use of AI prompts for self-regulation of academic writing, (ii) student metacognitive awareness of effective and ethical use of AI in academic writing, and (iii) student evaluation of self-regulated learning experiences of critical AI literacy. The findings will map students’ needs for developing critical AI literacy for academic writing and in turn inform an improved pedagogical approach. This study explores an effective way of teaching critical AI literacy—an essential skill set for academic writers as they navigate academic communication in an increasingly digitalized world.

References
Bishop, J., & Verleger, M. A. (2013). The flipped classroom: A survey of the research. 2013
ASEE Annual Conference & Exposition.
Casal, J.E. & Kessler, M. (2023). Can linguists distinguish between ChatGPT/AI and human
writing?: A study of research ethics and academic publishing. Research Methods in Applied
Linguistics, 2(3), 100068.
Vee, A., Laquintano, T., & Schnitzler, C. (Eds.) (2023). TextGenEd: Teaching with Text
Generation Technologies. The WAC Clearinghouse.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into
practice, 41(2), 64-70.