A Posthumanist Approach to AI Literacy

Synthesis: Wang and Wang (2025) argue for a posthumanist reframing of AI literacy, moving beyond the humanistic view of AI as a discrete "tool" used by autonomous human agents toward understanding AI literacy as an understanding of how meaning emerges through the entanglement of human and AI agencies. Through a case study of two multilingual undergraduate students (Zhimo and Asuka) in US writing courses, they document a productive tension between students' experiments with posthumanist literacy and their entrenched humanistic assumptions, showing how posthumanism offers a relational approach to cultivating AI literacy in language and literacy education.

Key Findings

The study contrasts two distinct approaches: Zhimo embodies a critical-pragmatic humanistic AI literacy, conceptualizing ChatGPT as a sophisticated "tool" (调教 / "tiao jiao", to train and manipulate) that he directs, while Asuka adopts an anthropomorphic posthumanist stance, treating AI as an agentic collaborator whose authorial agency is entangled with her own.

A central tension: Zhimo consistently asserts his human authorial agency while his writing process is nonetheless deeply entangled with AI — a posthumanist reading reveals that his "fix my language but not my meaning" approach still co-produces meaning through human-AI intra-actions.

Posthumanism de-centers humans as the sole autonomous meaning-making agents; agency is "enacted" through intra-actions between bodies, ideas, materials, language, technologies, and space (drawing on Barad, Latour, Bennett, Deleuze & Guattari's assemblage and rhizomaticity).

The framework repudiates both extremes: uncritical anthropomorphization of AI (which encourages overreliance and a transactional view of literacy) and dismissing AI as a mere tool (which leads to punitive measures that undermine students' exploratory meaning-making).

The article proposes that a posthumanist approach to AI literacy is essentially relational rather than transactional work, questioning celebratory corporate anthropomorphism and the anthropocentric discourse of human subjects leveraging AI "tools."

Study Design & Method

The study is a qualitative case study of two multilingual undergraduate students — Zhimo (a first-year student from China in an academic writing course) and Asuka (a Japanese student in an elective writing-intensive course on generative AI and writing) — at a private US college. Data were collected in Spring 2024 through 1.5–2 hour semi-structured interviews, students' AI-assisted writing artifacts, and guided reflections. A thematic analysis examined the students' AI-mediated literacy practices through both Wang and Wang's (2025) critical AI literacy model (awareness, positionality, human-AI interactions, evaluation of AI affordances) and posthumanist theory, triangulating data sources to understand how the students conceptualized, positioned themselves with, intra-acted with, and evaluated AI.

Implications for AI in Education

The article reframes AI Literacy in Writing Education away from tool-competence toward relational understanding of human-AI entanglement. For educators, it suggests three practical applications: (1) encouraging students to critically interrogate AI-generated texts as co-constructed, contingent artifacts rather than static outputs; (2) recognizing AI literacy extends beyond textual engagement to multimodal, algorithmic, and linguistic dimensions of meaning-making; and (3) creating learning environments that allow both engagement with and ethical refusal of AI. The case studies show how distinct cultural-linguistic backgrounds and rhetorical objectives shape students' approaches to AI, and how posthumanism complements (rather than replaces) humanistic perspectives by deconstructing and decentering them. It connects to Student Experience, agency gap, and debates about Academic Integrity and authorship in the age of Generative AI.

Limitations

The study is a small qualitative case study of two students in one institutional context, bounding generalizability. The posthumanist theoretical framing is interpretive and does not offer measurable learning outcomes. The authors acknowledge a posthumanist approach is not an all-encompassing framework and must be situated within broader unresolved debates about AI's societal and ethical implications. The case-study design emphasizes depth of understanding over breadth.

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Citation

Wang, Z., & Wang, C. (2025). A posthumanist approach to AI literacy.