Anticipating Writing’s Evolution: Insights from Writing Futures in the Age of Large Language Models

The 2021 publication Writing Futures: Collaborative, Algorithmic, Autonomous (Springer) by Ann Hill Duin and Isabel Pedersen offers a framework that not only anticipates the technological transformations of its time but presupposes the emergence and widespread adoption of large language models (LLMs), such as OpenAI’s GPT-series. This prescience underscores the book’s enduring academic and practical significance in understanding the evolving landscape of writing technologies.

Large language models are advanced artificial intelligence systems designed to process and generate natural language. These models are built on deep neural network architectures and are trained on massive corpora of text to learn patterns of language use, enabling them to produce coherent responses. LLMs have demonstrated remarkable capabilities in tasks ranging from automated content generation to complex conversational interactions, raising significant questions about authorship, creativity, and the future of writing (Brown et al., 2020).

Writing Futures as a Framework for Technological Emergence

Duin and Pedersen address the perennial challenge of professional and technical communication (PTC) scholars and practitioners: the lag between technological emergence and the critical frameworks required to engage with these changes. They propose the “Writing Futures Framework,” an interdisciplinary model that integrates collaborative, algorithmic, and autonomous paradigms into writing pedagogy and practice. By incorporating insights from computational intelligence, human-computer interaction, and rhetorical studies, the framework underscores the necessity of preemptively engaging with emergent technologies.

Their integration with the Fabric of Digital Life—a repository chronicling socio-technical trajectories (Iliadis and Pedersen 2018) —positions this book as both a theoretical and methodological touchstone. Through this resource, readers are invited to explore the implications of embodied, wearable, and autonomous technologies within dynamic social and cultural contexts.

Anticipating Large Language Models

Although written before the proliferation of LLMs like GPT-3, the text astutely engages with precursors such as earlier machine learning systems and speculative technologies that automate cognitive and creative tasks. The authors discuss the shift from writing as a purely human endeavor to one increasingly mediated by intelligent systems capable of not just assistance but collaboration. In doing so, the text envisions scenarios where algorithms become co-creators, challenging traditional notions of authorship and intellectual labor.

The chapter introduces autonomous agents and intelligent systems, highlighting their transformative potential in areas such as ambient intelligence, machine learning, and natural language processing. These discussions effectively foreshadow the operational and ethical complexities introduced by LLMs, which epitomize the very “algorithmic writing futures” the authors describe.

Writing as a Socio-Technical Assemblage

The book’s call to abandon “nostalgic notions of solo proprietary authorship” resonates profoundly in the age of LLMs. These models, trained on vast datasets and capable of generating coherent and contextually nuanced text, embody the collaborative potential Duin and Pedersen advocate. Their emphasis on dialogic collaboration with nonhuman agents challenges us to rethink writing not merely as a skill but as a socio-technical assemblage—a confluence of human creativity and machine intelligence.

The authors’ inclusion of civic and ethical dimensions further amplifies the book’s relevance. They interrogate the implications of machine autonomy in decision-making and emphasize the necessity of cultivating digital and AI literacies. These literacies, they argue, are critical not only for professional efficacy but also for civic engagement in addressing global challenges.

Writing Futures positions itself as a seminal framework for envisioning and advancing future-oriented approaches to writing studies. Its anticipatory framework offers tools for navigating the convergence of human and machine intelligence, a reality brought into sharp focus by the capabilities of LLMs. By connecting historical trajectories with speculative futures.

References

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … & Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877-1901.

Duin, A. H., & Pedersen, I. (2021). Writing Futures: Collaborative, Algorithmic, Autonomous. Springer Nature.

Gero, K. I., Liu, V., & Chilton, L. B. (2021). Sparks: Inspiration for Science Writing using Language Models. arXiv preprint arXiv:2110.07640.

Iliadis, A. and Pedersen, I. (2018) The fabric of digital life: Uncovering sociotechnical tradeoffs in embodied computing through metadata. Journal of Information, Communication and Ethics in Society 16(3): 1–18.