“It’s giving AI”: Reading ambiguously-authored texts and the role of felt sense

Authors

  • Elizabeth Velasquez Ohio State University | US
  • Christa Teston Ohio State University | US

DOI:

https://doi.org/10.17239/jowr-2026.17.03.08

Keywords:

tacit knowledge, meaning-making, RX Lab, evidence, grounded theory

Abstract

To understand how human readers navigate a literate landscape that newly includes AI-generated prose, we asked participants (n=76) to read and make decisions about who and/or what is responsible for writing anonymized, “ambiguously-authored” texts. Findings suggest that readers’ assumptions about who and/or what wrote a text are rooted in “felt sense.” Prompting participants to make their “felt sense” explicit allowed us to catalog the evidential warrants participants relied on when making authorship decisions. Enabled by a modified grounded theory approach to analysis, we constructed two main themes. First, readers are “triggered” by certain textual cues that, when combined with prior experiences and knowledge, evidentially warrant assumptions about who and/or what wrote a text. Second, after recognizing the consequences of making one’s felt sense explicit, some readers experience what we call an “axiological crisis.” Axiological crises emerge when participants meta-cognitively hear or see themselves attributing certain characteristics and values to an AI text-generator or human author. We conclude by reimagining the axiological crisis as an opportunity for improving metacognitive awareness about how felt sense affects our reading practices.

References

Adelani, D. I., Mai, H., Fang, F., Nguyen, H. H., Yamagishi, J., & Echizen, I. (2020). Generating sentiment-preserving fake online reviews using neural language models and their human-and machine-based detection. In Advanced information networking and applications: Proceedings of the 34th international conference on advanced information networking and applications (AINA-2020) (pp. 1341-1354). Springer International Publishing. https://doi.org/10.1007/978-3-030-44041-1_114

Adler-Kassner, L., Majewski, J., & Koshnick, D. (2012). The Value of Troublesome Knowledge: Transfer and Threshold Concepts in Writing and History. Composition Forum (Vol. 26). Association of Teachers of Advanced Composition.

Adler-Kassner, L., Clark, I., Robertson, L., Taczak, K., & Yancey, K. B. (2016). Assembling knowledge: The role of threshold concepts in facilitating transfer. Critical transitions: Writing and the question of transfer, 17-47. https://doi.org/10.37514/PER-B.2016.0797.2.01

Amidon, T. R. (2022, June). Troubling the Tacit: A Review Essay of Harry Collins’s (2010) Tacit and Explicit Knowledge. In Composition Forum (Vol. 49).

Amirjalili, F., Neysani, M., & Nikbakht, A. (2024). Exploring the boundaries of authorship: A comparative analysis of AI-generated text and human academic writing in English literature. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1347421

Anson, C. M., & Schwegler, R.A. (2012). Tracking the Mind’s Eye: A New Technology for Researching Twenty-First-Century Writing and Reading Processes. College Composition and Communication 64(1), 151-171. https://doi.org/10.58680/ccc201220864

Bedington, A., Halcomb, E., McKee, H. A., Sargent, T., & Smith, A. (2024). Writing with generative AI and human–machine teaming: Insights and recommendations from faculty and students. Computers and Composition, 71. https://doi.org/10.1016/j.compcom.2024.102827

BBC. 13 TikTok Phrases You Need to Know. https://www.bbc.co.uk/bitesize/articles/zrft9ty#zxj9p9q

Burriss, S. K., Smith, B. E., Shimizu, A. Y., Hundley, M., Pendergrass, E., & Molvig, O. (2025). Exploring the ethics of multimodal composition with AI: Student and educator perspectives on evaluating and using generative models in the classroom. Contemporary Issues in Technology and Teacher Education, 25(2).

Carillo, E. C. (2015). Securing a place for reading in composition: The importance of teaching for transfer. University Press of Colorado. https://doi.org/10.7330/9780874219609

Clayson, A. (2018). Distributed cognition and embodiment in text planning: A situated study of collaborative writing in the workplace. Written Communication, 35(2), 155-181. https://doi.org/10.1177/0741088317753348

Collins, H. (2019). Tacit and explicit knowledge. University of Chicago press.

Crozier, M., & Workman, E. (2022). Discourse-based interviews in institutional ethnography: Uncovering the tacit knowledge of peer tutors in the writing center. In Composition Forum (Vol. 49). Association of Teachers of Advanced Composition.

Cummings, R. E., Monroe, S. M., & Watkins, M. (2024). Generative AI in first-year writing: An early analysis of affordances, limitations, and a framework for the future. Computers and Composition, 71. https://doi.org/10.1016/j.compcom.2024.102827

Dugan, L., Ippolito, D., Kirubarajan, A., & Callison-Burch, C. (2020). RoFT: A tool for evaluating human detection of machine-generated text. https://doi.org/10.18653/v1/2020.emnlp-demos.25

Evia, C., & Patriarca, A. (2012). Beyond compliance participatory translation of safety communication for Latino construction workers. Journal of Business and Technical Communication, 26(3), 340–367. https://doi.org/10.1177/1050651912439697

Fleckenstein, K. S. (2003). Embodied literacies: Imageword and a poetics of teaching. SIU press.

Glaser, B., & Strauss, A. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Sociology Press. https://doi.org/10.1097/00006199-196807000-00014

Haas, C., & Flower, L. (1988). Rhetorical Reading Strategies and the Construction of Meaning. College Composition and Communication, 39(2), 167-183. https://doi.org/10.1177/105065190101500402

Haas, C., & Witte, S. P. (2001). Writing as an embodied practice: The case of engineering standards. Journal of Business and Technical Communication, 15(4), 413-457.

Haswell, R. H., Briggs, T. L., Fay, J. A., Gillen, N. K., Harrill, R., Shupala, A. M., & Trevino, S. S. (1999). Context and rhetorical reading strategies: Haas and Flower (1988) revisited. Written Communication, 16(1), 3-27. https://doi.org/10.1177/0741088399016001001

Hayles, N.K. (1999). How We Became Posthuman. Virtual Bodies in Cybernetics, Literature, and Informatics. The University of Chicago Press. https://doi.org/10.7208/chicago/9780226321394.001.0001

Herring, K. D. (2024). It’s giving queer rhetorical pedagogy: Introducing rhetorical criticism with queer vernacular. Communication Teacher, 38(3), 260-267. https://doi.org/10.1080/17404622.2024.2342803

Hutton, L., & King, C. M. (2024). A Commonplace Problem: Uncovering Composition’s Tacit Axiologies of Reading. College Composition & Communication, 76(1), 90-119. https://doi.org/10.58680/ccc202476190

Keller, D. (2013). Chasing literacy: Reading and writing in an age of acceleration. University Press of Colorado. https://doi.org/10.7330/9780874219333

Kim, S., & Cho, S. (2017). How a tutor uses gesture for scaffolding: A case study on L2 tutee’s writing. Discourse Processes, 54(2), 105–123. https://doi.org/10.1080/0163853X.2015.1100909

Knowles, A. M. (2024). Machine‑in‑the‑loop writing: Optimizing the rhetorical load. Computers and Composition, 71. https://doi.org/10.1016/j.compcom.2024.102826

Laquintano, T. P., Schnitzler, C., & Vee, A. (Eds.). (2023). “An Introduction to Teaching with Text Generation Technologies” In TextGenEd: Teaching with Text Generation Technologies. WAC Clearinghouse.

Lee, M., Liang, P., & Yang, Q. (2022, April). Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities. In Proceedings of the 2022 CHI conference on human factors in computing systems (pp. 1-19). https://doi.org/10.1145/3491102.3502030

Liang, W., Izzo, Z., Zhang, Y., Lepp, H., Cao, H., Zhao, X., ... & Zou, J. Y. (2024). “Monitoring ai-modified content at scale: A case study on the impact of chatgpt on ai conference peer reviews.” Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. arXiv:2403.07183.

Liu, J. Q. J., Hui, K. T. K., Al Zoubi, F., Zhou, Z. Z. X., Samartzis, D., Yu, C. C. H., Chang, J. R., & Wong, A. Y. L. (2024). The great detectives: Humans versus AI detectors in catching large language model-generated medical writing. International Journal for Educational Integrity, 20(1). https://doi.org/10.1007/s40979-024-00155-6

McCormick, K. (1989). Expanding the Repertoire: An Anthology of Practical Approaches for the Teaching of Writing (Reading-to-Write Report No. 11). Technical Report No. 30.

Odell, L., Goswami, D., & Herrington, A. (1983). The discourse-based interview: A procedure for exploring the tacit knowledge of writers in nonacademic settings. In P. Mosenthal, L. Tamor, & S. A. Walmsley (Eds.), Research on writing: Principles and methods (pp. 221–236). Longman.

Olinger, A. R. (2014). On the instability of disciplinary style: Common and conflicting metaphors and practices in text, talk, and gesture. Research in the Teaching of English, 48(4), 453-478. https://doi.org/10.58680/rte201425162

Pandey, H. L., Bhusal, P. C., & Niraula, S. (2024). Large language models and digital multimodal composition in the first‑year composition classrooms: An encroachment and/or enhancement dilemma. Computers and Composition, 75. https://doi.org/10.1016/j.compcom.2024.102892

Perl, S. (1979). The composing processes of unskilled college writers. Research in the Teaching of English, 13(4), 317-336. https://doi.org/10.58680/rte201117867

Perl, S. (1980). Understanding composing. College Composition & Communication, 31(4), 363-369. https://doi.org/10.58680/ccc198015928

Perl, S. (2004). Felt sense: Writing with the body. Portsmouth, NH: Boynton/Cook Heinemann.

Polanyi, M. (1958). Personal knowledge. Routledge.

Rice, J. (2015). Para-expertise, tacit knowledge, and writing problems. College English, 78(2), 117-138. https://doi.org/10.58680/ce201527547

Rivera Soto, R., Chen, B., & Andrews, N. (2025). Language models optimized to fool detectors still have a distinct style (and how to change it). arXiv. https://doi.org/10.48550/arXiv.2505.14608.

Rule, H. J. (2017). Sensing the sentence: An embodied simulation approach to rhetorical grammar. Composition Studies, 45(1), 19-38.

Saldaña, J. (2009). The coding manual for qualitative researchers. Sage.

Sauer, B. A. (2003). The rhetoric of risk: Technical documentation in hazardous environments. Routledge. https://doi.org/10.4324/9781410606815

Shapiro, S. (2014). “Words that you said got bigger”: English language learners’ lived experiences of deficit discourse. Research in the Teaching of English, 48(4), 386-406. https://doi.org/10.58680/rte201425159

Smith, B., Bouadjenek, M. R., Kheya, T. A., Dawson, P., & Aryal, S. (2025). A comprehensive analysis of large language model outputs: Similarity, diversity, and bias. arXiv. https://doi.org/10.48550/arXiv.2505.09056

Swales, J. M., & Feak, C. B. (2010). From text to task: Putting research on abstracts to work. In English for professional and academic purposes (pp. 169-182). Brill. https://doi.org/10.1163/9789042029569_012

Tang, R., Chuang, Y. N., & Hu, X. (2024). The science of detecting LLM-generated text. Communications of the ACM, 67(4), 50-59. https://doi.org/10.1145/3624725

Toulmin, S. E. (2003). The uses of argument. Cambridge University Press. https://doi.org/10.1017/CBO9780511840005

Turner, S. (2022). Polanyi and tacit knowledge. In The Routledge Handbook of Philosophy and Implicit Cognition (pp. 182-190). Routledge. https://doi.org/10.4324/9781003014584-17

Valiaiev, D. (2024). Detection of machine-generated text: Literature survey. arXiv preprint. arXiv:2402.01642.

Vee, T. Laquintano, & C. Schnitzler (2023), TextGenEd: Teaching with Text Generation Technologies. The WAC Clearinghouse. https://doi.org/10.37514/TWR-J.2023.1.1.02

Walters, W. H. (2023). The effectiveness of software designed to detect AI-generated writing: A comparison of 16 AI text detectors. Open Information Science, 7(1), 20220158. https://doi.org/10.1515/opis-2022-0158

Wolfe, J. (2005). Gesture and collaborative planning: A case study of a student writing group. Written Communication, 22(3), 298–332. https://doi.org/10.1177/0741088305278108

Published

2026-02-17

Issue

Section

Articles

How to Cite

Velasquez, E., & Teston, C. (2026). “It’s giving AI”: Reading ambiguously-authored texts and the role of felt sense. Journal of Writing Research, 17(3). https://doi.org/10.17239/jowr-2026.17.03.08

Similar Articles

1-10 of 109

You may also start an advanced similarity search for this article.