Resolution on Human Readers for Student Writing

June 2026

Student writers at all levels have the right to have their work read, responded to, and evaluated by human experts in the field in which they are working. Further, submitting the work of student writers to AI grading platforms, which typically use that work for training, violates their intellectual property rights. No written work by any SUNY student should be graded by a machine. We take this to be part of the fundamental promise of higher education: authentic response from a qualified human mentor, not automated response from an algorithm.

Background

When generative AI platforms began emerging in late 2022, there was no shortage of anxiety about the potential for student writers at all levels submitting machine-generated text in coursework.  Less than two weeks after OpenAI unveiled ChatGPT, Atlantic Monthly had already pronounced “The End of High School English” and declared that “The College Essay is Dead.”  Students, many assumed, would behave in unethical ways, happy to submit ChatGPT-generated text as their own work, and many college and university campuses indeed rushed to develop academic integrity policies focused on student misuse of AI. 

Much less attention, however, has been given to the potential for misuse of AI by faculty members and institutions.  Just as generative AI has made ghost authorship possible for students, a growing array of online platforms has emerged to read and assess their work, effectively making ghost readership possible for instructors.  Some course management systems even appear ready to offer AI-driven support for various assessment functions.  Appealing to the false promise of technology as a pedagogical cure-all, which has a long and growing history in higher education, these platforms promise to make reading, grading, and responding to student work effortless and effective – to have taken the messy, time-consuming burden of paying close attention to student writing off the shoulders of instructors.  

It’s not hard to imagine that this prospect might hold some appeal for fiscally minded institutional leaders – and even some overworked faculty members – especially as corporate marketing for AI seeps into public perception and its use becomes increasingly normalized in the larger culture.  

But training by algorithm is not education, and what the developers of AI technology promise does not approach authentic teacherly response, which we believe can only come from a qualified, thoughtful human reader.  

As Anne Herrington and Charles Moran pointed out in 2001, long before the advent of generative AI, adopting machine grading fundamentally redefines the nature of writing, which the authors say necessarily involves “a rhetorical interaction between writers and readers” (481).  Text generated either by or for a machine, they suggest, is not “writing.”  Marilyn Cooper’s work on rhetorical agency, which she says emerges “from the processes of living in the world” (421), insists similarly that “Individual agency is necessary for the possibility of rhetoric” (420):  reading and writing are by nature acts of social exchange between “embodied individuals” (421), who “create meanings through acting into the world and changing their structure in response to the perceived consequences of their actions” (Cooper, 2011, 420).  Haswell and Wilson’s famous Human Readers Petition (2013) echoed this sentiment a decade later, garnering broad support in Writing Studies.  A continuing thread of scholarship in the field concurs, including experiments with e-rater technology, which Les Perelman found in 2020 “reward[ed] lexically complex, but nonsensical essays,” worries over AI’s reinforcement and extension of racist and classist language bias, which Carmen Kynard says results in “white standardized essays,” and critiques of AI’s capacity to “perpetuate societal inequities and discriminatory practices despite appearing objective” (Warr and Heath).  

Any experienced teacher working with student writers, further, implicitly understands the role of fundamentally human skills in responding to student work and establishing a meaningful exchange with students.  They know that this work requires supportive dialogue, personal trust, and an assessment not only of students’ texts but also of their complex ego needs, habits of mind, and intellectual limits – their ability to hear and understand feedback. Canned evaluative responses, even when programmed to deliver encouragement and positivity, cannot foster student agency or establish the human relationships that developing writers need.  Mentorship cannot be outsourced or offloaded to an algorithm.

To offer machine-generated responses to student writing, then, would be a betrayal of both the notion of writing as an exchange between human minds and the larger mission of higher education – as students themselves have already begun to tell researchers.  Indeed, it is not difficult to imagine the prospect of an entirely closed digital loop, a sort of AI shell game in which students might submit machine-generated papers that in turn receive machine-generated grades and responses – schooling entirely without the messiness of thinking or learning.  This prospect – school without learning – is precisely what federal provisions for “regular and substantive interaction” between students and instructors in online courses were put in place in 2005 to prevent.  These standards do not apply to face-to-face instructions, but only because no one then imagined such regulation could be necessary outside online learning platforms.  

The absence of regulation notwithstanding, however, higher education would do well to ask itself why students would bother with college at all if they could simply open an AI platform to assess and certify their work.  Students doing honest intellectual work deserve the thoughtful responses of their instructors.

We recognize a host of other reasons, too, to worry over the advent of generative AI and its potential effects on both higher education and the practice of writing.  First and foremost among them are the violation of students’ privacy and intellectual property rights. This occurs whenever students’ work is submitted to a chatbot without their explicit permission. But we also worry more broadly about generative AI’s potential for displacing academic labor, its consumption of energy and its effects on the natural world, and the larger role it stands to play in the increasing polarization of wealth and power globally.

Resolution

As such, the SUNY Council on Writing resolves that student-writers at all levels have a right to have their work read, evaluated, and responded to by human experts in the field in which they are working.  No written work by any SUNY student should be machine-graded or machine-annotated.  We take this to be part of the fundamental promise of higher education: authentic response from a qualified human mentor, not automated response based on an algorithm.  The Council believes that this should apply not only to required first-year writing courses, but to all written work submitted by students across the disciplines and at all levels. 

Works Cited

Cooper, M. M. (2011). “Rhetorical Agency as Emergent and Enacted. College Composition and Communication, 62(3), 420-449.

D’Agostino, Susan. “Clarity, Confusion on ‘Regular and Substantive Interaction’.” Inside Higher Education. November 16, 2022. Accessed May 5, 2025.  https://www.insidehighered.com/news/tech-innovation/teaching-learning/2022/11/16/regular-and-substantive-interaction-online

Digital Education Council.  “What Students Want: Key Results from DEC Global AI Student Survey 2024.”  August 7, 2024.  Accessed May 5, 2025. https://www.digitaleducationcouncil.com/post/what-students-want-key-results-from-dec-global-ai-student-survey-2024

Herman, Daniel.  “The End of High-School English.” Atlantic Monthly. December 9, 2022.  Accessed: May 5, 2025. https://www.theatlantic.com/technology/archive/2022/12/openai-chatgpt-writing-high-school-english-essay/672412/

Herrington, A., & Moran, C. (2001). “What Happens When Machines Read Our Students’ Writing?” College English 63(4), 480-499.

Kelly, Samantha Murphy. “Teachers are using AI to grade essays. But some experts are raising ethical concerns.” CNN.  April 6, 2024. Accessed May 5, 2025. https://www.cnn.com/2024/04/06/tech/teachers-grading-ai/index.html

Kynard, Carmen.  “When Robots Come Home to Roost: The Differing Fates of Black Language, Hyper-Standardization, and White Robotic School Writing (Yes, ChatGPT and His AI Cousins).” Education, Liberation & Black Radical Traditions for the 21st Century: Carmen Kynard’s Teaching & Research Site on Race, Writing, and the Classroom.  December 11, 2023. Accessed June 4, 2026. http://carmenkynard.org/when-robots-come-home-to-roost/ 

Marche, Stephen. “The College Essay Is Dead.” Atlantic Monthly. Accessed May 5, 2025.  https://www.theatlantic.com/technology/archive/2022/12/openai-chatgpt-writing-high-school-english-essay/672412/

NCTE Executive Committee. “Machine Scoring Fails the Test: Position Statement on Machine Scoring.”  April 20, 2013.   Accessed December 12, 2025.  https://ncte.org/statement/machine_scoring/

Perelman, Les. “The BABEL Generator and E-Rater: 21st Century Writing Constructs and Automated Essay Scoring (AES).” Journal of Writing Assessment 13(1). Accessed May 15, 2026. https://escholarship.org/uc/item/263565cq

Warr, Melissa and Marie K. Heath. “Uncovering the Hidden Curriculum in Generative AI: A Reflective Technology Audit for Teacher Educators.” Journal of Teacher Education 76(3), May 2025, 245-261. Accessed 6 June 2026. https://doi-org.proxy.geneseo.edu/10.1177/00224871251325073

Reviewed by membership and ratified by Executive Board, 6/23/26