This guide by Jennifer Sano-Franchini and colleagues addresses the intentional refusal of Generative AI (GenAI) technologies in writing studies, emphasizing a principled response rather than a fearful rejection. It outlines ten premises that inform refusal, including the understanding of language and power dynamics, rejecting linguistic homogenization, and addressing ethical considerations around plagiarism and labor exploitation linked to GenAI. The guide critiques the economic and environmental impacts of GenAI, advocating for a critical stance towards these technologies until their use aligns better with educational values. The authors argue that refusal can serve as a pragmatic response to the proliferation of GenAI in writing education.
| name | description | change | 10-year | driving-force | relevancy |
|---|---|---|---|---|---|
| Emergence of AI Refusal Culture | Growing recognition among educators about refusing GenAI technology in the classroom. | Shift from acceptance to refusal of GenAI technologies in higher education. | In 10 years, education may emphasize human-centered learning over automated systems like GenAI. | A collective push against technological adoption without ethical consideration. | 4 |
| Recognition of Linguistic Diversity | The writing studies community is increasingly contesting linguistic homogenization pushed by GenAI. | Change from linguistic standardization to valuing diverse dialects and writing styles. | Expect education systems to more actively promote and preserve linguistic diversity. | A cultural movement towards inclusion and respect for varied linguistic identities. | 5 |
| Environmental Awareness in Technology Usage | Concerns regarding the environmental impact of GenAI technologies are rising. | Shift from unchecked technology adoption to critical evaluation of environmental costs. | In a decade, educational institutions may prioritize sustainable technology practices. | Growing advocacy for environmental justice and sustainable technology practices. | 4 |
| Labor Issues in Academia | Increased awareness of labor exploitation related to GenAI in educational systems. | Recognition of labor exploitation in the adoption of AI technologies by academia. | Higher education landscape may evolve to prioritize fair labor practices over automation. | Commitment to social justice and equity in academic labor conditions. | 5 |
| Critical Engagement with Technology | Writing studies scholars are emphasizing critical assessments of AI technologies. | Moving from passive adoption to critical engagement with technology’s implications. | Education will likely involve more critical literacy regarding technology’s societal impacts. | Desire for informed, ethical integration of technology in academic settings. | 4 |
| Demand for Ethical AI Practices | Calls for changes in AI economic models and ethical considerations are evident. | Transition from exploitative models to ethical and equitable practices in technology use. | AI technologies will be heavily regulated and aligned with ethical educational values. | Societal demand for accountability and ethical use of technology in education. | 5 |
| name | description |
|---|---|
| Technological Homogenization | The risk of linguistic homogenization as GenAI technologies may standardize language use and suppress diverse linguistic expressions. |
| Labor Exploitation | The potential exacerbation of labor exploitation in academic settings due to the adoption of GenAI, particularly affecting non-tenure-track faculty and graduate instructors. |
| Environmental Impact | The significant environmental costs associated with the infrastructure and energy consumption of GenAI technologies, including water and energy usage. |
| Ethical Implications of AI Adoption | The ethical challenges presented by GenAI technologies, especially regarding the exploitation of content creators and the impacts on academic integrity. |
| Discursive Manipulation | The potential for GenAI technologies to mislead users through biased marketing and metaphors that conceal their limitations and ideologies. |
| Surveillance and Mistrust | The risk of increased surveillance in academic environments that may lead to mistrust and alienation among students regarding educational integrity. |
| Ideological Bias in Technology | The inherent biases in GenAI technologies that reflect corporate values and cultural inequities, influencing the educational content and practices. |
| Market Pressures on Academic Integrity | Pressure from corporate investments to adopt GenAI technologies that may misalign with educational goals and outcomes. |
| name | description |
|---|---|
| Refusal to Embrace GenAI | Deliberate choice by educators to avoid or limit the use of Generative AI technologies in writing education based on ethical considerations. |
| Critical Engagement with Technology | Encouragement for educators to critically analyze the metaphors and marketing narratives surrounding GenAI, understanding their implications and biases. |
| Focus on Human-Centric Writing Education | Emphasis on the importance of writing as a human activity that encourages critical thinking, relationships, and self-expression, rather than mere transcription. |
| Advocacy for Linguistic Diversity | A stance against linguistic homogenization promoted by GenAI, reinforcing the need to respect and uphold diverse language varieties. |
| Ethical Consideration of Technology’s Impact | Awareness of the environmental and social implications of GenAI technologies, pushing for discourse that includes ecological concerns. |
| Rejecting Surveillance in Education | Opposition to the implementation of punitive plagiarism surveillance technologies, advocating for trust and understanding instead. |
| Labor Concerns in Tech Adoption | Addressing the potential exploitation and precarious conditions faced by educators with the integration of GenAI technologies in writing instruction. |
| Demand for Transparency and Accountability | Calling for clear benefits and ethical labor practices related to GenAI technology, alongside transparency in its use in educational settings. |
| Holistic Understanding of Digital Contexts | Understanding historical and ideological backgrounds of writing technologies to inform practices and policy regarding GenAI. |
| Pragmatic Technology Adoption | Approaching GenAI use in education cautiously and thoughtfully, supporting usage only when aligned with educational values and goals. |
| name | description |
|---|---|
| Generative AI (GenAI) | Technologies that generate human-like text, images, or other content using algorithms and vast datasets. |
| Large Language Models (LLMs) | AI systems designed to understand and generate human language, capable of context-aware responses. |
| Chatbots | AI applications that simulate human conversation to assist users in various contexts. |
| Neural Networks | Computational models inspired by the human brain, used in AI for pattern recognition and prediction. |
| Deep Learning | A subset of machine learning using multi-layered neural networks for complex data processing and analysis. |
| AI-driven Content Generation | Tools that automate the creation of written or visual content for various applications. |
| Plagiarism Detection Technologies | Software designed to identify instances of plagiarism in academic work, increasingly relevant with GenAI adoption. |
| name | description |
|---|---|
| Generative AI Refusal | The stance of refusing the use of Generative AI in writing studies as a principled and informed response to its impacts. |
| Linguistic Homogenization | Concerns over how Generative AI technologies can accelerate linguistic homogenization and affect diversity in language use. |
| Labor Exploitation in AI | The potential for GenAI adoption to exacerbate precarious labor conditions for writing instructors and industry workers. |
| Environmental Impacts of AI | The significant resource consumption required for AI technologies and their environmental consequences. |
| Academic Integrity Surveillance | The detrimental effects of surveillance and detection methods on academic integrity in the context of AI use. |
| Critical Discourse on GenAI | The need for critical examination of the economic models and marketing strategies behind Generative AI promotion. |
| Metaphors for AI Technologies | The limitations of common metaphors used to describe Generative AI, influencing perceptions of its capabilities. |
| Ethics of AI Adoption | The ethical considerations surrounding the adoption of Generative AI in educational contexts, requiring alignment with educational values. |
| Cultural Co-optation by AI | The appropriation of diversity and inclusion discourses to justify the use of GenAI amidst its documented harms. |
| Resistance to Academic Plagiarism Surveillance | A shift away from punitive measures against plagiarism to a more trust-based educational approach in the context of GenAI. |