Review of AI and Writing

[1] In AI and Writing, Sidney I. Dobrin provides readers with an accessible and easy-to-navigate overview of multiple aspects of artificial intelligence (AI) and its impacts on writing and learning. In the introduction, Dobrin explains that the book “focuses on transferable skills that can assist student writers in many contexts and on many platforms” (p. x). To follow through with this claim, each of the 10 chapters wraps up with an End of Chapters Materials section in which Dobrin offers questions, applications, and activities in these categories:

• So What?—why we should care about AI;
• Conceptual AI--what if scenarios;
• Applied AI--how we might consider using AI;
• For Discussion—larger discussion questions.

[2] Combined with the student-friendly tone of the book, these end-of-chapter sections make this book a nice tool for facilitating beginner conversations with students, teachers, or colleagues on AI and its many potential uses.

[3] The first chapter focuses on the changing landscape of AI and its impact on communities. The following chapters address the history of Generative AI (GenAI), academic integrity, writing with GenAI, prompt engineering, creating visuals with AI, assessing context of AI use, teaching career readiness in the age of AI, AI bias, and the environmental impacts of using AI.

[4] Going into this book, I would have expected to get a history of AI, how we might consider using it in writing, and the ethical considerations we need to take into account when using it. This book begins to address most of these expectations and starts with an overview of AI and traces a history of other tools and technologies that have made potentially similar impacts as AI presumably will (e.g. the printing press, ballpoint pens, Wikipedia, even writing itself). In addition to culturally contextualizing the public and academic responses to AI through this historical lens, Dobrin works to address common concerns of using AI and participating in “Human-Machine Collaboration” (p. 8). He invites readers to think about AI as a collaborative meaning-maker and offers different perspectives for considering AI in terms of academic integrity (pp. 31-38), pointing out the lack of transparency in AI outputs and the problem of having AI generate a class assignment which compromises the intention of the learning task (p. 38). While acknowledging that AI can simplify research tasks, Dobrin cautions that vetting the value and credibility of GenAI-created research outputs is the responsibility of the user (p. 50).

[5] When discussing AI as part of a Human-Machine Collaboration, Dobrin emphasizes the collaborative nature of writing professionally and academically, explaining that “[n]ot all writing is done individually. In many contexts, we collaborate with others to create a single writing project” (p. 8). He provides examples of how teachers may work with students to scaffold what they offer GenAI in order to produce the outcome that they intend for an assignment, offering thinking about writing process as one way to use AI to enhance learning.

[6] Dobrin introduces readers to “hallucinations”: “outputs that are false despite appearing correct,” warning that GenAIs can generate “false quotes, fake sources, and fabricated data (p. 25). He also cautions that users can inadvertently generate incorrect outputs due to faulty prompts because “GenAI programs cannot currently discern between correct and incorrect information. So, if you use inaccurate information as part of your prompt, GenAI will assume your prompt’s claims to be true, likely returning an output that itself contains false information” (p. 67). Along with this suggestion, he directs readers to avoid writing prompts that could result in “offensive content, intentionally misleading content, and content that might lead to harm” (p. 67).

[7] Dobrin nods to issues of bias toward the beginning of this book writing, “GenAI platforms are limited in their abilities. The information they supply can be inaccurate or biased” (p.10). I was struck while reading this text by the limited discussion of ethics and bias. Dobrin offers two short chapters at the end of the book that briefly address bias, ethics, and environmental impact rather than weaving discussion of potential ethical or bias issues into the arguments about using AI with writing. As someone who has worked closely with college-age students and writing center consultants talking about and researching AI, I’ve found deeper engagement with ethical concerns and the potential biases of AI really integral to engaging with these emerging technologies in practice. Knowing the history of work happening in ethics, digital rhetorics, and writing studies addressing issues of racism and sexism in technology, I would have appreciated more engagement with technology bias. Given the text's easy-to-use nature, it offers a good entry-point for novice and/or nervous AI users; thus, the missing, substantive discussion of ethics and bias seems a noticeable omission.

[8] Overall, AI and Writing offers an accessible overview of AI, many questions that emerge when using it, and discussion points for potential professional development. Dobrin explores numerous perspectives on integrating AI, highlighting its role as both an assistive tool for writing and a skill students should acquire for future careers. Courses in writing and other disciplines could easily be structured around Dobrin’s book. Furthermore, this book would be a good resource for writing center tutors faced with answering client questions about using AI to do assigned learning tasks.

[9] This book asks us to reconsider how we think about AI and writing and opens the door to constructive discussion surrounding the many different contexts and realities of AI use in and out of academia.