We think the field of rhetoric\/writing needs to address\u2014and quickly!\u2014three current and rapidly expanding developments in artificial intelligence (AI) technology:<\/p>\n
No sense denying these developments. \u201dHuman teachers can never be replaced!\u201d No, they can, it is already happening, this is already in the works. Yes, you, and we, the college writing teacher can be replaced. (Actually, in the first-year writing courses, we are already being replaced: by the Advanced Placement writing test and by dual enrollment composition courses.) So the issue on the table is, What do we do about it? What does this mean for our field? In this short piece our aim is mainly to highlight the issue and start the discussion we desperately need to have.<\/p>\n
First, it\u2019s helpful to know a bit about where AI is going. The term artificial intelligence<\/i> is applied to a lot of systems that aren\u2019t that intelligent\u2014yet. Siri, Alexa, and other personal assistants are advertised as \u201csmart assistants,\u201d but they are basically glorified search engines with natural language processing (NLP) and aural\/oral capabilities. They are able to listen, respond, read, and write in a way, but only within the constraints of pre-programmed data sets. What many personal assistants and customer service bots currently lack is what many newer AI systems are developing: the capability for deep learning and autonomous learning through neural networks.<\/p>\n
Neural networks makes it sound like AI is thinking like a human, but, of course, it\u2019s not\u2014at least not yet. It\u2019s still all numbers and algorithms. But as programmers build in layers of neural networks and code machines so the machines can lay down their own neural networks, AI is changing so that the machines are learning for themselves based on their interactions with the environment. Yes, they\u2019re still programmed at the front-end, but then they take their programming and run with it, sometimes in interesting ways (Libratus<\/a> winning at Texas Hold \u2018Em), but often in troublesome (Microsoft\u2019s Tay) and downright tragic (Uber\u2019s self-driving cars<\/a>) ways. The question of whether the machine is really<\/i> thinking or merely simulating<\/i> thinking\u2014the key question raised by John Searle in the Chinese Room Argument (1980)\u2014might well be beside the point because, however they are processing information, AI agents are acting<\/i> intelligently and making decisions in the world<\/p>\n Unlike in the United States, where AI development is literally killing people<\/a>, the European Union has recognized that the realm of AI and robotic technology development needs guidance and rules. In 2017 the EU passed the European Civil Law Rules on Robotics<\/i><\/a>, in which they define AI agents as interactive, autonomous robots that are able to adapt their behavior and actions to the environment. This statement is an important first step in laying down some guidelines for human-AI machine interaction.<\/p>\n As AI bots become more interactive, autonomous, and capable of self-processed adaptive learning, they will become better agents and communicators, better at such actions as speaking, reading, writing, and evaluating writing.<\/p>\n To what extent then can bots do our writing tasks for us, at a level of effectiveness and quality such that they could possibly pass a Turing Test for writing: that is, readers cannot tell the difference between the bot writing and the human writing the same task?<\/p>\n Well, that depends, of course, on the writing context, but bots have certainly passed the Turing Test informally for a wide-variety of contexts. For example, x.ai<\/a>\u2019s personal assistant scheduling bot, Amy\/Andrew Ingram, in the context of email about meetings, is very often mistaken as human. In fact, some email correspondents have even flirted with Amy Ingram and sent her flowers and chocolates<\/a>. Some poetry writing bots are already informally passing the Turing Test<\/a>. Of greater worry, though, is that millions of people in the United States during the 2016 presidential election were unaware that the fellow \u201ccitizens\u201d they were talking politics with in Twitter were actually bots (Bessie & Ferrara, 2016) deployed as part of troll farm attacks on US democracy<\/a>.<\/p>\n In the academic realm, bots are already writing student papers. For example, Dr. Assignment <\/a>\u00a0promotes on its website that \u201cA.I. technology will automatically write your assignment paper for you if you are too lazy or don’t know what to write.\u201d As shown perhaps by this marketing (aiming at students it seems who\u2019d be stoked with a C or D), the level of output isn\u2019t expected to be high quality (yet), but as these programs become more sophisticated and nuanced, bot produced papers will certainly get better.<\/p>\n William Hart-Davidson (2018) thinks that we are not so \u201cfar away from a time when almost nobody composes a first draft of anything … you\u2019ll take over at the revision stage\u201d (p. 252). Is it cheating to have a robot write your paper for you? Well, some composition teachers thought it unfair, originally, when spell and grammar checkers came online. We managed to get through that crisis, finally arriving at the position that spell checkers are aids<\/i>, but they are not 100% foolproof, and the human writer is still responsible for spelling. Will we treat draft writing bots the same way?<\/p>\n In relation to writing bots we need to be aware of AI-writing developments, be able to teach students how to write scripts for bots, how to write with bots, and, for those going into education, how to teach in a world with writing bots. We also need to be part of important conversations shaping the ethical use of writing bots, considering such questions as:<\/p>\n The field of rhetoric\/writing has much to offer these discussions, given our field\u2019s long history of considering writing in human contexts and in the realm of human-machine communication.<\/p>\n AI writing teachers might seem pretty far-fetched, but that too is developing faster than we may realize. Working with very broad brushstrokes, let\u2019s start by identifying the distinct functions of teaching: designing the course curriculum, delivering course material, interacting with students, and grading\/evaluating student work and performance.<\/p>\n Let\u2019s start with grading papers. Machine-scoring of student writing has been around a long time and numerous studies in or field have shown its limitations (Ericsson & Haswell, 2006). But many of those studies were conducted on older, less smart systems that, as Les Perelman demonstrated, could be easily gamed by nonsense long essays with long sentences and multisyllabic key words that would score higher than well-written short essays with shorter sentences (Herrington & Moran, 2001; Perelman, 2012). As the technology develops and computers can juggle more variables, AI raters will become better readers and evaluators of writing.<\/p>\n But can AI systems interact effectively with students? That\u2019s where the natural language processing comes in. Many students in computer science classes at Georgia Tech can\u2019t figure out which of their teaching assistants (for an online course in computer science) are human and which are computers. Jill Watson and family has them fooled. These computer teaching assistants aren\u2019t grading work (yet); they are just \u201cautomatically answering a variety of routine, frequently asked questions, and automatically replying to student introductions\u201d (Goel & Polepeddi; see also Eicher, Polepeddi, & Goel, 2018). Yet they are doing so in ways similar enough to human responses in the same context that they are, rather effectively it seems, replacing human teaching assistants.<\/p>\n As we all face increased pressures to raise class sizes, to teach more students with fewer faculty, to get \u201clean\u201d and \u201cefficient\u201d (buzzwords on our campus and we imagine on yours), it may be that we don\u2019t have AI agents as teachers but what of AI teaching assistants to interact in limited ways? Is there a place for a Jill Watson in some of our courses and programs? What are the roles for human teachers vs machine teachers? These are questions we need to consider and be prepared to answer because without a doubt the landscape of education is changing. In particular, for us, the most immediate question is this: Could bots handle the teaching of first-year college composition? That possibility could be upon us in another five to ten years<\/a>.<\/p>\n We close this post by inviting ongoing conversation and action around these issues among ourselves in the rhetoric\/writing field, with colleagues in other fields, and with working professionals.<\/p>\n In general we feel that a ludditic rejection of AI is not the answer. Instead, calling on the critical theory of Andrew Feenberg (1991) and others (Selber, 2004), we advocate for a critical engagement with technology development to insure that its designs and uses are truly smart, not just convenient or cost cutting, appropriate for our educational mission and goals and for our students. By turning our research and our teaching to AI-related areas, we will position ourselves and our students to be at the forefront of the changes, helping, we hope, to critically shape technology development, policy, and usage rather than merely reacting to it.<\/p>\n This blog post is a portion of a longer article-in-progress that we are currently completing. We have also written about this topic in a chapter titled \u201cAI Agents as Professional Communicators,\u201d in our book Professional Communication and Network Interaction: A Rhetorical and Ethical Approach<\/i><\/a> (Routledge, 2017).<\/p>\n Bessi, Alessandro, & Ferrara, Emilio. (2016). Social bots distort the 2016 U.S. Presidential election online discussion. First Monday, 21<\/i>(11). http:\/\/firstmonday.org\/ojs\/index.php\/fm\/article\/view\/7090\/5653a<\/a><\/p>\n Eicher, Bobbie, Polepeddi, Lalith, & Goel, Ashok. (2017). Jill Watson doesn\u2019t care if you\u2019re pregnant: Grounding AI ethics in empirical studies. (2017). Georgia Tech Library. http:\/\/dilab.gatech.edu\/publications\/jill-watson-doesnt-care-if-youre-pregnant-grounding-ai-ethics-in-empirical-studies\/<\/a><\/p>\n Ericsson, Patricia Freitag, & Haswell, Richard H. (eds.). (2006). Machine scoring of student essays: Truth and consequences<\/i>. Logan, UT: Utah State University Press.<\/p>\n Feenberg, Andrew. (1991). Critical theory of technology<\/i>. Oxford: Oxford University Press.<\/p>\n Goel, Ashok, & Polepeddi, Lalith. (2016). Jill Watson: A virtual teaching assistant for online education. Georgia Tech Library. https:\/\/smartech.gatech.edu\/handle\/1853\/59104<\/a><\/p>\n Hart-Davidson, William. (2018). Writing with robots and other curiosities of the age of machine rhetorics. In Jonathan Alexander & Jacqueline Rhodes (eds.), The Routledge handbook of digital writing and rhetoric <\/i>(pp. 248-255). New York: Routledge.<\/p>\n Herrington, Anne, & Moran, Charles. (2001). What happens when machines read our students\u2019 writing? College English, 63<\/i>(4): 480-499.<\/p>\n McKee, Heidi A., & Porter, James E. (2017). Professional communication and network interaction: A rhetorical and ethical approach<\/i>. New York: Routledge\/Series in Rhetoric and Communication.<\/p>\nChat Bots & Writing Bots<\/h2>\n
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AI Instructors & Teaching Assistants<\/h2>\n
Call to Conversation and Action<\/h2>\n
Note<\/h2>\n
References<\/h2>\n