{"id":2616,"date":"2023-08-14T11:58:32","date_gmt":"2023-08-14T15:58:32","guid":{"rendered":"https:\/\/krieger.jhu.edu\/writing-program\/?page_id=2616"},"modified":"2023-11-27T11:20:07","modified_gmt":"2023-11-27T16:20:07","slug":"ai-writing-in-the-college-classroom","status":"publish","type":"page","link":"https:\/\/krieger.jhu.edu\/writing-program\/writing-in-the-majors\/concepts-and-practices\/ai-writing-in-the-college-classroom\/","title":{"rendered":"AI Writing in the College Classroom"},"content":{"rendered":"\n

Written by Nate Brown<\/p>\n\n\n\n

Attendant to the rise of powerful artificial intelligence (AI) technologies like ChatGPT 4, Perplexity, Jasper, YouChat, Chatsonic, and others, instructors in higher education must consider how students and teachers will (or will not) use AI tools in the classroom and beyond. <\/p>\n\n\n\n

There is already ample evidence that AI technologies built on large language models (LLMs) can produce text, images, and computer code essentially indistinguishable from work produced by humans. Ethan Mollick at the Wharton School of Business at the University of Pennsylvania has signaled cautious optimism<\/a> about AI\u2019s potential to save time on iterative technical and compositional tasks in the workplace. AI skeptics, on the other hand, express concern about the use of the technology to produce false or misleading information and\/or to misrepresent authorship. <\/p>\n\n\n\n

As of this writing, there is a clear prerogative to help students navigate these new tools so that their original intellectual efforts and products are presented responsibly and assessed fairly.  <\/p>\n\n\n\n

In what follows, we lay out four starting assumptions about AI that guide our recommendations. These first principles are drawn from the current literature and may be helpful to discuss with students to ensure that everyone is on the same page regarding AI\u2019s capabilities. Then, we suggest five best practices for using AI in the classroom.  <\/p>\n\n\n\n

Starting Principles<\/h2>\n\n\n\n

1. AI is here to stay. <\/strong> <\/h3>\n\n\n\n

Generative AI is already widely available to populations around the globe, which means that it\u2019s incumbent upon professors and teaching assistants to familiarize themselves with the current AI landscape and to develop clear AI policies for their own courses.<\/p>\n\n\n\n

2. Generative AI cannot create novel material in a traditional sense.<\/strong><\/h3>\n\n\n\n

As Mollick points out, the terms associated with various generative AI technologies are still ill-defined but describe algorithmic systems that use large data sets to “[predict] what the next word in a sentence should be so it can write a paragraph for you [and] what an image should look like based on a prompt.\u201d <\/p>\n\n\n\n

In other words, generative AI creates new combinations of extant language, images, and computer code when prompted to do so. <\/p>\n\n\n\n

The ethical considerations here are vast, but of particular importance in the classroom is a reminder to students that their original thoughts, analyses, scholarship, and labor are central to their educational development and to the advancement of intellectual and academic pursuits. AI cannot produce novel thought.  <\/p>\n\n\n\n

3. While many AI technologies are described as \u201cgenerative,\u201d current iterations are not sentient or self-aware. <\/strong> <\/h3>\n\n\n\n

The current conversation about AI is muddied by occasional claims of sentience, which make for surprising and enticing headlines. In the summer of 2022, Google Engineer Blake Lemoine made the news when he claimed that Google\u2019s AI-driven chatbot<\/a>, LaMDA, was alive.  <\/p>\n\n\n\n

Then, in the spring of 2023, New York Times<\/em> technology writer Kevin Roose published a column entitled \u201cA Conversation With Bing\u2019s Chatbot Left Me Deeply Unsettled\u201d<\/a> alongside a transcript of his chat with Bing\u2019s Chat GPT4-powered search engine.  <\/p>\n\n\n\n

Most technologists working in the AI space are quick to point out that enormous datasets and advances in artificial neural networks (ANNs), which are inspired by the structure of the human brain, have honed the most advanced chatbots\u2019 ability to mimic natural language.  
 
In discussing AI technologies with students, instructors should take care to make this distinction clear, both so that the next buzzy headline doesn\u2019t mislead them and so that the class has an opportunity to discuss the potential uses, advantages, and pitfalls of producing text that is virtually indistinguishable from organic human language.  <\/p>\n\n\n\n

4. Most generative AI chat tools cannot access paywalled research. <\/strong> <\/h3>\n\n\n\n

This limitation means that what any LLM-based AI tool can generate will not necessarily include the best, most recent, or most relevant peer-reviewed data in a given field. For research purposes, then, AI-generated text is particularly limited.  <\/p>\n\n\n\n

While this may change over time, current chatbots are not well suited for creating reliable and legitimate university-level research. Instead, they tend to present general information culled from a massive dataset of popularly available information, like news articles, blog posts, and trade publications.  <\/p>\n\n\n\n

Best Practices<\/h2>\n\n\n\n

Here\u2019s our best advice for how to address the use (or prohibition) of AI technologies in the classroom:<\/p>\n\n\n\n

1. Have frank discussions with students about the potential uses and limitations of AI technologies in the classroom and beyond. Guiding questions might include: <\/strong> <\/h3>\n\n\n\n
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