We discuss the following topics on this page:

• Introduction: Using AI for Literary Research Projects
• What is Artificial Intelligence and How is it Relevant for Literary Research?
• Prompt Engineering: Shaping AI Responses
• Leveraging AI for Critical and Creative Inquiry
• Why CoPilot?
• Maintain Academic Integrity with AI

Introduction: Using AI for Literary Research Projects

Why include a page about AI in this textbook? Because most students already use AI tools, but not all have learned how to use them appropriately and effectively. This introductory discussion aims to close that gap. We will help you to determine which aspects of your research project could benefit from AI support, select the right tools for your goals, and craft prompts that yield meaningful and relevant responses. We also discuss how to ethically use AI to account for copyright issues, intellectual property rights, and the subtle biases and cultural stereotypes that often surface in AI-generated content. We’ll focus on critical engagement with these technologies and develop the skills to use AI efficiently, ethically, and with academic integrity.

What is Artificial Intelligence and How is it Relevant for Literary Research?     

“Artificial Intelligence” is an umbrella term that encompasses thousands of applications performing a variety of tasks ranging from audio-to-text, image-to-text, video-to-text, question answering, document retrieval, image classification, text-to-image, image-to-video, text-to-3D, text classification, translation, text ranking, text-to-speech, speech recognition, and many others. For most tasks in literary research, though, the appropriate tools are Large Language Models (LLMs) and Reasoning Engines. An LLM “is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation” (Wikipedia), while a reasoning engine “is a piece of software able to infer logical consequences from a set of asserted facts or axioms” (Wikipedia).

When used with care and critical awareness, these tools offer transformative possibilities for interpreting texts, generating new insights, and sparking meaningful scholarly inquiry.

Prompt Engineering: Shaping AI Responses

Prompt engineering is the practice of strategically crafting inputs that guide AI systems to produce accurate and relevant outputs. Because Large Language Models (LLLMs) and Reasoning Engines are natural language processors, you don’t need to learn special code to interact with them. However, these systems are sensitive to the quality of your prompts and can easily misinterpret vague and ambiguous instructions. That’s why prompt engineering is so essential: By making your inputs specific, contextually rich, and precisely worded, you can steer AI toward responses that best suit your needs. For instance, you may need to prompt the AI to discard certain types of responses, concentrate on particular criteria, or adopt a particular tone.

Leveraging AI for Critical and Creative Inquiry

We focus on two primary applications of AI: Harnessing existing knowledge and generating new insights. At its most basic level, AI excels at gathering, summarizing, organizing, and connecting information; tasks that significantly streamline the research process. However, our deeper focus lies in AI’s capacity to facilitate the creation of new knowledge, particularly within the context of literary and humanities research. To that end, each relevant module now includes a dedicated section offering practical strategies for using AI to identify compelling problems, formulate and refine research questions, test thesis statements, develop targeted search strategies, and locate high-quality sources. In this context, AI is more than a mere tool; it is a dynamic, creative, critically engaged research companion.

It’s important to emphasize that AI is entirely optional. Scholars have been innovating without AI for generations and will continue to do so. Whether or not to use AI is a personal choice (though your instructor may constrain that choice). Our approach is simply to encourage anyone who engages with AI to do so in a thoughtful and critical way. While AI offer, exciting possibilities for creativity, exploration, and innovation, there are also valid concerns about the negative effect of AI on critical thinking skills and the ethical issues around bias, the theft of intellectual property, and that many tools “hallucinate,” which means they “make things up” and refer to non-existent sources. If you choose to incorporate AI into your research process, you are responsible for vetting and verifying its outputs before integrating them into your research. We will discuss these issues in greater depth later in the course when we discuss locating and citing reliable sources.

Why CoPilot?

We have chosen Microsoft’s CoPilot because it’s supported at our home institution, the University of Central Florida, and is therefore the most secure and accessible for our students. However, the strategies we discuss throughout this course will apply to any option you choose.

To access CoPilot with your UCF account, log in here.

Maintain Academic Integrity with AI

The best way to maintain academic integrity when using AI is to cite its output properly. The Modern Language Association (MLA) has a page about the proper citation of AI. We expand on proper citation in Chapter 13.