1 Introduction

Introduction

The rise of AI has been described as the fourth industrial revolution (French et al., 2021), and it demands attention from those in higher education. With the stroke of a keyboard, generative artificial intelligence (GenAI) tools like ChatGPT can provide users with comprehensive and concise information about virtually any topic, and even write “frighteningly good” essays (Kelly, 2022, p. 1). While such technological advancements hold the power to enhance student comprehension and generate ideas as a “digital muse” (Gesikowski, 2023, para. 1), there is also widespread concern about academic integrity implications (Dale, 2023). Additionally, overreliance on AI tools may diminish critical thinking (Gister, 2023), limit mastery of important concepts and jeopardizes instructors’ ability to assess learning. Despite these issues, educators are increasingly embracing this technology, finding innovative ways to harness AI to elevate teaching and even enhance critical thinking (Magruder, 2023a). So, how can instructors leverage AI so that they are more efficient, creative, and effective in the classroom? This resource guide provides various practical applications which educators can immediately implement in their course(s). Before looking at the applications, it’s important to provide some context about AI, including the limitations and opportunities for its use and defining key terms in the AI field.

Background

AI is a broad term encompassing a branch of computer science that involves the development of algorithms and systems capable of performing functions that would have previously required human intelligence These include learning, problem-solving, and processing the meaning of language (IBM, n.d.). Simply put, AI refers to computer systems designed to perform tasks that mimic human intelligence, such as visual and speech recognition, decision-making, identifying patterns, and language translation (Russell & Norvig, 2020). AI has been under development for many years, but beginning in November 2022 with the launch of ChatGPT there has been an explosion of public awareness and access to AI tools. Many Americans interact with AI without even realizing it. There are many technologies that integrate AI, including traffic navigation apps, healthcare data analytics, and virtual assistants like Amazon’s Alexa and Apple’s Siri.

Under the umbrella of AI, large language models (LLMs) are of particular interest for educators. LLMs are a form of generative AI, meaning that they generate new content that does not currently exist elsewhere based on unique user inputs or prompts (Trott et al., 2023). As the name implies, LLMs work by learning from vast amounts of written text from sources like the internet and published books (OpenAI, 2022). In addition to providing factual information, LLMs can handle creative tasks, from writing poetry and customized bedtime stories, to editing audio and video, to adjusting written work to fit a certain style, such as reworking scholarly writing for third grade reading level comprehension. Clearly, there are many possibilities for using AI in educational settings.

Much of the activities in this guide will center around generative AI (GenAI), a subset of AI that focuses on creating novel content rather than merely analyzing or acting upon existing data (Zewe, 2023). GenAI most famously produces original text, but can also create images, video, and music. This technology is made possible by LLMs, those sophisticated AI systems trained on extensive datasets containing vast amounts of text in order to understand and generate human-like language (IBM, 2024). With these key terms in mind, this next section explores the benefits and drawbacks .

Limitations of AI

Before educators fully embrace AI, they should be aware of the pitfalls associated with these tools. For example, misinformation propagated through AI can mislead students and instructors alike, undermining quality education (Monteith et al., 2024). In fact, AI can produce hallucinations, in which the LLM provides false information such as fictitious citations (Alkaissi & McFarlane, 2023). Additionally, LLMs are trained on existing data. Therefore, biases that exist in the data are also embedded within AI algorithms, possibly perpetuating systemic inequalities (Agarwal et al., 2023). Overreliance on GenAI may also diminish students’ critical thinking (Gister, 2023), limit mastery of important course concepts and jeopardize instructors’ ability to assess true learning. Furthermore, issues of academic integrity are exacerbated by GenAI tools capable of producing high quality writing (Hodgson et al., 2022), raising concerns about plagiarism and cheating (Holmes & Porayska-Pomsta, 2023). Moreover, so-called AI detectors are notoriously unreliable (Sadasivan et al., 2023), leading to many universities banning their use (Ghaffary, 2023) and thus limiting professors’ ability to enforce AI policies. Finally, AI holds the potential to further deepen the digital divide (Ben-Avie, 2024), leaving those without access to technology, or without more advanced technological skills, behind. AI certainly poses a challenge to educators, and considering the issue only with rose-colored glasses is unwise. However, as we will discuss there are many positive aspects to using AI.

Opportunities

AI can significantly improve both teaching and learning outcomes (Siau & Wang, 2020) as newly emerging AI technologies hold the power to enhance student comprehension and aid in idea generation (Gesikowski, 2023). It also holds significant potential to aid educators. For example, GenAI can help instructors create grading rubrics, simplify assignment directions, generate case studies, come up with discussion questions, generate video captions, and much more. Current research is pointing to AI as a means to quickly take care of mundane administrative work, freeing up time for other efforts that require a human touch (Daugherty & Wilson, 2018). For example, AI can create a template for letters of recommendation, generate ideas for specific types of emails, or provide proofreading assistance on research manuscripts. By taking advantage of what AI has to offer educators can focus their efforts on high impact tasks that involve direct student interaction. Overall, AI should be seen as a partner in learning which can and should be utilized in an ethical and responsible manner.

Looking Ahead

Educators should be careful not to prematurely leap to either a utopian or apocalyptic view of an AI-enhanced world (Boyd & Holton, 2018). Instead, professors and educational administrators should pragmatically assess how this technology can be ethically utilized to enhance learning. To actualize this vision, [we] must be prepared to capitalize on the advantages and address the challenges associated with its incorporation into the classroom (Magruder, 2023b).

This volume presents a series of activities that educators from a wide variety of disciplines at the University of Texas at Arlington have used to integrate AI in their courses. Keep in mind that use of any specific technology, software, or application should be approved at the institution level prior to implementation.

References

Agarwal, R., Bjarnadottir, M., Rhue, L., Dugas, M., Crowley, K., Clark, J., & Gao, G. (2023). Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework. Health Policy and Technology, 12(1), 100702. https://doi.org/10.1016/j.hlpt.2022.100702

Alkaissi, H., & McFarlane, S. I. (2023). Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus, 15(2). 10.7759/cureus.35179

Ben-Avie, J. (2024, January 18). Don’t let AI become the newest digital divide. Council on Foreign Relations. https://www.cfr.org/blog/dont-let-ai-become-newest-digital-divide

Boyd, R. and Holton, R. J. (2018). Technology, innovation, employment and power: Does robotics and artificial intelligence really mean social transformation? Journal of Sociology, 54(3), pp. 331–45. https://doi.org/10.1177/1440783317726591

Dale, M. (2023). ChatGPT and social work: be curious, excited and skeptical. NASW. https://www.socialworkers.org/News/Social-Work-Advocates/June-July-2023-Issue/ChatGPT-and-Social-Work-Be-Excited-Curious-and-Skeptical

French, A., Shim, J. P., Risius, M., Larsen, K. R., & Jain, H. (2021). The 4th Industrial Revolution powered by the integration of AI, blockchain, and 5G. Communications of the Association for Information Systems, 49(1), 6.

Hodgson, D., Goldingay, S., Boddy, J., Nipperess, S., & Watts, L. (2022). Problematising artificial intelligence in social work education: Challenges, issues and possibilities. The British Journal of Social Work, 52(4), 1878-1895. https://doi.org/10.1093/bjsw/bcab168

Holmes, W., & Porayska-Pomsta, K. (Eds.). (2022). The Ethics of Artificial Intelligence in education: Practices, challenges, and debates. Taylor & Francis.IBM. (n.d.). What is artificial intelligence? https://www.ibm.com/topics/artificial-intelligence

IBM. (2024, June 12). What are LLMs? https://www.ibm.com/topics/large-language-models

Gesikowski, C. (2023). The digital muse: an AI awakening. Medium. https://gesikowski.medium.com/the-digital-muse-an-ai-awakening-fef9b52d415b

Ghaffary, S. (2023, September 21). Universities rethink using AI writing detectors to vet students’ work. Bloomberg News. https://www.bloomberg.com/news/newsletters/2023-09-21/universities-rethink-using-ai-writing-detectors-to-vet-students-work

Gister. (2023). Are AI tools destroying critical thinking? Gister. https://gigster.com/blog/are-ai-tools-destroying-critical-thinking/

Kelly, S. (2022, December 5). This AI chatbot is dominating social media with its frighteningly good essays. CNN. https://www.cnn.com/2022/12/05/tech/chatgpt-trnd/index.html

Magruder, K. (2023a). Engaging students in critical analysis of AI-generated responses: contributing to the knowledge base. [Conference presentation]. Council for Social Work Education (CSWE) Annual Program Meeting 2023, Atlanta, Georgia, United States.

Magruder, K. (2023b). Artificial intelligence and social work education. Social Work Today, 23(3). https://www.socialworktoday.com/archive/Summer23p26.shtml

Monteith, S., Glenn, T., Geddes, J. R., Whybrow, P. C., Achtyes, E., & Bauer, M. (2024). Artificial intelligence and increasing misinformation. The British Journal of Psychiatry, 224(2), 33-35. https://doi.org/10.1192/bjp.2023.136

OpenAI. (2022, November 6). Introducing ChatGPT. OpenAI. https://openai.com/blog/chatgpt

Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.

Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2023). Can AI generated text be reliably detected?. arXiv preprint arXiv:2303.11156.

Siau, K., & Wang, W. (2020). Artificial intelligence (AI) ethics: Ethics of AI and ethical AI. Journal of Database Management (JDM), 31(2), 74-87. https://doi.org/10.4018/JDM.2020040105

Trott, S., Jones, C., Chang, T., Michaelov, J., & Bergen, B. (2023). Do Large Language Models know what humans know?. Cognitive Science, 47(7), e13309. https://doi.org/10.1111/cogs.13309

Zewe, A. (2023, November 9). Explained: Generative AI. MIT News. https://news.mit.edu/2023/explained-generative-ai-1109