Before the rise of generative AI, various AI systems were already employed in higher education. These systems focused on tasks such as identifying at-risk students, recommending courses, boosting motivation, and predicting student performance. Your institution’s homepage might deploy a chat bot to answer questions, one of the most common uses of AI in higher education.

The introduction of generative AI has further expanded these capabilities, offering adaptive and automated assessments, personalized learning experiences, tutoring, and feedback opportunities. In higher education, generative AI is utilized across disciplines for creating content, writing code, facilitating research, addressing accessibility concerns, and restructuring writing processes. The recent development of students using AI-powered chatbots to generate essays, code, and digital art has sparked discussions on academic integrity, the evolving nature of learning, and the role of technology in the classroom.

AI & Academic Integrity

Academic integrity became the locus of concerns around generative AI upon the launch of ChatGPT (often to the detriment of other needed discussions around the use of AI in higher education). With the arrival of large language models (LLMs) has come new concerns surrounding academic integrity, plagiarism, and cheating. As Janelle Miles articulated in an early article responding to these concerns, What is ChatGPT and why are schools and universities so worried about students using AI to cheat?:

The big concern is that ChatGPT could potentially be used by university and school students to cheat on written assignments without being detected. It’s been described as akin to students outsourcing their homework to robots.

Leon Furze argues:

It is also still unclear to what extent using an AI constitutes “cheating.” It is not, strictly speaking, plagiarism as the output of the model is not copied from another source. Rather, the output is an original creation which is generated “probabilistically.” Knowing where to draw the line raises ethical questions about academic integrity and honesty.

Faculty and, in some cases, institutions as a whole, have been revisiting academic integrity policies to address the use of AI. Policies range from outright bans of the technologies to those that allow and even encourage AI usage. For example, Texas Tech University offers three Recommended Syllabus Statements for AI Use, including if AI use is encouraged and allowed in a course, if AI use is allowed only for specific assignments, or if AI use is prohibited in a course. The University of North Texas offers similar guidance: AI, Plagiarism, and Academic Integrity.

Appropriate attribution and acknowledgment of AI technologies used to create assignments and other products of learning are crucial as well. In education, AI and academic integrity are inevitably tied to intellectual property and copyright. These are discussed in more detail in Lesson 7.

For Faculty and Students

As we move towards a world of hybrid human/Al work, understanding how to use generative Al will be a critical digital information literacy skill. To be competitive in the workforce, students will need to know how to use generative Al to produce a variety of work products. This will mean that students will need to know how to craft prompts, evaluate responses, determine appropriate use of AI, and guide the Al into creating the appropriate output.

Today’s students likely already are interacting with numerous AI-embedded tools daily. AI’s ongoing development for teaching and learning promises to expand these tools to create more personalized, on-demand student success tools to provide:

• Tailored learning materials on customized paths based on their progress and strengths (e.g., Knewton Alta)
• Just-in-time help to explain, clarify, or recommend resources (e.g., IBM Watson or ChatGPT)
• Tutoring and coaching (e.g., Khan Academy’s Khanmigo)
• Improve writing and language skills (e.g., Grammarly and Google Translate)
• Academic  Support (e.g., Starfish)
• Virtual reality (VR) and augmented reality (AR) applications, when integrated with AI, can create immersive and interactive active learning experiences (e.g., Meta Quest)

This article in the OpenStax blog offers helpful tips for “harnessing the power of AI in teaching and learning”: The AI shakeup in education.

For Faculty and Instructional Designers

AI is rapidly changing course design and instructional processes in higher education, with numerous platforms integrating AI tools to help instructors auto-generate course content. For example, in April 2023, Coursera announced that, with their new AI-assisted course builder, with just “a few simple inputs from a human author, a new set of AI-powered features can auto-generate course content  — such as overall course structure, readings, assignments, and glossaries — to help educators dramatically reduce the time and cost of producing high-quality content” (Goli, 2023).

AI is reshaping instructional design in higher education and can be used to create personalized learning experiences for students while optimizing course content, from personalized and adaptive learning content to intelligent tutoring systems, natural language processing, gamification, content creation, assessment, and feedback generation, and learning analytics resource allocation (Gibson, 2023). In a 20-minute webinar, Exploring AI In Instructional Design: 5 Essential Strategies, Lance Eaton (director of faculty development and innovation at College Unbound) offers five tips for using generative AI in instructional design. He provides one especially helpful tip to improve prompts for better outputs with generative AI: “The first question to ask should ALWAYS be to improve the question you want to ask. I usually start with ‘Improve this prompt to maximize the creativity and analytical abilities of a large language model’ and then I insert my prompt. The new prompt it provides is the prompt I use, and I always get better results.”

The Artificial Intelligence (AI) & Adapting to Innovation group in OERTX features resources curated by the Texas Higher Education Coordinating Board’s Division of Digital Learning to guide faculty, staff, and administrators as they incorporate Artificial Intelligence (AI) in their classrooms and institutional operations. There is also a discussion board that includes topic-based conversations with links provided to some resources within the discussion prompt.

Examples of resources shared with the group include:

• Bloom’s Taxonomy Revisited for AI: a resource developed by Oregon State University’s E-Campus.
• Ethics of AI: a free online course created by the University of Helsinki.
• Exploring the Real-life Impacts of AI in Higher Education: Sam Houston State University presentation of ethnographic research on AI in higher education and future jobs.
• Generative AI in the Rhetoric & Composition Classroom: resource published by the Texas A&M University Libraries designed to support instructors and students as they navigate the presence of generative AI tools, specifically Large Language Models (LLMs) such as ChatGPT, in the rhetoric and composition classroom.