AI: Artificial Intelligence: Teaching and AI

This guide focuses on generative AI.
We cover only AI that can generate text, images, video, music, or speech. Examples: ChatGPT, Microsoft Copilot, Gemini, Perplexity, Adobe Firefly, Midjourney, Eleven Labs, and more.

Office of Educational Technology

We’ve curated some helpful information for you to consider as you think about how AI might impact your teaching, course design, and student learning.

Mississippi Department of Education

Abstract
This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and limitations. The authors propose seven approaches for utilizing AI in classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AI-simulator, and AI-student, each with distinct pedagogical benefits and risks. Prompts are included for each of these approaches. The aim is to help students learn with and about AI, with practical strategies designed to mitigate risks such as complacency about the AI’s output, errors, and biases. These strategies promote active oversight, critical assessment of AI outputs, and complementarity of AI's capabilities with the students' unique insights. By challenging students to remain the "human in the loop," the authors aim to enhance learning outcomes while ensuring that AI serves as a supportive tool rather than a replacement. The proposed framework offers a guide for educators navigating the integration of AI-assisted learning in classrooms.

Educators everywhere are grappling with the disruptive technology of generative artificial intelligence (AI). Librarians, who are at the center of information literacy instruction in many schools and universities, are juggling the task of learning about this quickly evolving technology while also teaching students about the algorithms within language learning models that lead to the creation of false information and fabricated references. As librarians and other educators explore these quickly emerging AI tools, we should involve students in conversations and experiential activities that investigate both the potential and challenges associated with using AI tools as part of the learning process.

20 Activities and 9 Lesson Plans developed in collaboration by the University of Texas at Austin and Grammarly.

Generative Minds is a journal dedicated to exploring the transformative potential of artificial intelligence in education. We spotlight groundbreaking research, innovative classroom practices, and interdisciplinary approaches that place equity, ethics, and creativity at the core of AI adoption.

From pedagogy aimed at early childhood to higher education, from lesson planning to educational policy, Generative Minds fosters dialogue among educators, technologists, and researchers building the future of AI-powered learning.

Abstract
This paper explores how instructors can leverage generative AI to create personalized learning experiences for students that transform teaching and learning. We present a range of AI-based exercises that enable novel forms of practice and application including simulations, mentoring, coaching, and co-creation. For each type of exercise, we provide prompts that instructors can customize, along with guidance on classroom implementation, assessment, and risks to consider. We also provide blueprints, prompts that help instructors create their own original prompts. Instructors can leverage their content and pedagogical expertise to design these experiences, putting them in the role of builders and innovators. We argue that this instructor-driven approach has the potential to democratize the development of educational technology by enabling individual instructors to create AI exercises and tools tailored to their students' needs. While the exercises in this paper are a starting point, not a definitive solutions, they demonstrate AI's potential to expand what is possible in teaching and learning.

To help you explore some of the ways students can use this disruptive new technology to improve their learning—while making your job easier and more effective—we’ve written a series of articles that examine the following student use cases:

AI as feedback generator
AI as personal tutor
AI as team coach
AI as learner

Abstract
This paper provides guidance for using AI to quickly and easily implement evidence-based teaching strategies that instructors can integrate into their teaching. We discuss five teaching strategies that have proven value but are hard to implement in practice due to time and effort constraints. We show how AI can help instructors create material that supports these strategies and improve student learning. The strategies include providing multiple examples and explanations; uncovering and addressing student misconceptions; frequent low-stakes testing; assessing student learning; and distributed practice. The paper provides guidelines for how AI can support each strategy, and discusses both the promises and perils of this approach, arguing that AI may act as a “force multiplier” for instructors if implemented cautiously and thoughtfully in service of evidence-based teaching practices.