Artificial intelligence (AI) is changing how faculty and students interact in online courses, and how graduates will approach their future careers (Bowen & Watson, 2026). When the company OpenAI launched the demo of its first AI chatbot on November 20, 2022, it reached 100 million users in just two short months (Bowen & Watson, 2026). “Like the internet, AI is a technology that is going to change everything—and not just education” (Bowen & Watson, 2026, p. 1). The authors argue that graduates will be expected to use AI effectively in their future careers.
The authors successfully balance technical explanations of AI with practical guidance for faculty, administrators, and instructional designers. While the book addresses topics across multiple fields, including healthcare, law, and finance, it is particularly relevant for online graduate education due to its practical tips for teaching and learning with AI. Faculty in online graduate courses often receive limited, if any, training on using AI; thus, the book serves as a valuable resource for expanding faculty understanding and development in this area. As Bowen and Watson (2026) note, the technological revolution created by AI “will change the nature of work for everyone” (p. 32).
Purpose and Organization
In the second edition of Teaching with AI: A Practical Guide to a New Era of Human Learning, the authors examine how AI is influencing teaching, learning, and human thinking. The authors explain that the second edition of the book was necessary due to the rapid advancements of AI since the first edition was published. The second edition expands the discussion of AI-assisted teaching, simulations, and models. The book is divided into three sections: Thinking With AI, Teaching with AI, and Learning with AI.
Thinking With AI explores the technical foundations of AI, including machine learning, neural networks, prompt writing, AI literacy, and ethics. The authors explore these topics across multiple disciplines. Recognizing that readers may have varying interests in the technical details of AI, the authors encourage them to focus on the chapters that align best with their interests.
Teaching With AI is the most relevant to online graduate education. It explores practical applications of AI in teaching, research, course development, grading, academic integrity, and institutional policy. The book contains numerous prompt examples and templates to allow readers to begin using AI immediately while considering the opportunities and limitations of AI in higher education.
Learning with AI shifts the focus to how AI can be used to enhance student learning. The authors explore AI as a tutor, writing partner, assessment tool, and collaborative learning resource. The book concludes with a discussion of AI bots and the future of teaching and learning with AI.
The authors provide several examples of AI use and prompts throughout the text. Few examples focus specifically on online graduate education; however, many of the examples and prompts are relevant to the field. Future versions of the book could include more examples and resources that are specific to online and graduate programs to make the resource more relevant to this audience.
Major Contributions
AI as a Co-Instructor
One of the book’s most significant contributions is its discussion of AI as a partner in supporting faculty. The authors note that AI can assist faculty by managing schedules, organizing meeting notes, summarizing previous interactions, and generating follow-up recommendations for individual students. These capabilities are particularly valuable in online graduate education, where faculty regularly provide individualized student support through written communication.
Drawing upon contemporary research, the authors also describe AI’s ability to detect stress indicators and generate responses that demonstrate emotional intelligence. AI can process information quickly, identify trends over time, and assist faculty with drafting announcements, emails, and assignment feedback that conveys encouragement while maintaining a professional tone. The authors further argue that educators should use AI to strengthen their existing instructional practices: “It has always been the job of educators to help students become better thinkers. Our new job is to help them become even better thinkers in collaboration with AI” (Bowen & Watson, 2026, p. 87).
Chapter 10 focuses on the consistency and efficiency of AI-assisted grading. The authors outline a five-step process for implementing AI-assisted grading, emphasizing AI model selection, clear learning outcomes, effective prompting, and quality control. This framework provides a practical roadmap for online graduate faculty and institutions to incorporate AI into the grading process while maintaining instructional oversight.
Bowen and Watson (2026) also explore AI as a teaching assistant and tutor. Examples include faculty-created chatbots that respond to common student questions and AI tools that review assignment requirements, assess student understanding, and provide formative feedback before students submit their assignments. For students in online graduate education, the availability of this type of guided feedback may promote greater confidence in their work and reduce misunderstandings about assignment expectations.
AI as a Research Partner
Faculty in online graduate programs often publish and present research. In Chapter 6, the authors examine how faculty can use AI for research support. The authors note that AI can help faculty generate research ideas, refine research questions, review literature, summarize information, and cite sources in the appropriate style. The authors describe various AI tools for writing, editing, and workflow management. For example, one scenario demonstrates a researcher using an AI agent to search databases, retrieve articles, and organize them into folders that are ready for review. Automation of these early steps may allow researchers to devote more time to scholarly analysis of literature.
AI for Course Development
In Chapter 7, the authors focus on instructional design and course development with AI. The authors describe how AI can be used to create and evaluate discussion board prompts, quizzes, and assignments for courses. For instance, instructors or course designers can ask AI to suggest ways to better align an assignment with course learning outcomes and to recommend revisions.
Chapter 10 expands this discussion by offering tips and prompts for using AI to design rubrics. The chapter also encourages the integration of responsible AI use into student assignments. For instance, the authors provide an example of students building AI bots as part of an assignment. The specific prompts and examples are likely to be of interest to faculty and course designers in online graduate education.
AI for Administration
In Chapters 8 and 9, the authors address AI and how it pertains to plagiarism, academic integrity, and institutional policy. The authors provide practical steps and interventions for universities to consider when incorporating AI into institutional policies and programs. They also discuss strategies for reducing academic integrity violations. The included policy templates and guidelines are likely to interest institutional leaders in online graduate education.
Concerns
Bowen and Watson (2026) focus on the many advantages of AI throughout the book. The authors also acknowledge that there are several concerns with AI in higher education, ranging from learning curves and academic integrity to occupational impact. Each of these topics is likely to be of interest to faculty and administrators in online graduate education.
Learning Curve
Bowen and Watson (2026) note that while AI can improve efficiency, users must first learn how to write precise prompts and evaluate AI-generated responses critically. According to the authors, it takes time to learn to use AI; thus, task completion may seem slower initially. The book provides many prompt-writing examples that readers can adapt when creating their own AI prompts.
Security and Ethics
In online graduate education, faculty and students may be apprehensive about using AI because of security and ethical concerns. Online graduate faculty do need to be aware of AI-use policies established by universities and publishers. According to Bowen and Watson (2026), AI systems can provide inaccurate information, and data privacy and security are also important concerns. The authors note that AI is so heavily ingrained in our society that most people underestimate how frequently they are already using AI. Bowen and Watson (2026) write, “If you watch Netflix, use a spam filter, shop at Amazon, or drive a car, you are already a part of the new AI and data-center economy” (p. 93). The authors further note that privacy and intellectual security are also concerns with using AI: “[A]nything you ask an AI is collected and used” (Bowen & Watson, 2026, p. 163). These elements are important to keep in mind when considering whether to use AI in online graduate education.
The authors explain that AI has been used across various fields and platforms for some time. It is the availability, capability, and role of AI that have shifted dramatically in recent years. “Previous AI helped curate your world (or what you saw and read online), but GPT AI now allows you to create your world” (Bowen & Watson, 2026, p. 31). The authors note that the goal is not to avoid AI in higher education, but to teach students to work with AI. They further note that faculty may be better positioned than students to use AI because they can weigh the quality of the responses that they receive from AI against their existing knowledge in the field.
Academic integrity concerns are often at the forefront of discussions about AI in education. The authors cite current research showing that seven popular AI detectors misclassified over half of essays that as AI-generated when they were not. The authors discuss the potential negative effects of falsely flagging a paper on students’ academic and future career opportunities. They also discuss the history of plagiarism and academic integrity both on campus and online, and provide practical tips to help faculty use and interpret AI detectors. The author’s recommendations regarding AI policies and interventions warrant further consideration. For instance, Chapters 8 and 13 include recommendations to require writing histories, version tracking, and related documentation from students to demonstrate that AI was not used for their assignment. Caution must be exercised, as this places an additional requirement on students to defend their authorship rather than instructors evaluating evidence of learning in each assignment. As AI becomes more widely implemented in writing tools and university resources, faculty may benefit from evaluating student understanding of course material rather than focusing on the tools that were used. The authors note that when the internet was initially released, some educational institutions took steps to restrict its use to preserve academic integrity; yet, they soon recognized the internet as a necessary tool for education. The authors draw a parallel in the acceptance of the internet and AI in higher education.
Workload
Bowen and Watson (2026) question the common assumption that AI will create more leisure time for workers who use it. Rather, they suggest that increased efficiency is likely to correspond with increased expectations of quality and quantity. In online graduate education, perceived increases in faculty free time due to AI may be offset by greater numbers of students assigned to each course or by increased expectations for scholarship and university service.
Recommendations
Teaching with AI: A Practical Guide to a New Era of Human Learning is a valuable resource for faculty who seek to better understand the rapidly evolving role of AI in higher education. Online graduate faculty will likely find the discussions of teaching, research, grading, and student support particularly useful. Instructional designers may benefit from practical guidance on developing assignments and rubrics aligned with learning outcomes, while academic administrators may find the policy recommendations and institutional guidelines especially valuable.
Overall, Bowen and Watson (2026) effectively balance the technical explanations of AI systems with practical strategies for implementing AI in education. Some recommendations may require refinement as AI continues to evolve; however, the book provides a thoughtful and informative resource for teaching and learning with AI. It is likely to serve as a valuable reference for faculty, instructional designers, and administrators seeking to incorporate AI into higher education.