REVIEW of AI in Education: A Roadmap for Teacher-Led Transformation by Mike Kentz and Nick Potkalitsky: A humanities revolution in (AI) education
AI in Education: A Roadmap for Teacher-Led Transformation is a welcome book on AI written by pioneering educators for educators. It puts forward accessible frameworks for understanding and using ChatGPT-like technologies to enhance learning—especially at the K-12 level. Authors Mike Kentz and Nick Potkalitsky emphasize a teacher-led approach to leveraging GenAI that ensures it serves to enhance, not replace, human-centric education practices.
The book argues that AI can act as a transformative tool for education when integrated thoughtfully and ethically. And when it is led by teachers who are empowered to adapt and innovate. It stresses the importance of addressing systemic educational challenges, developing AI literacy among faculty, and reimagining educational practices with AI as a complementary resource.
This review will first look at their arguments for AI use as a humanities skill or art, then key insights, and after that provide a summary of the chapters and finally close with takeaways for (language) education and the workplace.
Revaluing the humanities skill set
Kentz and Potkalitsky’s approach to AI in education complement the recent view of the insightful and highly influential entrepreneur and venture capitalist Peter Thiel who sees advances in AI having a negatively impact on job market roles requiring strong mathematical skills; he thinks it will lead to a rebalancing between STEM and humanities disciplines. Kentz and Potkalitsky convincingly argue that humanities skills need to be at the forefront of AI use and training students (and faculty) to use AI effectively. Here’s the gist of the argument:
Premise 1: Generative AI tools are fundamentally language-based systems that require precise communication, critical evaluation, and ethical decision-making to be used effectively.
Premise 2: Humanities disciplines—such as literature, philosophy, creative writing—develop skills in critical thinking, contextual analysis, and ethical reasoning, which are essential for engaging thoughtfully and responsibly with language-based systems.
Premise 3: Journalistic skills, particularly the ability to ask thoughtful, open-ended questions and engage in iterative dialogue, are crucial for eliciting meaningful responses from generative AI and critically assessing its outputs.
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Conclusion: Therefore, humanities thinking and skills bolstered by journalistic practices like formulating probing and coherent question threads, are not only appropriate but essential for the effective and ethical use of generative AI. That’s why they are central to AI literacy and its integration into education and broader society.
Reviewing prompting
In this vein, the authors argue that the concept of "prompt engineering" should be demystified and reframed as a natural extension of humanities-based skills like critical thinking, curiosity-driven inquiry, creative writing and journalistic thinking. Specifically, they assert that:
Prompting is a language art, not a technical skill: Crafting effective prompts is akin to writing thoughtful questions or statements in humanities fields, emphasizing clarity, context, and purpose rather than requiring specialized technical expertise.
Human-centric approach: The term "engineering" implies a technical barrier that can discourage educators and students. By framing prompting as an art of communication, they make it accessible and relatable by leveraging existing skills in reading, writing, and critical inquiry.
Iterative and reflective process: Prompting is described as a dynamic and iterative process that aligns closely with practices in creative writing and journalism, where users refine their prompts based on feedback and adapt their strategy to achieve meaningful engagement with AI.
I appreciate this stripping away of jargon and emphasis on the humanistic aspects of prompting. The result is an accessible “roadmap” for an inclusive and approachable way to integrate AI into education.
Key insights and practical applications
The authors offer several other insights and applications for K-12 educators and administrators, but the three practical ones that caught my attention (as a language educator) were related to assessment practices, process writing, and literacy training across disciplines and faculty.
Transforming assessment practices to highlight critical thinking and creativity
Kentz and Potkalitsky recommend a shift from traditional, summative assessments to continuous, process-based approaches enhanced by AI. One practical method is "grading chats," where educators evaluate student interactions with AI tools like ChatGPT. This assesses not only the final answer but also the critical thinking demonstrated in refining questions and responses.
This is what I have done as well with my students, but it takes time. Teacher’s may need another AI told to streamline this process, like the Khan Academy’s Khanmigo, which Sal Khan in Brave New Words described as having the functionality of grading and assessing a student’s contribution to a project, like an essay or report, compared to the AI’s contribution.
Another example is portfolio-based assessment, where AI tracks long-term student progress across multiple projects, highlighting growth in skills such as writing or problem-solving.
These strategies foster a holistic view of student learning and would ensure evaluations capture creativity, critical engagement, and adaptability—skills essential in an AI-driven world. But again, it would be ideal to have an AI grading companion to make this more efficient for teachers who many over a hundred students to track.
AI-responsive writing curricula
The authors propose a process-oriented approach to writing education that leverages AI for iterative feedback while preserving student voice. Like to other pro-AI educators like Ethan Mollick (cf Co-Intelligence), I refuse to accept writing assignments that are linguistically incorrect and lack coherence; the authors of AI in Education similarly point out that since AI tools like Grammarly or ChatGPT provide immediate feedback on grammar and structure, students can refine their drafts independently. This can allow educators to focus on higher-order elements, such as argumentation or practicality or originality.
A classroom activity might also involve students generating a draft with AI assistance, critically evaluating the AI's suggestions, and reflecting on how these changes enhance or compromise their voice. Although I am personally on the fence with the issue of voice, since it takes years of deliberate practice to develop, I think it is good to at least get learners to critically engage with AI output and to decide how much (or how little) it “sounds” like them. This is at least the beginnings of developing an awareness of voice. This iterative process will help students learn to use AI as a supportive tool rather than a replacement for their skills.
Broadening AI Literacy Across Disciplines
Beyond assessment and writing, the authors advocate for integrating AI literacy into diverse subject areas. They propose "incubation labs" where teachers can experiment with AI tools before classroom implementation and in so doing, optimize its alignment with pedagogy. They also emphasize the humanities' role in fostering ethical AI use, teaching students to recognize biases and navigate AI outputs critically. For example, a history lesson might involve analyzing AI-generated simulations of historical events to push the students to evaluate the accuracy and ethical implications of these reconstructions.
By embedding AI literacy into curricula, educators prepare students to navigate the complexities of an AI-enhanced world. This is what first excited me as a language educator in Taiwan when I first discovered ChatGPT in December 2022: This will become an essential career skill, especially for my students going into English-dominated international business.
Summary of the chapters
Chapter 1: AI, Education, and the Five Stages of Grief
· Thoughtful exploration of educators' emotional responses to AI adoption in framework of the five stages of professional grief and adaptation (denial, anger, bargaining, depression, acceptance).
Chapter 2: AI's Impact on Existing Educational Challenges
· Examination of how AI may amplify systemic issues like equity and assessment but also offer opportunities for systemic reform.
Chapter 3: From Tech-Driven to Teacher-Led Transformation
· A persuasive argument for a teacher-first approach to AI integration that empowers educators to become meaningful innovators.
Chapter 4: Developing AI Literacy Among Faculty
· Practical steps to equip teachers with AI literacy through iterative engagement and humanities-based skills.
Chapter 5: Designing an AI-Responsive Writing Curriculum Focused on Voice
· Focus on preserving student voice in writing while leveraging AI for iterative feedback and process-based learning.
Chapter 6: Transforming Challenges into Opportunities
· Recommendation for replacing traditional grading with process-oriented assessments enhanced by AI tools.
Conclusion: A Balanced Approach for Administrators
· An address to administrators pointing out the need for ethical, teacher-driven AI integration to not just preserve but enhance human-centered values in education.
Takeaways for language education
The section on AI’s role in assessment particularly stood out for me. In an ESL or EFL classroom, the authors’ assessment suggestions like grading GenAI conversations, process-oriented feedback, portfolio-based growth tracking, and personalized adaptive assessments could be innovatively integrated to transform traditional evaluation methods. For example, students could engage with AI-powered conversational tools like ChatGPT to simulate real-world interactions, with teachers assessing not only their responses but also their ability to ask meaningful questions and give answers. AI could provide immediate feedback on grammar and syntax during these exchanges, which would complement the teacher’s focus on fluency and cultural appropriateness.
Portfolio-based assessments could capture long-term progress by compiling student work, such as recorded conversations, essays, and vocabulary logs. This could allow teachers to track improvements in writing coherence, pronunciation, fluency, and vocabulary usage over time.
Finally, AI platforms can also be used by learners to personalize quizzes and activities in order to tailor vocabulary exercises or grammar drills to individual proficiency levels. Together, these approaches not only make assessments dynamic and individualized but also encourage active engagement with language in real-(AI)-world contexts to help create more immersive and meaningful learning experiences.
Takeaways for the workplace
The workplace can also benefit from insights from the book. The three first takeaways that come to mind involve training AI literacy, fostering continuous learning, and enhancing collaboration among co-workers.
First, corporate training programs need to prioritize AI literacy to equip employees with the skills to engage critically—and productively—with AI tools, such as crafting effective prompts, analyzing outputs, and recognizing biases. This will make sure that AI becomes a collaborative partner, not just an information tool.
Second, organizations can leverage adaptive AI systems to support continuous learning and provide personalized development pathways for employees that address individual skill gaps and encourage lifelong upskilling.
Lastly, AI has the capacity to enhance project collaboration between staff and departments support, monitor, and analyze teamwork dynamics and contributions to ensure accountability and equity. Together, these approaches position companies to harness AI for innovation, professional growth, and cohesive teamwork in the evolving workplace.
AI in Education: A Roadmap for Teacher-Led Transformation offers a timely, thoughtful and teacher-empowering perspective on the integration of AI in education. It emphasizes a teacher-first approach and the critical role of humanities in navigating AI technologies. Kentz and Potkalitsky’s accessible frameworks and practical applications make a compelling case for the transformative potential of AI. Their insights provide not only a roadmap for educators but also a vision for reshaping learning and work environments in a world that—for better and worse—will be AI-driven.