A Reflection on: Hoang, F. Q. (2024). Case Study: Collaborative AI in a West Point Classroom.
I am deeply grateful to Francis Q. Hoang for sharing his work in his West Point case study on collaborative AI in the classroom. His work points to a future that is full of hope. Like me, Hoang foresees a future for education which is “relevant and valuable in the AI era”. A future in which teachers are “not just imparting knowledge”.
[W]e’re cultivating humanity, fostering innovation, and preparing students to excel in a world where “B” is just the beginning.
His reflections, especially in the Lessons Learned and Closing Thoughts sections, have affirmed, challenged, and crystalised my own thinking about AI’s place in education.
While they are reflections that have broad application, I think they have a special applicability for history pedagogy.
His insights certainly align with my research into an agentic, transformative, generative, and reparative, technology-infused history pedagogy and my praxis in using AI with secondary students.
AI as an Iterative Teaching Partner
Hoang states:
The process of using AI to draft instructional materials, followed by human refinement, allows for the creation of more precise and tailored educational content. This iterative approach ensures that instructions are aligned with course goals and pedagogical strategies.”
This strongly resonates with my own classroom strategies. While Hoang’s work relates especially well to BoodleBox in higher education, the experiences he notes are a close match for the wat that I encourage my secondary students to work.
My use of structured AI thinking routines – such as the 2x I Statements and a Verb, the Plus 3, and the Plussy RAG approach – also reflect his iterative processes. These routines guide my students in refining their AI outputs, ensuring an alignment with learning goals.
As I’ve experienced in multiple classes, this approach helps students engage more deeply with content while honing their analytical and evaluative skills.
AI and Flipped Learning: A Natural Pairing
As Hoang states:
Encouraging students to use AI tools in various ways – such as reading materials first and then using AI for clarification, or vice versa – can foster a more flexible and personalized learning experience. This approach supports diverse learning styles and preferences.
This directly mirrors my work with flipped learning, where I provide students with core content (usually in the form of short videos) before class and then use a range of higher-order thinking activities (some infused with AI-driven experiences) to foster deeper learning during lessons.
This strategy differentiates in ways that support the diverse learning preferences of students by following the principles of a universal design for learning approach. It facilitates a high degree of autonomy by students as they engage with material. As such, these types of experiences foster a sense of agency in students.
From Rote Learning to Deep Understanding
One of the most compelling takeaways, for me, from Hoang’s research is the role of AI in shifting the focus from memorisation to deeper conceptual understanding. I discuss this concept and observation in This Teacher’s Journal: Blog Post 9 | March 14, 2025.
To support learning in a context of flipped AI-infused approaches to study, I have made ongoing use of Teams Assignments (including use of Flip and Reflect) as routine and regular ‘checks for understanding’. In this way, my teaching experiences align with his argument for AI-driven formative tools which encourage critical thinking before class discussions. Student submissions of checkpoints and ‘checks for understanding’ keep them accountable to teachers in a flipped AI-infused learning approach. When well-designed, such tools have the ability to play a role in allowing students to self-assess their progress. In this way, checks for understanding and checkpoints empower students to interrogate their own knowledge gaps before stepping into the more public and collaborative learning space that is the face-to-face classroom.
Using AI-Generated Flaws as a Critical Thinking Tool?
Hoang’s discussion of using AI to generate flawed case summaries as a means of strengthening students’ critical evaluation skills is an idea I find particularly interesting.
Using AI to generate flawed case summaries serves as an effective teaching tool to develop students’ critical evaluation skills. This exercise highlights the importance of verifying AI-generated content and understanding its limitations.
I can see potential in such an approach for the history classroom. Perhaps rather than case summaries, history students might engage in activities in which they are to identify from a range of historical sources, those that are AI generated ‘phonies’ This way of working presents an opportunity to develop fact-checking, evaluation, and analysis tasks that challenge students to not only engage with historical sources but to dissect AI-generated outputs, reinforcing the necessity of verification and an appropriate level of scepticism suitable for an age of misinformation and disinformation.
I see potential for incorporating this technique into the Plussy RAG methodology, helping students identify biases, inaccuracies, and missing perspectives in historical narratives.
Custom AI Tutors and Student-Generated RAG Models
Hoang’s discussion of custom AI tutors aligns with my ongoing goal of empowering students to develop their own personalised AI mentors. Rather than relying on commercially developed AI tutors, I envision students conducting research, evaluating sources, and curating their own retrieval-augmented generation (RAG) models.
Imagine the transformative potential of students designing their own tutor bots (perhaps, Copilots, GPTs, or apps via a platform such as PlaylabAI) using my Plussy and RAG approaches – a true exercise in historical agency, digital literacy, and metacognition.
Such a creative experience would build agency in students and make the skill of source evaluation, so highly valued in historical thinking approaches, an authentic experience of learning with a life beyond that of assessment tasks.
The Challenge: AI Raises the Floor, So We Must Raise the Ceiling
Hoang’s closing thoughts capture an essential tension.
We live in a world where AI has democratized competence. Everyone has become a “B” performer across various domains– we’re all B-level writers, artists, coders, linguists, and more, thanks to AI assistance. This new reality poses a significant challenge to traditional education models. If all we expect from our students is to achieve a “B” level of competence, then the value of formal education comes into question. AI can already help anyone reach that “B” standard with relative ease…
This shift has profound implications for the workplace and society at large. As “B” becomes the new baseline, employers and industries are raising their expectations. “A” level performance is now the standard, the bare minimum for standing out and succeeding in a competitive, AI-augmented world.
If education merely aims to achieve competency, its value becomes questionable. If education merely aims to achieve competency, its value becomes questionable. I argue that excellence and “mastery” must now transcend performative assessment scores on content or skill. Mastery now must include ethics, value judgements, working with those things that are most human, the ambiguous, the relational, and the uncertain.
Hoang’s assertion that “A” is the new expected standard resonates deeply with my research on reparative, generative, and transformative history education. It’s not about doing more work – it’s about offering students a richer, more powerful human experience.
“Reframing the Mission” and a “Vision for the Future”
Perhaps the most compelling challenge Hoang poses is the need to reframe our mission as educators.
As he argues, the role of education is no longer just about mastering subject content or domain specific skills – it must be a process in which we focus on developing critical thinking, creativity, and ethical judgement.
We must ensure students leverage AI as a tool for empowerment rather than a crutch for easy answers. Teachers must work to ensure that students collaborate with AI in ways that amplify their unique human insights, abilities, and voice.
His vision of AI-enhanced, human-centred classrooms aligns with my work in transformative history pedagogy, where students interrogate the past to build a more just and equitable future.
Imagine classrooms where AI doesn’t replace human interaction, but enhances it. Picture a learning environment where students and faculty collaborate seamlessly with AI, pushing the boundaries of creativity and critical thinking. This is a future we can create together…
As we embrace this AI revolution, we must do so with a steadfast commitment to equity. We have a responsibility to ensure that every student, regardless of background, has access to these powerful tools and the guidance to use them effectively.
Final Thoughts
Hoang’s reflections have profoundly affirmed and clarified my thinking. In many respect they encapsulate both the transformative potential of AI in education and the challenges we must soon all face as educators.
His case study provides a compelling insight into how we might integrate AI into classrooms in ways that enhance – not replace – human learning.
His distinguished career in public service, military leadership, and education underscores the significance of this work. I extend my deep gratitude to Francis Q. Hoang – not only for his service to education but also for his service to his country and the global community.
Disrupted History 