Camille Faivre is a distinguished authority in education management, specializing in the complex transition to digital and hybrid learning environments. With a background that emphasizes supporting institutions through the post-pandemic landscape, she has become a vital voice for districts navigating the integration of generative artificial intelligence. Her approach moves away from traditional software-driven strategies, focusing instead on the human elements of leadership, psychological safety for staff, and discipline-specific literacy. In this conversation, we explore her blueprint for institutional AI adoption—a method that prioritizes structured departmental engagement and transparent communication over mere procurement.
The discussion covers the psychological barriers facing veteran educators, the shift from school-wide training to small-group mastery, and the necessity of moving beyond binary “cheating” policies. Camille also outlines a specific sequencing for district leaders to follow, ensuring that cultural readiness precedes the selection of any technology platform.
Teachers often worry about appearing less knowledgeable than their students regarding new technology. How can leadership shift institutional culture to alleviate this common fear of “looking stupid”?
It truly begins with a moment of radical honesty from leadership to address the quiet anxiety sitting in every staff room. I recall a specific instance where a mathematics teacher with 22 years of experience finally asked, “What if I look stupid in front of my students?” and the entire room went silent because everyone was harboring that same fear. To solve this, district leaders must shift their language to provide explicit institutional permission for teachers to be learners alongside their students. We worked with around 50 K-12 colleagues across three international schools, and we found that naming this barrier—rather than ignoring it—accelerated teacher uptake significantly by the eight-month mark. When a leader admits they don’t have all the answers, it transforms the classroom from a stage where the teacher must be an infallible expert into a collaborative lab for discovery.
Many districts rely on one-off professional development days that rarely result in long-term behavioral changes. What structure have you found actually leads to durable shifts in how teachers use AI?
The traditional model of whole-school professional development days typically produces very little durable change, with teachers often returning to their old habits just six weeks later. Instead, the strongest behavioral signals come from department-level structured engagement, involving small groups of four to eight teachers working together. We found success in a six-week cycle consisting of four sessions, each lasting precisely 45 minutes and focusing on a single pedagogical question rather than a specific piece of software. Between these meetings, teachers perform one practice task in a live lesson and conclude with a shared observation that is distilled into two paragraphs for the rest of the faculty. By starting with just two willing departments and sharing their internal write-ups, you allow resistant staff to see the benefits and approach leadership when they feel ready, which is a much more effective sequence.
The conversation around AI in schools often focuses on a binary “did they or didn’t they” use the tool. Why is this framing problematic, and how should districts define student competence instead?
The binary framing of AI use—viewing it simply as cheating or not cheating—cannot survive the complexity of a real classroom environment. For instance, a mathematics student using AI to check their logic before submission is engaging in a different cognitive act than a student using it to bypass the work entirely. We advocate for a framework where AI use is treated as a competence within a discipline, with three to five observable criteria tailored to the specific needs of that subject. This doesn’t have to be a long, legalistic process; a head of department can usually draft these criteria and have them signed off by a principal in about 90 minutes. Most importantly, these statements must be written in language a 14-year-old can easily read and understand. When students have clear, discipline-specific guidelines, they are far more likely to self-regulate their use of the technology rather than attempting to circumvent the rules.
In the rush to implement AI, many districts prioritize buying platforms first. Why is this sequencing often a mistake, and what should the order of operations look like?
Most districts begin by evaluating three or four platforms, picking one, and then wondering why teacher engagement remains uneven six months later. The order that consistently works is actually the reverse: you must start with the language the leader uses in faculty meetings, then move to the structure of department-level engagement, and then establish those discipline-specific competence statements. Only after those three pillars are in place should you choose a platform, and that choice should be made in consultation with the department heads who will actually use it, not just an IT committee. A district that gets the platform right but ignores the cultural and structural foundation will get a budget line without the necessary behavior change. If you build the language and structure first, you will get a significant return on whatever platform you eventually decide to procure.
For a district leader who wants to begin this transition immediately without waiting for a new budget cycle, what are the first practical steps they can take?
A leader can start this transformation tomorrow morning without spending a single dollar by simply changing the language they use in their next faculty meeting. Instead of saying, “we will permit AI under these specific conditions,” they should say, “we will learn this alongside our students, and here is what that journey looks like.” You can immediately propose a four-session structured engagement to two of your department heads and offer to personally attend the first session to show your support. Ask one of those department heads to draft just one discipline-specific competence statement in plain language to serve as a template for everyone else. This demonstrates that AI literacy is not a procurement project, but a language and structure project that values the expertise of the staff already in the building. It costs nothing to empower your teachers to move from a place of fear to a place of professional curiosity and shared growth.
What is your forecast for the evolution of AI literacy in K-12 education?
I believe we are moving toward a future where AI literacy will no longer be viewed as a separate technical skill, but as a foundational element of every single academic discipline. We will see a shift where the process of using AI to iterate, critique, and refine ideas becomes just as measurable and valuable as the final product a student turns in. Districts that fail to address the psychological safety of their veteran teachers will likely see a widening gap in instructional quality, while those that embrace a “learn-alongside” model will thrive. Eventually, the focus will move away from the tools themselves and back to impactful instruction, with AI acting as a quiet assistant that frees up teachers to do what they do best: mentor and inspire. The districts that will lead this charge are the ones starting the “language project” today, focusing on human competence rather than just software licenses.
