There is no shortage of announcements about artificial intelligence in education. My aim here is to surface what’s meaningful for teachers, leaders, and edtech developers in effort to support you to ask the right questions, take action where it matters, and steer rather than simply react.
Top of Mind This Week: Five Implementation Lessons from the HP Futures 2025 Report
This week I’ve been revisiting HP Futures: AI and the Future of Learning a report I had the privilege of contributing to as a member of the Council on AI & Educators.
The collective Councils brought together a multidisciplinary group of global experts, including educators, policymakers, researchers, nonprofit leaders, and EdTech innovators. Being part of these conversations was a reminder of how much wisdom sits in the lived experiences of teachers and school leaders who are navigating these shifts in real time. Here are four implementation-focused insights that feel especially important for this moment.
- Implementation must start with purpose and principles, not tools
Clear rules on transparency, learner-data ownership, sustainability, and child protection should guide vendors, policymakers, and institutions. - A national AI-readiness baseline is a prerequisite for meaningful policy
Leaders cannot design sound policy without understanding their current state. - Policy can’t be static in a fast-moving AI environment
AI adoption requires “live policy” that is reviewed annually and adjusted as models evolve. - Educator co-design is the difference between uptake and resistance
As a member of the Council on AI & Educators, this was one of the strongest themes across our discussions. Implementation succeeds only when educators are co-designers, not end-users.
In Nigeria we see the launch of a nationwide AI teacher-training initiative.
The national ministry of education in Nigeria has begun rolling out a program to train teachers in AI-enabled pedagogy and classroom practices. This reflects the shift from high-level policy to implementation. For leaders and partners, the key questions will be about infrastructure readiness, digital equity, teacher coaching models and how edtech vendors might align with the national approach.
In Asia, a Chinese university embeds AI-fluency and personalized pathways into higher-ed At Xi’an Jiaotong‑Liverpool University in Suzhou the institution is piloting a “syntegrative education” model which uses AI to personalize student pathways, emphasize leadership, entrepreneurship and AI-fluency. This offers a concrete example of how higher education in Asia is rethinking not just tools but the student-experience. For edtech developers this signals an appetite for platform innovation; for institutional leaders it raises questions about alignment of curriculum, teacher roles and ethics of personalized data.
In the United States: AI in Education Network and new FIPSE competition
The American Institutes for Research (AIR) has announced an AI in Education Network which is a multi-study research collaboration aimed at how AI is used in K-12 instruction, assessment, decision-making and teacher professional learning. The significance: evidence generation is now front and centre. If you are an edtech developer, this means opportunity (and risk) to align to rigorous evidence. The big question is: how will outcomes be measured? How do we pilot in a way that is implementable, scalable and ethical?
Also, this week via a new call under the U.S. Department of Education’s Fund for the Improvement of Postsecondary Education (FIPSE) competition, expanding the use of artificial intelligence is explicitly named as a priority. For higher-ed leaders and vendors this signals that federal funding will increasingly link to AI readiness, responsible integration and alignment with workforce learning. The challenge becomes: how do you ensure that innovation also preserves human judgement, agency and equitable access?
“AI Charter” and Ongoing Investment Signals
A global report recommends that schools and higher-education systems worldwide adopt a mandatory “AI charter” to set guard-rails and foster trust as AI tools proliferate. For leaders this is a timely reminder: the governance, ethics and policy around AI are not optional. Will this charter framework become a non-negotiable part of our overall AI and education strategy?
Also, this week Google LLC announced new commitments in AI and Learning including US$ 30 million in funding for learning projects globally and a forum to convene educators, academics and technologists. For developers of edtech this reinforces that big-tech interest remains strong. Some questions come to mind with this move including how can we engage with these platforms in a way that aligns to our values, ensures local relevance, and avoids dependence on vendor roadmaps?
What does this week signal?
Looking across this news and other sources (and reflecting on what we found in our analysis from our first six months) a few things to make note of that are ongoing.
- AI readiness is advancing where the leadership asks sharper questions about teacher development, learner agency and ethical frameworks, not just tool acquisition.
- Implementation is not just about access but about coaching, ongoing support and fit for purpose in context.
- Global, national and regional policy nudges indicate that simply adopting AI is no longer enough; the how and why are being scrutinized.
- The leap from pilot to scalable practice demands that vendors and institutions anchor in meaningful outcomes, not just novelty.
That’s it for this week (also — in case you missed it, OpenAI launched ChatGPT 5.1 — stay tuned for the review). See you next week!
Remember that the future of education is being shaped now and we need more voices that are informed, thoughtful, and equity-driven. We need informed, thoughtful voices to make meaningful choices. Subscribe to Jody’s newsletter for weekly updates as soon as they’re published at https://jodybritten.medium.com/subscribe
