A Human-First SHAPE Framework in Schools

AI isn't neutral; it can amplify inequalities in schools unless we apply a human-first framework for responsible AI ethics education.

In this episode:

• The SHAPE framework provides critical human-first AI principles for AI ethics education, ensuring AI strengthens human capability and promotes equity in schools.
• Responsible AI teaching requires schools to be 'System-Aware' by auditing their infrastructure and digital literacy before implementing AI, preventing the amplification of existing inequalities.
• Applying the 'Human-Augmenting' principle means using AI to enhance teacher judgment and student connection, not to replace the irreplaceable human element in education.
• An 'Accountability-Driven' approach to AI frameworks education demands rigorous assessment of AI tools for their actual impact on student learning and teacher workload, beyond mere novelty.
• Developing an 'Equity-Centred' AI social impact curriculum means actively designing AI to address disparities and ensure accessibility for all students, making it an equalizer rather than a gap-widener.

Chapters:

• 00:00 — Cold open & welcome
• 00:30 — Zahid Torres-Rahman, Business Fights Poverty, and AI's non-neutrality in education
• 01:25 — Introducing the SHAPE framework for responsible AI teaching
• 02:00 — S: System-Aware – Auditing your school's AI readiness
• 03:45 — H: Human-Augmenting – AI for teacher enhancement, not replacement
• 05:15 — A: Accountability-Driven – Measuring AI's true impact on learning
• 06:45 — P: Partnership-Led – Diverse stakeholders for AI frameworks education
• 08:15 — E: Equity-Centred – Designing an AI social impact curriculum for all
• 09:45 — Recap: Human-first AI principles with the SHAPE framework

How can schools develop an effective AI ethics education program?
Schools can adopt the SHAPE framework to guide their AI ethics education, focusing on being System-Aware, Human-Augmenting, Accountability-Driven, Partnership-Led, and Equity-Centred in their AI strategies.

What are human-first AI principles for educators?
Human-first AI principles, as outlined in the SHAPE framework, advocate for using AI to strengthen human capabilities, promote equity, build accountability, and ensure that technology genuinely improves lives rather than replacing human judgment or exacerbating inequalities.

How can teachers use AI responsibly without widening achievement gaps?
Teachers can use AI responsibly by being 'System-Aware' of their school's context and 'Equity-Centred' in their design, ensuring AI actively addresses existing disparities and provides accessible, differentiated support for all learners rather than just scaling current systems.

Featuring: Dan Fitzpatrick, Zahid Torres-Rahman, Business Fights Poverty, SHAPE framework, Centre for Human-Inspired AI, University of Cambridge, Amarai.tech.

Follow AI in Education with Dan Fitzpatrick for more on AI in education.

Transcript

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If this episode makes you think, please let us know in the comments and support us by subscribing and leaving a review. Thank you. Today we are exploring the critical question of how to apply a truly human first approach to AI, drawing insights from an article titled What Does a Human First AI Enabled Approach to Fighting Poverty Look Like? This piece, published by Business Fights Poverty on July 27, 2026, was co-authored by Zahid Toras Rahman, who is the co-founder and CEO of Business Fights Poverty. What really struck me from the outset is how Zaheed and his team, including insights from the Centre for Human Inspired AI at the University of Cambridge, stressed that while AI offers new ways to do more with less, it's absolutely not neutral. In fact, they state that AI can amplify existing inequalities on a scale we haven't seen before if we don't handle it with care. This immediately got me thinking about our schools because while we're not explicitly fighting poverty in the same way, we are constantly battling educational inequalities, and AI holds the same dual potential for us. Zahid Torres Rahman and his colleagues have spent the last three years grappling with this challenge, and their work culminated in the creation of what they call the Shape Framework. It's designed to help organizations put people before technology when designing and deploying responsible AI. Now I know what you might be thinking. This is about social impact and fighting poverty. How does it apply directly to schools? Well that's exactly where the juice is. These principles for Sigma A IHAI Ethics Education and Uber Responsible AI teaching are profoundly relevant to anyone leading or teaching in a school today, because at its heart the Shape framework is about ensuring that AI strengthens human capability, promotes equity, builds accountability, and genuinely improves lives. So let's break down this Shape framework because I think it offers us a really clear lens for how we integrate AI into our educational settings without losing our way. First up, let's talk about S for Rock System Aware. The framework argues that a human-first approach starts by understanding the system into which AI is being introduced. AI doesn't just drop into a neutral environment, it enters a context shaped by unequal access to connectivity, digital literacy, computing capacity, and even things like data quality and institutional capacity. And here's the kicker. AI tends to scale the system it enters. So it can amplify what's strong, but it can also amplify what's already unequal or fragile. Think about your school for a moment. If you're rolling out a new AI tool, have you really audited the underlying system? What does this mean for a head of department planning a new curriculum using AI tools, for example? It's not just about the tools features, is it? It's about asking, do all my year nine students have reliable access to devices and internet at home to complete AI-assisted homework? Do my colleagues have the digital literacy skills not just to use the tool, but to think with it? And what about the quality of the data we're feeding it or the data it's generating about our students? If there's already an achievement gap or a digital divide in your community, simply deploying AI without addressing those systemic issues could unintentionally make those gaps even wider. It's about starting with why, not how, and ensuring that why includes an honest assessment of your context. The H and shape for on human augmenting really resonates with my core philosophy. The authors from Business Fights Poverty make a powerful statement here. The goal should not be to replace human capability, but to strengthen it. In social impact work, they argue people bring empathy, trust, lived experience, contextual judgment, relationships, and the ability to interpret complexity, qualities AI simply cannot replicate. This is our enhancement not replacement pillar in action, isn't it? It's about recognizing that while AI can help a teacher draft a lesson plan faster or provide differentiated resources at scale, the real magic, the irreplaceable element, is the teacher's judgment, their relationship with the students, their ability to inspire wonder and care. We want to outsource our doing, not our thinking. An AI can generate a thousand quiz questions, but only a human teacher can truly understand why a particular student is struggling, offer that encouraging word, or design a project that genuinely sparks a student's imagination, because it connects to their unique context. It's about letting AI handle the complexity so we have the capacity for creativity and crucially for connection. This is how we ensure we're teaching students not to outsmart machines, but to outthink them. Then we get to A from what accountability driven sooth. The article acknowledges the excitement around new AI models, but wisely states that in social impact, novelty is not the test. Improving outcomes for people is, and this means looking beyond mere efficiency. It means asking about harms, financial costs, long-term effects, and safeguards. For us in education, this means we need to be deeply accountable for the impact of AI on student learning and teacher well-being. Are we just deploying tools because they're new and shiny or because they demonstrably improve student outcomes or give teachers back valuable time? This speaks to our need for rigorous assessment in the AI era. It's not just about the product a student submits, but the process they engaged in, including their interaction logs with AI and their live performance to demonstrate genuine understanding. As school leaders we need to ask, how will we know this AI tool is truly improving learning in a year eight geography lesson, or making a year eleven English teacher's workload more manageable rather than adding to it? And what mechanisms do we have for human review, challenge and correction when AI outputs are used? This ties into our ethical non-negotiables, transparency about AI use and critically human accountability for AI assisted decisions. Next, P4 Sol Partnership led. Zahid Torres Rahman stresses that no single organization has all the answers for AI for social impact. It spans technology, business, public policy, ethics, and community engagement. Each actor sees only part of the picture and progress relies on collaboration. This is such a vital point for schools. Think about who's at your school's AI strategy table. Is it just the tech department and senior leadership? Or have you brought in teachers from across subjects, parents who are concerned or excited, students who are the primary users, and perhaps even local industry partners? Building and Faber Change AI frameworks education. Strategy requires diverse perspectives. It's about giving stakeholders agency and choice, understanding their SPARK motivators, their status, purpose, adventure, risk, and knowledge. We need to be consistent in our communication, show consideration for their concerns, and truly collaborate in co-creating solutions. Because if teachers are often labelled as resistant to change, it's usually because they haven't been given the time, space, and a voice in the process. Give them that, and they become your greatest champions. If you're finding these insights helpful and want more practical advice on integrating AI responsibly into your school, please do follow and subscribe to the podcast wherever you listen. We release new episodes regularly, packed with ideas to spark your imagination. And finally, each are force force at Ariti Chi Equity Centered This is highlighted as perhaps the most important test of all. The article warns against treating communities merely as sources of data rather than holders of knowledge, agency and rights. An equity-centred approach means ensuring affected communities shape the problem definition, data use, design, governance, benefits, and access. For us this means seeing AI not as a way to widen gaps, but as an equalizer. How can AI genuinely serve our multilingual learners, our students with IEPs, or provide differentiation that reaches the often invisible middle eighty percent who need support but aren't always shouting about it? We need to actively design AI enabled learning that addresses existing disparities, rather than passively allowing AI to exacerbate them. It's about designing an AI social impact curriculum. From the ground up that prioritizes accessibility, ensuring it's not an afterthought but a foundation. This means teaching students to be critically aware of AI's limitations and failure modes, understanding potential biases, and asking themselves who benefits from this AI and who might it be leaving behind. It's about enabling all students to manage AI conversations with precision and to develop a reflective awareness about AI's influence. The Shape Framework developed by Zahid Torres Rahman and Business Fights Poverty with input from the Center for Human Inspired AI gives us a powerful set of Yacht human first AI principles for our schools. It's a reminder that the future of education, just like the future of social impact, should not be AI first. It should be human first, AI enabled and shaped by the people whose lives it aims to improve, our students and our educators. The real value of AI in education isn't in what the machine produces, but in how the student and the teacher responds to it. That's all for today. Thanks for listening.