Moving Beyond the Banking Model with Freire's Problem-Posing Approach
As artificial intelligence rapidly transforms our world, Paulo Freire's revolutionary educational philosophy offers a powerful framework for fostering meaningful learning in a "cognified" era—an age in which artificial intelligence is deeply integrated into daily life, reshaping how we think, learn, and interact with the world around us, as described by Kevin Kelly in his book, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future.
In his seminal work, Pedagogy of the Oppressed, Paulo Freire introduces two contrasting educational paradigms: "Banking Education" and "Problem-Posing Education." Today, as artificial intelligence increasingly permeates educational environments, Freire's insights offer valuable guidance for fostering meaningful learning in a "cognified" era—an age in which artificial intelligence is deeply integrated into daily life, reshaping how we think, learn, and interact with the world around us, as described by Kevin Kelly in his book, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future.
This convergence of Freirean pedagogy and modern AI technologies presents both profound challenges and transformative opportunities for education in the 21st century. The question is not whether AI will influence education, but rather how we might purposefully shape its implementation to serve genuinely liberatory educational aims.
Paulo Freire (1921-1997) was a Brazilian educator and philosopher who is best known for his influential work, "Pedagogy of the Oppressed." His educational philosophy emphasized critical consciousness, dialogue, and transformative action for social change. Freire's work has had profound impacts on education, particularly in contexts of social and economic marginalization.
Explore the key differences between Freire's educational models and their implications for AI in education:
Using AI as a source of authoritative answers that students consume without question, similar to feeding facts into a human database.
Using AI as a tool for collaborative inquiry, where students critically question AI outputs and use them as starting points for deeper investigation.
Imagine education as a bank, with students depicted as empty accounts waiting to be filled by teachers who deposit knowledge. In this scenario, students become passive recipients, expected merely to absorb, memorize, and reproduce information without questioning. Freire argues this approach is inherently dehumanizing, stifling critical thinking, creativity, and autonomy.
A similar danger arises in the passive use of AI technologies. When students rely on AI-generated answers as definitive truths without critical scrutiny, they inadvertently participate in a digital form of banking education. They become passive consumers, reducing their active intellectual engagement and inadvertently surrendering their capacity for critical reflection to algorithmic outputs.
Consider a classroom where AI tools are simply banned with declarations of "that's cheating." The teacher positions themselves as the sole legitimate authority on knowledge, viewing AI as a threat rather than an opportunity for critical engagement. When students do use AI outside class, they do so secretively and uncritically, without developing the skills to evaluate its outputs. The banking model persists as students remain passive recipients of teacher-sanctioned information, while simultaneously accepting AI-generated content without question in contexts where it's available to them.
This phenomenon manifests in various concerning practices: students submitting AI-generated essays without meaningful engagement with the content, learners accepting AI explanations without probing underlying assumptions, or educators deploying AI-powered adaptive learning systems that prioritize standardized knowledge transmission over critical inquiry. In these scenarios, AI inadvertently reinforces the very banking model that Freire critiqued as oppressive.
Move the slider to see how different approaches to AI in education align with Freire's educational models.
Conversely, Freire's alternative—the Problem-Posing model—positions students and educators as co-investigators engaging actively with real-world problems. Here, students critically explore, question, and collaboratively construct knowledge. The educator's role transforms into that of facilitator and dialogue partner rather than a mere transmitter of predetermined facts.
Imagine a classroom where students use AI to generate multiple perspectives on a controversial historical event. The teacher guides students in analyzing the differing AI outputs, identifying potential biases, questioning the assumptions, and developing their own critically informed understanding of the historical complexities. Students become knowledge investigators rather than knowledge recipients.
Incorporating AI through a problem-posing approach involves coaching and metacognitive engagement. Students actively use AI as a tool for inquiry rather than a passive information source. They pose challenging questions, interrogate assumptions underlying AI responses, and critically analyze its outputs. This interaction empowers students, enhancing their critical consciousness and promoting active knowledge construction.
Rather than simply accepting an AI-generated summary of a complex social issue, students might interrogate the summary's framing, identify potential biases, and use the AI as a dialogue partner to explore alternative perspectives. Similarly, a teacher might guide students to use AI to generate multiple competing analyses of historical events, then facilitate critical discussions about the strengths and limitations of each interpretation.
Freire emphasizes dialogue as central to liberatory education—genuine, respectful exchanges where both parties learn and grow. In his vision, authentic dialogue requires mutual trust, humility, hope, and critical thinking. How might we reimagine this dialogical relationship in contexts that include AI systems?
While AI cannot truly participate in dialogue as Freire envisioned (lacking consciousness and authentic intentionality), it can function as a powerful mediating tool for human dialogical engagement. When thoughtfully integrated, AI can expand the dialogical space by introducing new perspectives, challenging human interlocutors to clarify their thinking, and making visible the gaps and connections in collective knowledge construction.
The key lies in maintaining human agency within this expanded dialogical field. Students and educators must remain the primary meaning-makers, using AI as an extension of their inquiry rather than surrendering their epistemic authority. As Freire writes, "Knowledge emerges only through invention and re-invention, through the restless, impatient, continuing, hopeful inquiry human beings pursue in the world, with the world, and with each other."
Click on each box to see details about the role in dialogical education. Each box will open a detailed explanation dialog.
Facilitates inquiry
Actively engages
Guides critical engagement with both content and AI
Provides resources, challenges, and expanded perspectives
Critically engages with both human and AI interlocutors
Freire's concept of praxis—the integration of reflection and action upon the world to transform it—provides another vital framework for AI integration in education. In a problem-posing approach, engagement with AI should ultimately connect to meaningful praxis.
This might involve students using AI to help analyze community challenges, develop potential interventions, critically evaluate proposed solutions, and refine their approaches based on ongoing reflection. Throughout this process, AI serves not merely as an information resource but as a tool for amplifying human capacity for transformative action.
For Freire, praxis refers to the cycle of action and reflection that leads to meaningful transformation. It's not enough to simply understand the world theoretically—true education must connect to practical action to change reality, followed by reflection on that action.
A high school environmental science class might use AI to analyze local pollution data, model potential remediation strategies, and craft evidence-based proposals for community action. The technology becomes a vehicle for praxis rather than a substitute for it.
Students begin by reflecting on their community or world, identifying issues that matter to them. AI can help expand their awareness by providing multiple perspectives and relevant data.
Students use AI to gather diverse perspectives on water quality issues in their community, including scientific, economic, and social justice dimensions.
Students investigate the root causes and interconnections of the identified issues. AI assists by analyzing complex data sets and revealing patterns that might otherwise be missed.
AI helps students analyze water quality data across different neighborhoods, revealing correlations with income levels and historical industrial development.
Based on their analysis, students develop plans for intervention. AI can help evaluate different approaches and anticipate potential outcomes or challenges.
Students use AI to model the potential impact of different water quality intervention strategies, considering factors like cost, implementation time, and projected outcomes.
Students put their plans into action, engaging with real-world contexts. AI can support documentation, coordination, and adaptation during this phase.
During community water testing events, students use AI to process test results in real-time and adjust their outreach strategy based on emerging patterns.
Students reflect on their actions, evaluate outcomes, and consider next steps. AI helps analyze impact data and identify areas for improvement in future cycles.
AI helps students analyze community feedback, water quality changes, and policy responses to evaluate their campaign's effectiveness and plan next steps.
For Freire's vision to manifest in AI-enhanced educational settings, educators need specialized preparation that goes beyond technical training. They require opportunities to develop what might be called "critical AI pedagogy"—an approach that integrates technical fluency with critical consciousness about AI's implications and applications in educational contexts.
A critical AI pedagogy requires thoughtful preparation of educators with both technical knowledge and critical educational theory. While research in this area is still emerging, educators would benefit from professional development that addresses both the practical aspects of AI systems and their social, ethical, and pedagogical implications.
Problem-posing engagement with AI aligns with Freire's vision of humanizing and liberatory education. It positions students as empowered knowledge creators rather than passive consumers. Through reflective dialogues with AI, students cultivate deeper insights into social, political, and economic complexities, ultimately preparing them to engage proactively with broader societal issues.
This approach recognizes that education in a cognified world must prioritize distinctly human capacities: ethical reasoning, creative problem-solving, collaborative meaning-making, and critical consciousness. By emphasizing these uniquely human domains, problem-posing education with AI can foster what philosopher Martha Nussbaum calls "capabilities"—the substantive freedoms that allow people to lead lives they have reason to value.
Compare the potential learning outcomes of different approaches to AI integration in education:
Freire's pedagogy is fundamentally concerned with exposing and challenging structural inequities. As we integrate AI into educational contexts, we must critically examine how these technologies might reproduce or amplify existing inequities in educational systems.
Questions of access, representation in training data, algorithmic bias, and the political economy of educational technology all demand careful consideration through a Freirean lens. Whose knowledge is privileged in AI systems? Whose experiences are marginalized? How might AI be deployed to challenge rather than reinforce existing power structures in education?
A truly Freirean approach to AI in education would prioritize the needs and perspectives of marginalized communities, seeking to ensure that these technologies serve genuinely liberatory purposes rather than reproducing historical patterns of exclusion and oppression.
Students might analyze the training data used in educational AI systems to identify whose voices are represented or excluded, then work collectively to advocate for more inclusive technologies. This process itself becomes an exercise in critical consciousness—helping students understand how technologies can encode and perpetuate social hierarchies.
In a rapidly cognified world, educators have the opportunity—and responsibility—to guide students toward meaningful, empowering interactions with AI. By embracing Freire's problem-posing framework, education can leverage AI as a tool not just for information, but for profound personal and social transformation.
This approach demands vigilance against the tendency to slip into new forms of banking education with sophisticated technological veneer. It requires ongoing critical reflection on how AI shapes educational experiences and relationships. Most importantly, it calls us to remember that the ultimate purpose of education, as Freire reminds us, is humanization—the ongoing process of becoming more fully human through critical engagement with the world.
AI should be positioned as a tool for inquiry rather than a source of definitive knowledge. Students should be encouraged to question, analyze, and critically evaluate AI outputs.
Problem-posing education with AI cultivates deeper metacognitive skills and critical thinking, helping students evaluate information from multiple sources.
AI should enhance rather than replace human dialogue, supporting collaborative knowledge construction between teachers and students.
By thoughtfully integrating AI within a problem-posing framework, we can work toward an educational future that embraces technological advancement while remaining firmly grounded in the humanistic values at the heart of truly liberatory education.