CBL and AI

By Mark H. Nichols

A Moment of Transformation

Education is once again standing at a crossroads. New forms of intelligent technology are reshaping how we access information, create content, and solve problems. What was once the exclusive domain of experts—designing, composing, coding, analyzing—is now accessible to anyone with curiosity and a device.

This moment is not just another wave of innovation. It is a fundamental shift in what it means to learn and create. When knowledge can be generated instantly, the purpose of education can no longer be simply to deliver facts or train technical proficiency. The question becomes: how do we prepare learners to think critically, act ethically, and design for human good in a world of intelligent tools?

That question is precisely where Challenge Based Learning (CBL) belongs.

Just as CBL emerged in response to the Internet’s transformation of information, it now offers the structure we need to make sense of an age defined by generative capability. It provides a framework for using technology not to replace human learning, but to deepen it—helping learners connect knowledge to purpose, creativity to ethics, and innovation to impact.

A Framework for Human-Centered Learning

The literature on human-centered design (Xu, 2019; Auernhammer, 2020; Shneiderman, 2020) offers a powerful reminder that technology should always begin and end with people. Systems should enhance human capacity, support reflection, and embody values such as transparency, accountability, and trust.

These principles align directly with the foundations of CBL. In both domains, agency, reflection, and purpose define meaningful creation. However, CBL moves beyond design philosophy—it translates these principles into daily learning practice.

Through the CBL cycle—Engage, Investigate, Act—learners encounter authentic challenges, explore multiple perspectives, and take informed action. Each phase cultivates the very qualities the age of intelligent technology demands: curiosity, critical thinking, ethical reasoning, and collaboration.

Human-centered design provides the theory; Challenge Based Learning provides the practice.

Designing for Agency and Purpose

When intelligent systems enter learning environments, the key design question shifts from what technology can do to what learners can do through it.

CBL gives educators a way to answer that question. In the Investigate phase, learners might use AI tools to collect data, simulate outcomes, or visualize possibilities—but the goal is not automation; it is amplification. The technology expands curiosity, enabling learners to test ideas, see patterns, and ask better questions.

Xu (2019) describes this as preserving human interpretive authority. Learners remain the meaning-makers, responsible for evaluating information, understanding context, and connecting insights to human needs.

When CBL is the organizing framework, technology becomes a partner in inquiry rather than a substitute for it. Learners use it to extend thinking, not escape it.

Ethics and the Practice of Learning

CBL has always been about action with integrity—turning learning into something that matters in the world. When intelligent systems become part of that action, ethics moves from abstraction to lived experience.

Within a challenge cycle, learners explore bias, authorship, privacy, and inclusion not as academic topics, but as questions embedded in their work. As Berman and O’Brien (2023) describe, this develops moral imagination—the ability to anticipate the human consequences of design.

Because CBL situates learning within authentic challenges, it becomes a natural space for ethical practice. Learners must balance creativity with responsibility, efficiency with empathy. In this way, CBL does not just prepare students to use technology; it prepares them to shape its direction.

Toward a Challenge Based Future

The convergence of intelligent tools and CBL signals more than a shift in teaching strategy—it represents a redefinition of learning itself.

Where human-centered design theory reminds us that innovation must serve people, Challenge Based Learning operationalizes that idea. It gives educators a repeatable, adaptable process for ensuring that every learning experience—digital or otherwise—remains connected to purpose, agency, and human growth.

As Perna, Recke, and Nichols (2023) emphasize, CBL’s power lies in its adaptability and authenticity. By integrating the insights of human-centered design into the CBL cycle, we can ensure that as our tools become more capable, our learning becomes more humane.

Across Apple Developer Academies and Challenge Institute programs, this future is already visible. Learners are using intelligent systems to prototype solutions for accessibility, sustainability, and social innovation. Yet in every case, the organizing force remains constant: the challenge. Technology serves the challenge, and the challenge serves humanity.

A Second Turning Point

The Internet ended the era of information scarcity. Intelligent systems are ending the era of creative scarcity. Both revolutions call for an education model that values thinking, empathy, and agency over memorization or efficiency.

Challenge Based Learning offers that model. It gives structure to curiosity, channels innovation into ethical action, and ensures that learning remains grounded in the human experience.

Human-centered design offers the theory; CBL turns it into a movement. Together, they define a vision for education in an age of intelligent tools—one where challenges drive learning, reflection fuels growth, and technology amplifies what makes us most human.

As Seligman (2011) reminds us, human flourishing involves living meaningfully, engaging deeply, and contributing productively to one’s world. Those outcomes are not side effects of Challenge Based Learning; they are its purpose.

Notes

AI tools were used in the research, writing and editing of this essay.

References

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Auernhammer, J. (2020). Human-centered AI: The role of human-centered design research in the development of AI. In Proceedings of the Design Research Society Conference 2020 (pp. 1–12). https://doi.org/10.21606/drs.2020.282

Berman, A., & O’Brien, M. (2023). Human-centered artificial intelligence in education. AI & Society, 38(2), 411–427. https://doi.org/10.1007/s00146-022-01446-5

Nichols, M., & Cator, K. (2008). Challenge Based Learning White Paper. Cupertino, California: Apple.

Perna, S., Recke, M. P., & Nichols, M. H. (2023). Challenge Based Learning: A Comprehensive Survey of the Literature. The Challenge Institute. https://www.challengeinstitute.org/CBL_Literature_Survey.pdf

Schön, D. A. (1983). The Reflective Practitioner: How Professionals Think in Action. Basic Books.

Seligman, M. E. P. (2011). Flourish: A Visionary New Understanding of Happiness and Well-Being. Free Press.

Shneiderman, Ben, Human-Centered AI (Oxford, 2022; online edn, Oxford Academic, 17 Feb. 2022), https://doi.org/10.1093/oso/9780192845290.001.0001, accessed 6 Nov. 2025.

Xu, W. (2019). Toward human-centered AI: A perspective from human–computer interaction. Interactions, 26(4), 42–46. https://doi.org/10.1145/3328485