The Double-Helix Logic of Curriculum: Reframing Universality and Personalization in the Age of Artificial Intelligence

Abstract

Purpose

This article examines how education systems should redefine what and how students learn in the age of artificial intelligence (AI). It critiques the persistence of universalist frameworks that prescribe a single profile of the “ideal graduate” and argues for a double-helix logic of curriculum that balances universality with personalization.

Design/Approach/Method

The article synthesizes insights from multiple disciplines and draws on cases from multiple countries. It integrates multiple conceptual frameworks to evaluate the limitations of one-size-fits-all approaches and illustrate a more dynamic and integrated framework.

Findings

Analysis reveals that prevailing reforms continue to reinforce uniformity. Instead, what we need is a curriculum logic that ensures that all students acquire societal and ethical foundations while enabling them to pursue personalizable strengths, passions, and real-world applications.

Originality/Value

This article contributes to the ongoing debate on curriculum design by reframing the tension between universality and personalization as a synergistic relationship. It highlights how the double-helix logic of curriculum can prepare learners not only to adapt to rapid technological and societal change but also to shape inclusive, innovative, and humane futures. By emphasizing both collective foundations and individual flourishing, the framework provides a structural vision of curriculum transformation for the AI era.

The question of what students should learn has been a central concern in educational theory and practice for centuries (Spencer, 1860). Each major technological and social transformation—whether the industrial revolution, the spread of mass literacy, or the emergence of the Internet—has reignited debates over the aims and content of education. Today, the rapid rise of artificial intelligence (AI) poses this question with renewed urgency because of AI's potential to outperform human beings in numerous cognitive tasks. AI is likely to profoundly reshape economies, communication, governance, and culture (Luckin et al., 2022), resulting in significant disruptions in the future labor market (Muro, Maxim et al., 2019; Muro, Whiton et al., 2019) and societal conditions. The challenge, therefore, is no longer only about deciding what knowledge and skills are worth learning. It is about how education can balance universal foundations with personalizable pathways, so that all learners are prepared to thrive amid both unprecedented opportunities and heightened uncertainties (McDiarmid & Zhao, 2022).

What to Learn in the Twenty-first Century: Global Ambitions and the Limits of Universality

In the early twenty-first century, education systems around the world tried to move beyond narrow test scores and traditional subjects. Policymakers and scholars argued that young people needed more than academic knowledge to thrive in a changing world. The U.S. Common Core State Standards called for deeper thinking and application (National Governors Association Center for Best Practices & Council of Chief State School Officers, 2010). Australia added “general capabilities” such as intercultural understanding and ethical reasoning to its curriculum (ACARA, 2022). The European Union emphasized multilingualism, digital literacy, and cultural awareness in its Key Competences for Lifelong Learning (European Commission, 2018). OECD's Global Competence framework (2018) and UNESCO's Global Citizenship Education (2015) aimed to prepare students to live responsibly in an interconnected and sustainable world. Local initiatives such as the U.S. Portrait of a Graduate also tried to combine academic skills with civic engagement and resilience (Battelle for Kids, 2014). Together, these reforms show a genuine ambition: to broaden the answer to “what students should learn.”

But despite these efforts, most of the reforms still share a hidden assumption: All students should become a certain kind of identical “ideal graduate” through a uniform curriculum. Whether described as twenty-first-century skills, global competence, or national competency models, the expectation is often the same set of qualities for everyone. For example, the Common Core sought uniform standards across U.S. states. In Germany, the “PISA shock” pushed the country to focus on measurable competencies, using international comparisons as the benchmark of success (Ertl, 2006). Singapore's twenty-first Century Competencies framework requires every student to show resilience and critical thinking (Tan, 2013). China's Core Competencies for Student Development (Ministry of Education of the People's Republic of China, 2016) and Japan's ikiru chikara (“zest for living”) also outline nationally prescribed qualities (Takayama, 2018; Zhao, 2020). In both Western and East Asian systems, the result is similar: standardized curricula, age-based cohorts, and uniform expectations—what Tyack and Tobin (1994) famously called the “grammar of schooling.”

The problem with this universalist approach is that it ignores how different learners actually are. Research in psychology and education has shown this for decades. Howard Gardner's (1983) theory of multiple intelligences reminds us that people show strengths in very different areas—one student may think logically, another may excel in music, another in social skills. Costa and McCrae's (1992) Big Five personality model shows how motivations and traits vary widely. Deci and Ryan's (1985) self-determination theory highlights how autonomy and interest drive learning differently for each individual. Rose (2016) captured this vividly with the idea of “jagged profiles”: No one is good at everything or weak at everything. A student might be strong in math but struggles in writing or be creative but less confident in memorization. As Heckman and Kautz (2012) note, life circumstances also shape opportunities in deeply unequal ways. In short, there is no single “average learner.” Designing education around an idealized graduate risks overlooking individual talents, discouraging motivation, and reinforcing inequities.

These risks are even sharper in the age of AI. As machines increasingly take over routine and standardized tasks, the qualities that will matter most are those that cannot be automated—creativity, empathy, ethical reasoning, and entrepreneurial imagination (Brynjolfsson & McAfee, 2014; Selwyn, 2019; Zhao, 2018, 2025). A framework that steers all students toward the same outcomes may suppress precisely the diversity of human strength that has become our greatest advantage (Zhao et al., 2022).

Personalizable Learning: Promise and Limitations

This is why universalist frameworks, despite their good intentions, fall short. They remind us that the real challenge is not simply to update the list of skills and competencies, but to rethink how education can value and cultivate diversity.

Over the past two decades, personalized learning has moved from a niche idea to a central focus of education policy and school reform. While its roots can be traced to Dewey's progressive philosophy and Montessori's child-centered approach, advances in technology and workforce demands have accelerated its adoption (Pane et al., 2015; Zhao, 2018). National initiatives in the United States, the United Kingdom, China, and elsewhere have promoted it as a way to increase engagement, close achievement gaps, and prepare students for a global innovation economy (Patrick et al., 2013). Differentiated instruction has also gained renewed attention, adapting content, process, and assessment to diverse needs (Tomlinson, 2014). Digital platforms, adaptive systems, and AI-powered analytics now make it possible to deliver tailored materials, track progress in real time, and generate detailed learning profiles (Holmes et al., 2019). Philanthropic foundations, notably the Bill & Melinda Gates Foundation, have invested heavily in these reforms, reinforcing the belief that moving beyond one-size-fits-all teaching is essential for twenty-first-century education (Pane et al., 2015).

Yet much of what is called “personalized” remains narrow in scope. Many systems still resemble Skinner's teaching machines, adjusting pace or sequence but requiring all students to master the same predetermined content. In practice, this means students may move through a math program at different speeds, but they still solve the same set of problems in the end. As Zhao (2018, 2023) warns, this “process personalization” standardizes under the guise of flexibility and fails to support learners in cultivating distinctive strengths and passions. Rose (2016) calls this the problem of “jagged profiles”: Students excel in some areas while struggling in others, but uniform benchmarks reduce them to averages. This also explains why personalized learning often struggles to close the achievement gap. Students enter with different aptitudes, motivations, and life circumstances (Coleman et al., 1966; Reardon, 2011). Some are overlooked if their strengths fall outside the prescribed curriculum, while others become trapped in remediation cycles that undermine confidence (Zhao, 2018).

Traditional personalized learning, focused on efficiency within rigid curricular boundaries, risks preparing students for a world that no longer exists. What is needed, as Zhao (2018, 2023) and Zhao and Zhong (2024) argue, is a shift from personalization as adaptation to personalizable education: a model in which both goals and content evolve with each learner's profile. In this vision, personalization is not merely a delivery system but a design principle for learning ecosystems where students co-construct their pathways with educators, AI, and communities.

This argument also connects to wider critiques of meritocracy. Systems that enforce uniform benchmarks reward conformity and exacerbate inequality, while a personalizable model empowers learners to design their own trajectories and contribute their distinctive talents to collective flourishing. In this way, personalizable learning, reframed as personalizable education, becomes integral to the Human Interdependence Paradigm (HIP), in which education fosters both individuality and interdependence, preparing learners to thrive amid uncertainty by complementing one another's strengths.

Balancing Personalization and Universality

While personalizable education addresses many shortcomings of traditional personalization, it cannot by itself sustain an education system. Without a shared foundation, individual pathways risk fragmenting learning experiences and undermining social cohesion. Every society requires a set of shared foundations—literacy, numeracy, civic and societal knowledge, digital competence, and ethical reasoning—that enable communication, collaboration, and collective action (Bruner, 1996; Nussbaum, 2010; Pellegrino & Hilton, 2012). International frameworks such as OECD's global competence (2018) and UNESCO's global citizenship education (2015) also highlight the necessity of common ground.

Thus, education must balance the personalizable and the universal. The former ensures that learners’ distinctive excellences emerge; the latter ensures these excellences are legible, sharable, and socially impactful. These dimensions are not opposites but, as Zhao and Zhong (2024) argue, mutually constitutive elements of an educational ecosystem. The true challenge is not to expand standardized lists but to design systems that embrace difference while sustaining common ground. In this sense, the coexistence of personalization and universality becomes the very mechanism by which education, in the AI age, can move from meritocracy toward the HIP (Zhong & Zhao, 2025).

The Double-Helix Logic of Curriculum

This dynamic points toward what we describe as a double-helix logic of curriculum, in which personalization and universality are not opposed but entwined. Borrowing the double-helix metaphor from molecular biology, the model conceptualizes universality and personalization as two intertwined strands of a single helix. In DNA, the discovery of the double-helix structure (Watson & Crick, 1953) revealed that genetic information is stored in two complementary strands. The template strand serves as the pattern for replication and transcription, ensuring stability and fidelity of genetic information, while the coding strand mirrors the sequence of RNA transcripts and represents the visible expression of genetic information (Alberts et al., 2015; Berg et al., 2019). Neither strand functions in isolation; together they form a stable and evolving system.

Analogously, in education, universality functions as the template strand, providing the societal foundation—shared literacies, civic and societal knowledge, ethical reasoning, and digital competence—that ensures coherence and recognition. Personalization functions as the coding strand, expressing learners’ distinctive interests, strengths, and passions, and making visible the diversity that drives innovation and growth. As in DNA, the two strands are not opposites but mutually dependent, spiraling upward together as the curriculum accumulates experiences across scales.

This double-helix logic resonates with the dynamics of Panarchy theory, which explains change in complex socio-ecological systems through cycles of resilience, adaptability, and transformability, mediated by cross-scale interactions (Allen et al., 2014; Gunderson & Holling, 2002; Holling, 1973; Walker et al., 2004). In traditional education systems, the universal strand has been overemphasized, turning the curriculum into an instrument of standardization and selection, thereby reinforcing a meritocratic logic in which individual value is defined by proximity to a common benchmark (Zhao & Zhong, 2024).

Recent work extends this perspective. Zhong and Zhao (2025) analyze schooling as a spatiotemporal system and identify the curriculum as the “anchor” that links pedagogy, activities, environment, and assessment. Curriculum reflects both external demands (national standards, credentialing mechanisms) and internal variation (learners’ interests and potentials), thereby generating systemic tension. In traditional systems, this tension is compressed into external conformity; in innovative practice, however, curriculum can be reimagined as a generator of micro learning spaces in which learners’ differences are expressed and accumulated through projects, inquiries, and collaborations. As these differences accumulate, they feed upward, propelling the system into the reorganization phase of the Panarchy cycle and enabling transformation.

For example, the Innovation, Creativity, and Entrepreneurship Education (ICEE) program at Chongqing No. 8 Secondary School in China designs each unit as a “micro-space” where learners express and accumulate differences. Over time, these differences converge, producing the energy necessary to transform the broader system. In this way, education shifts from a competitive, meritocratic logic to a double-helix logic—what Zhao and Zhong call the HIP. In this paradigm, universality remains as a societal foundation but no longer functions as a tool of suppression; instead, it supports the articulation of difference. Personalizable learning, in turn, intertwines with universality, fostering collaboration and complementarity in diversity. In this way, the curriculum becomes not merely a set of prescribed outcomes but a blueprint of experiences that mediates between external societal demands and internal learner variation, operating as the mechanism through which education evolves—via Panarchy's adaptive cycles—from meritocracy toward human interdependence.

Designing for Integration: From Logic to Practice

Balancing universality and personalization is not only a pedagogical choice but also an ethical and societal imperative in the age of AI. Schools must be designed as ecosystems that honor both collective responsibilities and individual journeys of self-discovery.

One way to illustrate this balance is through flexible time-allocation frameworks. A commonly cited heuristic is the “1/3–1/3–1/3” structure: one-third of time devoted to universal learning (literacy, numeracy, civic and societal knowledge, and digital competence), one-third to school-guided personalization (educator-designed explorations and interdisciplinary inquiry), and one-third to student-directed learning (autonomous projects supported by AI tools and mentors). The purpose of this framework is not to prescribe fixed ratios but to remind us that all three domains deserve protected space. Schools at different levels may adjust these proportions—early years allocating more to universal foundations, high schools shifting toward student-led inquiry—but the principle remains the same: Integration requires time for both shared foundations and diverse pathways.

The ICEE program offers a concrete example of how such integration can take shape within existing school structures. Rather than dismantling the system, ICEE created “micro-spaces”—subsystems embedded in the traditional timetable, functioning as a school within a school. Each unit is anchored in thematic modules and organized through task chains, combining universal competencies with personalizable exploration. For instance, under the theme “Taking Chongqing to the World,” some students built digital tools while others staged performances; both addressed civic and cultural questions while allowing different talents to emerge. Teachers, meanwhile, shifted roles from knowledge transmitters to designers, co-learners, and mentors, supported by AI-enabled evaluation tools that generate dynamic growth profiles. Over time, these micro-spaces expanded from pilot classes to a broader ecosystem, demonstrating how differences accumulate, converge, and generate transformative energy within the larger system.

Other structural strategies can also support integration. Zhao and Zhong describe the “school within a school” model as a promising pathway: dedicated hubs for personalizable projects operate alongside conventional classrooms, ensuring that individual growth builds on a stable foundation. Additional strategies include:

Curriculum Design—Keep universal learning “lean but deep,” focusing on transferable skills and enduring understandings rather than exhaustive content coverage.

Mentorship Models—Pair students with faculty or community mentors to connect personal projects to real-world contexts and societal needs.

AI-Enhanced Guidance—Use AI to monitor progress in both strands, provide adaptive scaffolding, and identify opportunities for cross-pollination between universal and personal learning.

Integrated Assessment—Combine scenario-based evaluations for universal competencies with portfolio-based assessments for personalizable achievements.

In short, integration does not mean choosing between universality and personalization, nor does it mean imposing rigid templates. It means designing dynamic ecosystems where both strands coexist as mutually reinforcing elements—much like the two strands of DNA. If realized, such designs can prepare young people not only to adapt to rapid technological and societal change but also to shape futures that are inclusive, innovative, and humane.

Conclusion

The arguments advanced in this article underscore the urgency of rethinking curriculum in the age of AI. If education continues to pursue a single profile of the “ideal graduate,” whether framed as standardized academic outcomes or universalized competencies, it risks both stifling individual potential and failing to prepare societies for an uncertain future.

The double-helix logic of curriculum offers a generative response. By conceptualizing universality as the template strand—ensuring societal foundations of literacy, civic and societal knowledge, and ethical responsibility—and personalization as the coding strand—expressing learners’ distinctive strengths and passions—the model reframes curriculum as a blueprint of experiences rather than a checklist of content. In this way, the model situates curriculum as a mechanism that mediates between external societal demands and internal learner variation, and, through the adaptive cycles of Panarchy theory, becomes the driver of systemic evolution—from conformity and meritocratic selection to the HIP (Zhong & Zhao, 2025).

The implications of this model are significant. For policymakers, the double-helix logic suggests that the challenge is not to expand ever-longer lists of universal competencies but to articulate flexible frameworks: preserving broad societal and ethical foundations while creating policy space for schools to design personalizable opportunities. For practitioners, the model calls for the deliberate construction of ecosystems where AI is not used to standardize instruction but to amplify student agency, guide diverse trajectories, and connect learners to communities of knowledge. This requires innovative structures such as “school within a school,” flexible time-budget frameworks, and the integration of authentic assessment practices. For researchers, the model highlights the need to move beyond studying “average effects” and instead focus on how different learners flourish under different conditions—treating variability not as error but as a fundamental resource for systemic resilience and innovation.

Ultimately, the double-helix logic of curriculum is not a metaphorical suggestion but a structural theory of curriculum. It posits that universality and personalization must spiral upward together, each strand reinforcing the other across scales and over time. By embracing this intertwined logic, curriculum becomes the evolutionary engine of education, enabling it to move beyond its industrial past, break free from meritocratic constraints, and guide the transition toward a future defined by human interdependence in an AI-mediated world.