The McDonaldisation of higher education in the age of AI

In 1993, sociologist George Ritzer introduced the concept of McDonaldisation to describe how principles like efficiency, calculability, predictability, and control, derived from the fast-food industry, permeate other sectors of society.

While these principles often streamline operations and increase scalability, they risk overshadowing qualitative aspects of human experience.

Today, with artificial intelligence revolutionising industries at an unprecedented pace, a critical question arises: does AI represent a new form of McDonaldisation in higher education?

This question is particularly urgent for higher education, traditionally a space for intellectual exploration, creativity, and personal growth. AI systems promise efficiency and innovation, but they also raise concerns about standardisation, equity, and the erosion of relational dynamics that define meaningful learning.

This article examines how AI aligns with McDonaldisation principles, the opportunities and challenges it introduces, and how institutions are both embracing and resisting its influence.

McDonaldisation framework and AI in HE

Ritzer’s McDonaldisation framework, ECPC – efficiency, calculability, predictability, and control – is increasingly relevant in the era of AI, which is profoundly shaping teaching, learning, and administration in higher education:

• Efficiency: Tools like Gradescope automate grading, reducing instructor workloads, while adaptive platforms like Blackboard Learn and Coursera personalise content delivery to save time and resources.

• Calculability: AI-driven analytics allow universities to measure outcomes such as retention and productivity, while AI-powered admissions systems emphasise metrics to identify ‘ideal’ candidates.

• Predictability: Platforms like edX and Coursera ensure consistent course delivery on a global scale, though this often marginalises diverse perspectives in favour of uniformity.

• Control: Tools like Proctorio and Respondus Monitor enforce academic integrity through surveillance technologies, raising ethical concerns about privacy and equity.

These applications enhance scalability and precision but often prioritise operational efficiency over the relational and critical aspects of education, revealing the inherent tensions of AI-driven McDonaldisation.

AI technologies as modern McDonaldisation

AI technologies reflect Ritzer’s principles of ECPC, offering transformative capabilities while presenting significant challenges.

They bring unprecedented efficiency and scalability but risk compromising creativity, diversity, and personal connections essential to meaningful learning. This duality necessitates a critical examination of how AI reshapes the core values of education.

Here are examples of AI-driven McDonaldisation:

• Adaptive learning systems

Platforms like Smart Sparrow and DreamBox analyse student performance to deliver tailored content, optimising efficiency and reinforcing calculability by systematically tracking progress and learning outcomes.

• Algorithmic Decision-Making

AI simplifies administrative processes such as course scheduling and resource allocation, ensuring predictable, data-driven outcomes. However, reliance on algorithms risks reducing complex decisions to inflexible frameworks, limiting human judgement and adaptability.

• Generative AI

Tools like ChatGPT and Jasper AI support communication and content creation with instant responses and customised materials. While streamlining these tasks, they risk diminishing intellectual depth, critical thinking, and the mentoring relationships that define transformative learning.

Trade-offs in AI integration

The integration of artificial intelligence into higher education presents a series of complex trade-offs that challenge the foundational principles of teaching and learning. One of the most pressing concerns is the over-reliance on metrics in assessing educational outcomes.

AI systems often prioritise measurable data, such as test scores and completion rates, at the expense of intangible yet crucial attributes like creativity, ethical reasoning, and interpersonal skills.

This narrow focus risks reducing education to a transactional process, neglecting its broader mission of cultivating well-rounded individuals capable of navigating complex societal challenges.

By emphasising quantifiable metrics, institutions may inadvertently devalue the unique, intangible aspects of human learning that are essential for fostering innovation and holistic development.

Another critical issue is the erosion of relational dynamics within the academic environment. Automation, while efficient, threatens to undermine the personal connections between educators and students that are fundamental for fostering intellectual growth and critical inquiry.

These relationships, built on meaningful dialogue and mentorship, often serve as the cornerstone of transformative education. Without them, the learning experience risks becoming impersonal and detached, stripping education of its ability to engage students deeply with ideas and nurture their critical thinking skills.

The ethical implications of AI adoption in higher education further complicate the picture.

The increasing use of surveillance tools to monitor student behaviour and ensure compliance raises serious concerns about privacy, equity, and the potential dehumanisation of the learning experience.

Such technologies may enhance efficiency and security, but they also risk fostering an environment of mistrust and eroding the agency of both students and educators.

This tension highlights the need to carefully weigh the benefits of AI-driven efficiency against the ethical imperatives that underpin a humane and equitable education system.

While we all recognise that AI integration offers significant opportunities, it also presents profound challenges. Addressing these trade-offs requires a thoughtful balance between leveraging technological advancements and preserving the relational, creative, and ethical dimensions that make education transformative.

Ensuring that AI enhances rather than detracts from the educational experience will depend on deliberate and balanced implementation strategies, guided by a clear commitment to the holistic development of learners.

Finding balance in an AI-driven era

Higher education thrives on originality and the nuanced exchange of ideas, which often clash with AI’s focus on automation and standardisation. Without a thoughtful, critical approach, AI risks deepening this trend, eroding the humanistic essence of education and undermining its transformative purpose.

To navigate these challenges, institutions must adopt a balanced approach to AI, leveraging it to enhance efficiency, personalise learning, and promote equity while safeguarding creativity, critical thinking, and human connection.

Addressing these issues requires a clear understanding of how McDonaldisation’s dimensions – ECPC – are transforming higher education. A critical examination of these dynamics helps identify AI’s opportunities and limitations, ensuring its integration supports education's transformative mission.

Efficiency: Streamlining education

AI excels at streamlining educational processes, particularly those traditionally labour-intensive. Automated grading for large classes, for instance, Gradescope, has been reported to reduce grading time by 30% to 60%, enabling educators to focus more on other responsibilities (Panopto, 2023 and Axon Park).

Similarly, AI-powered learning management systems (LMS) like Blackboard and Moodle provide personalised pathways by identifying knowledge gaps and recommending tailored resources, allowing students to learn more efficiently and independently.

While these tools enhance efficiency, they often come at the expense of deeper engagement. Automated grading systems can streamline evaluation but fail to provide the nuanced, personalised feedback that fosters meaningful student-instructor connections.

For instance, research published in the International Journal of Educational Technology in Higher Education found that students rated AI-generated feedback lower than human feedback, particularly after being informed of its AI origin.

This paradox underscores a critical issue: education is not merely transactional but deeply relational, rooted in dialogue and mentorship. Human interaction fosters critical thinking and intellectual growth – qualities that efficiency-focused AI systems struggle to replicate.

The trade-offs of efficiency reflect a broader tension in higher education.

As institutions streamline processes, they risk eroding the relational depth that defines meaningful learning experiences.

This tension aligns with Ritzer’s principle of calculability, where measurable outcomes increasingly dictate educational success, often at the expense of intangible aspects such as creativity and emotional connection.

Calculability: The metrics-driven mindset

Higher education is increasingly turning to AI-driven data analytics to assess success in areas such as enrolment, curriculum design, and institutional rankings.

While these metrics offer valuable insights, they often oversimplify complex educational phenomena by reducing them to quantifiable data points – a practice known as reductionism, a term popularised by Ernest Nagel in his 1961 book The Structure of Science.

Reductionism in motion

This reliance on quantifiable outcomes can neglect essential qualities such as creativity, ethical reasoning, and emotional intelligence.

For example, the American Educational Research Association (2023) found that AI-driven student success prediction models, when trained on historical data, can perpetuate existing biases, potentially disadvantaging under-represented groups and undermining equity efforts.

Similarly, AI-driven standardised assessments focus on measurable skills like test performance while overlooking intangible attributes, risking intellectual conformity and stifling diversity.

To address these challenges, institutions must balance quantitative metrics with qualitative evaluations like reflective assessments, peer reviews, and project-based learning to preserve the richness and diversity of education.

While calculability prioritises measurable outcomes, its reductionist approach often aligns with the McDonaldisation principle of predictability, risking stifling diversity and creativity.

By ensuring that efficiency does not overshadow critical thinking or hinder the broader development of human potential, institutions can integrate AI responsibly while upholding the transformative mission of education.

Predictability: Standardising learning and its consequences

AI’s strength lies in its ability to deliver predictable, standardised learning experiences.

Platforms like Coursera and edX exemplify this by providing consistent content to millions of learners globally. Adaptive learning systems further ensure that students follow structured pathways aligned with their proficiency levels.

While standardisation in education has expanded access, it also risks homogenising learning experiences. Content on global platforms, such as MOOCs, often reflects dominant cultural perspectives, marginalising diverse voices and perspectives.

Despujol et al (2022) reveal the dominance of Global North MOOC providers, with nearly 17,000 courses from about 1,000 universities, largely concentrated in Western institutions. This centralisation reinforces Western-centric perspectives, marginalising non-Western knowledge systems.

Standardised AI-driven assessments often favour conformity over creativity, reflecting McDonaldisation’s emphasis on uniformity. To foster innovation and critical inquiry, higher education must balance scalable AI tools with inclusive content and diverse pedagogical approaches to mitigate homogenisation risks.

Control: AI is shaping educational environments

AI exerts control most visibly through surveillance technologies such as Proctorio and Respondus Monitor, which use facial recognition and behavioural analysis to uphold academic integrity in online exams.

While these systems aim to ensure fairness and rigour, they raise critical questions about trust versus surveillance. Ethical concerns have surfaced, highlighting biases against neurodiverse and culturally diverse students (Electronic Frontier Foundation, 2021).

These tools risk eroding trust and spotlight the broader challenge of integrating AI into education responsibly. Institutions must navigate these issues, balancing AI’s transformative potential with the need to protect equity and foster trust.

Higher education’s embrace of AI

In response to these complexities, many higher education institutions are choosing to see AI as a transformative opportunity rather than merely a challenge.

Universities are actively integrating AI into their curricula, not only to equip students with technical skills but also to instil a sense of ethical responsibility.

For instance, courses on AI ethics and applications have become integral components of computer science and business programmes, aiming to prepare graduates to thrive – and lead – within an AI-driven economy.

AI is also revolutionising research. Machine learning algorithms enable scholars to analyse complex datasets, from genomic sequences to historical texts, uncovering insights that would be impossible for humans to discern alone.

Additionally, AI-powered virtual assistants, such as chatbots, enhance student services by providing instant support for administrative queries.

These innovations demonstrate how AI can enhance educational experiences when implemented thoughtfully. However, they also underscore the need for a balanced approach that prioritises human agency and ethical considerations.

While many institutions embrace AI-driven McDonaldisation, others actively resist its dehumanising aspects by prioritising human interaction, creativity, and personalised learning experiences. These efforts aim to preserve the core values of education in an increasingly AI-driven world.

Human-centred learning models

Liberal arts colleges such as Amherst College and Williams College exemplify a commitment to small, discussion-based classes that foster critical thinking, interpersonal skills, and intellectual curiosity.

At Amherst College, first-year seminars are designed with an enrolment limit of 15 students to facilitate discussion-based learning, close reading, and critical interpretation of texts, thereby promoting active engagement between students and professors.

Similarly, Williams College emphasises intimate seminar-style learning. For example, their Winter Study programme includes courses that focus on group-led discussions of readings and films, encouraging collaborative learning and in-depth exploration of subjects.

These institutions prioritise small class sizes and interactive learning environments to ensure that students engage deeply with their peers and instructors, thereby nurturing a rich educational experience.

Educators across various disciplines are also adopting project-based learning and experiential education models that stress collaboration and real-world problem-solving over rote memorisation.

For instance, at the University of Michigan, the Center for Academic Innovation develops programmes that enable students to apply theoretical knowledge to address complex societal challenges.

Some institutions are creatively integrating AI into education without compromising humanistic values. For example, the AI + Arts initiative at Stanford University Human Centred Artificial Intelligence (Stanford HAI) explores how AI can enhance, rather than replace, creative practices.

Through interdisciplinary projects, students engage with technology to deepen their understanding of artistic expression while preserving the intellectual exploration central to the humanities.

Challenges to resistance

Despite the promise of innovative approaches to counterbalance the influence of AI in higher education, resistance efforts face significant obstacles that hinder their widespread implementation.

One major challenge is ‘limited funding’.

Human-centred programmes, which emphasise personalised learning and interaction, often require smaller class sizes, specialised faculty, and additional resources.

These elements, while crucial for fostering deep learning and creativity, come with high costs that are difficult to sustain in an era of budget constraints and competing institutional priorities.

Another significant barrier is ‘administrative inertia’.

Large educational institutions often resist change due to entrenched bureaucratic structures and a tendency to adhere to traditional models. This inertia can stifle the adoption of creative and interdisciplinary approaches that are essential for navigating the complexities of an AI-driven educational landscape.

Without bold leadership and a willingness to embrace change, resistance efforts may fail to gain the traction needed to drive meaningful reform.

A third challenge is the ‘pressure to scale’.

The efficiency and scalability of AI-powered solutions frequently clash with the personalised and interactive nature of human-centred education.

Programmes that prioritise relational and creative dimensions are inherently less scalable, creating tension between the demand for cost-effective, uniform solutions and the need for individualised learning experiences that truly engage students.

Overcoming these hurdles requires advocating for human interaction and investing in personalised learning environments that foster mentorship, creativity, and collaboration.

By balancing technological innovation with relational and creative dimensions, higher education can avoid over-reliance on AI and uphold its transformative mission.

The way forward

Championing human-centred approaches allows higher education to resist McDonaldisation’s homogenising effects while embracing AI’s potential, ensuring human connection remains central to education.

In the context of AI, ethical frameworks are emerging to guide its integration into education while preserving human-centric values. The Asilomar AI Principles, developed by the Future of Life Institute, outlines guidelines for transparency, accountability, and equity in AI applications.

Similarly, UNESCO’s Recommendations on the Ethics of Artificial Intelligence emphasise the importance of inclusivity, equity and ethical oversight in the deployment of AI in educational settings.

These frameworks encourage institutions to align AI adoption with humanistic principles, resisting the dehumanising aspects of McDonaldisation while leveraging AI’s strengths to enhance learning experiences.

Reimagining education with AI

Some universities are taking proactive steps to reimagine their missions in the face of AI.

For instance, Stanford HAI fosters interdisciplinary research that integrates AI with the humanities. Initiatives such as its “AI and the Human Condition” course and collaborative projects on historical text analysis demonstrate how technology can deepen our understanding of human experiences rather than replace them.

These efforts illustrate the potential for AI to complement, rather than dominate, educational practices. By focusing on the synergy between technology and humanistic values, institutions can leverage the benefits of AI while safeguarding the relational and creative aspects of education that remain vital to its mission.

Higher education at a crossroads

As AI-driven McDonaldisation reshapes higher education, institutions stand at a critical crossroads. The transformative potential of AI is undeniable, offering the promise of greater accessibility, efficiency, and personalised learning.

However, these benefits are accompanied by significant risks. By prioritising efficiency, calculability, predictability and control, higher education may sacrifice the very values that make it a cornerstone of intellectual and societal development.

This crossroads demands thoughtful choices. Institutions must ask themselves whether they will adopt AI uncritically, focusing solely on its efficiencies, or integrate it in ways that preserve the relational, creative, and human-centred aspects of learning.

Rather than viewing AI as a replacement for human capabilities, higher education should position it as a complement. For example, while AI grading systems can handle repetitive tasks, educators must retain responsibility for providing deeper, qualitative feedback.

Similarly, AI can analyse data to identify at-risk students, but human advisors must lead the conversations that support them.

In an AI-driven world, adaptability and ethical reasoning are paramount. Universities should prepare students for this reality by fostering interdisciplinary skills that blend technical proficiency with creativity and critical thinking.

Programmes like Stanford’s AI + Arts initiative, which explores the intersection of technology and creativity, offer models for how higher education can balance innovation with humanistic values.

Finally, institutions must prioritise the ethical implications of AI.

Frameworks like UNESCO’s AI and Education: Guidance for Policy-Makers emphasise transparency, inclusivity, and fairness in AI integration. By aligning with these principles, universities can ensure that AI enhances rather than undermines their educational mission.

This moment of reckoning is not just about adopting AI – it is about shaping its role to align with the core purpose of higher education: to empower individuals to think critically, act ethically, and contribute meaningfully to society.

Conclusion

In the rapidly evolving landscape of higher education, AI presents both extraordinary opportunities and significant risks.

While its integration can enhance efficiency, scalability, and innovation, it must not come at the cost of equity, creativity, and human connection. Institutions must adopt ethical guidelines to ensure AI complements, rather than replaces, the core values of education.

Actionable steps include prioritising interdisciplinary curricula that blend technical skills with critical and creative thinking, fostering transparency in AI applications, and implementing evaluation systems that combine quantitative and qualitative metrics.

Additionally, policymakers and educators must collaborate to establish frameworks that promote inclusivity, diversity, and equity in AI-driven education.

By embracing AI thoughtfully and responsibly, higher education can harness its transformative potential while safeguarding the relational and intellectual dynamics that make education a cornerstone of human development.

James Yoonil Auh is the dean of the School of IT and Design Convergence Education and the chair of computing and communications engineering at KyungHee Cyber University in South Korea.

This article is a commentary. Commentary articles are the opinion of the author and do not necessarily reflect the views of University World News.