Education
Don’t let AI in schools become the next e-tolls debacle
Rennie Naidoo, professor in Information Systems at the Wits School of Business Sciences.
In a dusty classroom in the Eastern Cape, a teacher with 50 learners and no assistant juggles maths, discipline and hope. It’s no wonder artificial intelligence (AI) sounds like salvation.
In South Africa’s ongoing battle to improve education quality, especially in STEM, it’s tempting to look toward technological solutions. Our classrooms are overcrowded, under-resourced and unevenly distributed in quality. AI, with its promise of smart analytics and personalised learning, seems like a modern-day lifeline.
But before we welcome AI surveillance into our schools, we must ask: What kind of education are we designing − and at what cost?
Let’s learn from China’s AI-equipped classrooms, where facial recognition cameras, emotion-reading algorithms and brainwave-monitoring headbands have turned students into data points.
Despite being wrapped in the rhetoric of progress and efficiency, these technologies operate less like tutors and more like digital wardens. And the results are as dystopian as they sound. If we’re not careful, South Africa could sleepwalk into a similar paradigm, especially as our growing tech partnerships often come with a quiet surrender of policy autonomy to powerful global allies.
Before we welcome AI surveillance into our schools, we must ask: What kind of education are we designing − and at what cost?
Just look at how the e-tolls saga unfolded: imported solutions, limited consultation and a public left footing the bill for decisions they never truly owned. And now, as our politicians maintain an increasingly cosy digital relationship with China, the worry is that we won’t just adopt their technology − we’ll absorb their values too: ones that prioritise control over consent.
This is not a debate about whether AI can assist learning. Yes, it can. It’s about the use of AI to control learning, to surveil, discipline and normalise children through silent, ever-watching algorithms. A promise of better marks that undermines the very soul of education: curiosity, autonomy, dignity and the slow, human work of deliberate practice.
The classroom as a panopticon
French philosopher Michel Foucault warned that modern institutions such as schools and prisons use surveillance to shape individuals into ‘docile bodies’, compliant, conforming and self-policing.
Some classrooms in China have taken this literally. Students sit beneath AI cameras that scan their faces every 30 seconds. Algorithms judge their expressions: bored, happy, confused? An AI scoreboard tallies their attention. Deviate from the norm, and a warning is issued, sometimes to the teacher, sometimes to parents.
The result? Students learn to fake smiles, suppress emotion and feign engagement. They become actors in a play written by code. The spontaneous giggle, the curious side question and the thoughtful daydream – hallmarks of real learning − are lost in the noise of digital conformity.
Advocates of classroom AI point to its benefits: faster attendance tracking, data to support struggling learners and real-time feedback. All helpful if the intent is to make classrooms more efficient.
But education is not a manufacturing process. It’s not about producing obedient, test-ready graduates. It’s about nurturing independent thinkers who can ask hard questions – about science, society, technology and themselves.
Here, Foucault’s idea of “normalising judgement” comes into play. Technology begins to define what is acceptable, not based on pedagogy, but on what is easiest to quantify. AI surveillance risks turning our classrooms into compliance factories.
Imagine an AI misinterpreting a shy learner’s gaze as disengagement or labelling a neurodiverse student as inattentive. These systems encode narrow models of “normal” behaviour, models that may not reflect South Africa’s rich diversity of learners.
If China’s model of AI in education evokes Orwell’s 1984, a society watched into submission, then the American variant brings to mind Aldous Huxley’s Brave New World, where learners aren’t surveilled into fear, but nudged into passive distraction.
In the US, classroom tech often arrives in friendlier packaging: gamified apps, digital learning environments and AI tutors. But beneath the glossy UX lies a quieter risk, not of being watched too much, but of thinking too little. Of outsourcing curiosity to content recommendation engines. Of training children not to comply, but to consume.
One is a model of discipline through data, while the other is a model of distraction through dopamine. Neither prioritises the kind of critical human development South Africa so urgently needs. We must be cautious not to import either extreme.
Our children deserve more than a choice between control and commodification. They deserve education that nurtures self-belief and curiosity, not through coercion or convenience, but through care.
Education needs trust, not tracking
Education thrives on trust between the teacher and student, the parent and the school, and between the learner and their own sense of purpose and possibility. AI surveillance corrodes that trust.
When every glance is recorded, every yawn flagged, learners begin to perform for the system, not for their own growth. Teachers, too, may self-censor, teaching to what the algorithm rewards rather than what sparks inquiry.
In a country still healing from generations of authoritarian control, importing systems that automate obedience feels dangerously tone-deaf. Moreover, constant monitoring can erode mental health. Some Chinese students have reported stress, paranoia and a sense of being “boxed in”. Must we add this burden to learners, who already contend with trauma, poverty and structural inequality?
Even in China, resistance has emerged. Students have unplugged cameras during exams. Parents pushed back against brainwave-monitoring headbands. Teachers quietly ignored AI alerts. These acts echo a vital truth: education is largely a human process. It cannot be fully automated. Nor should it be.
As Brazilian educator and philosopher Paulo Freire, a pioneer of critical pedagogy, warned, education either functions as an instrument to bring about conformity or freedom.
China’s AI-heavy classrooms represent the former, where data governs behaviour and dissent disappears under algorithmic discipline. In contrast, the US model seduces learners into passivity through gamified apps and dopamine-driven edtech. In both cases, the learner is no longer a thinker, just a user or a subject. Freire would remind us that real education is not about managing attention but awakening it.
Freire also argued that, in oppressive systems, even those who were once oppressed can internalise the logic of domination. When teachers begin to see AI surveillance not as a threat, but as a tool for efficiency and discipline, they risk reproducing the very values that once silenced them.
The teacher, once a guide for critical thought, becomes a technician of behavioural compliance. The school, once imagined as a space of liberation, becomes a testing ground for algorithmic authority.
In adopting the tools of control, education ceases to be an act of freedom and becomes an instrument of quiet submission.
The path to human-centred AI
We can choose how we build systems. We can choose to reduce harm rather than normalise it. South Africa can use AI ethically, responsibly and humanely.
As we navigate the promises and perils of AI in education, we must stay clear-eyed about power, ethics and intent. In an age of smart technology, the wisest move may be to remain sceptical of systems that promise control but resist accountability.
If we are to build a just education system, we must resist the temptation to outsource decisions to those driven by profit, prestige, or political survival instead of listening to the real needs of learners and educators on the ground.
The goal of education in South Africa should be to unlock potential, not surveil it into silence.
Let’s invest in reliable infrastructure, teacher training and context-sensitive curricula. Let’s adopt tools that genuinely assist teachers: adaptive platforms, early-warning analytics and AI that expands access, not restricts behaviour.
Social media is rapidly eroding our ability to focus and reflect. Its instant feedback and dopamine loops discourage the discomfort essential to real learning. Platforms built to maximise engagement teach young minds to chase likes, not understanding, to skim, not grapple. If schools now adopt a similar logic of tracking, rewarding and nudging, deliberate practice won’t just be neglected. It’ll be unrecognisable.
Progress isn’t just about what technology can do − it’s about what we choose to do with it. The question is not whether AI belongs in education. It’s whether our children belong in systems designed to watch rather than teach. That’s a debate worth having.
Let’s remember: children don’t learn through monitoring. They learn through struggle, reflection and deliberate practice − the kind of learning no algorithm can automate. True learning requires deliberate practice, not digital obedience.
Be sceptical of both the Chinese model that disciplines through data and the US model that distracts through dopamine. Both disempower learners by denying them agency and critical thought, which is the essence of education.
South Africa must heed this warning. We should empower teachers, not replace their judgement with code. We should uplift learners, not condition them to mask their true selves. And we should never confuse surveillance with care. Let us not forget that just because we can watch a child’s every move doesn’t mean we should.
A child is not a data point. And education should never feel like a prison.
Use human-centred AI where it helps, to personalise learning, support teachers and catch learners before they fall through the cracks. But when it comes to AI surveillance, face scanning, emotion detection, behaviour scoring and addictive edtech that manipulates young minds, let’s draw the line.
Let’s say: “Not here.”
Black Tech Street (BTS) is bringing transformative AI innovation education to Tulsa’s Historic Greenwood District with the help of microchip maker NVIDIA. The partnership aims to help Tulsa become a national model for tech-driven economic empowerment and community advancement.
NVIDIA, the world’s most valuable company, will collaborate with the organization to lead efforts in integrating cutting-edge technologies, according to The Black Wall Street Times.
“NVIDIA is powering the AI revolution—likely the most impactful leap in human history,” Tyrance Billingsley II, founder and CEO of Black Tech Street, said. “To have them standing with us in Greenwood, committing to help our community harness and lead this revolution, means more than I can articulate. This is a game-changing win for Black Wall Street.”
Advancing tech in Tulsa
As a member of Tulsa’s Tech Hub coalition (led by Tulsa Innovation Labs), Black Tech Street has played a significant role in the city’s designation as a Tech Hub for Autonomous Systems by the US Economic Development Administration.
The coalition recently secured $51 million in federal funding, with $10.6 million allocated to create a Greenwood AI Center of Excellence, led by Black Tech Street. Black Tech Street and NVIDIA will partner with the Tech Hub and local community organizations through this collaboration.
The State of Oklahoma will also be a part of this partnership, as it will train up to 10,000 learners. Training will be delivered through educational institutions and community partners.
Goals of the partnership
The partnership has three main goals: expanding AI education, boosting Tulsa’s economy, and positioning the city as a national hub for AI innovation. By offering advanced training programs through local institutions, the initiative will give Tulsa’s wider community access to in-demand AI skills. In turn, this will help create high-paying jobs and establish Tulsa—through Greenwood—as both a model and a testing ground for AI development.
NVIDIA will commit to the partnership by hiring local entrepreneurs, technologists, and students for NVIDIA’s training and certification programs. The company will also collaborate with Black Tech Street to secure federal, state, and philanthropic funding for research and talent development. Additionally, NVIDIA will connect Tulsa startups with its Inception program and global startup ecosystem to help accelerate local growth.
“This partnership will generate economic impact for years to come. NVIDIA and Black Tech Street are setting the stage for new jobs for Tulsans and opening doors of opportunity for our community,” Tulsa Mayor Monroe Nichols said.
Image: Mike Creef
The rapid rise of artificial intelligence (AI) and immersive digital tools is reshaping the foundations of modern education, demanding urgent strategic reform to prepare Generation Alpha for a technology-driven future.
Researchers have mapped this educational shift in a study titled “Education Strategy for the Net Generation”, published in Information. The paper calls for a complete overhaul of conventional teaching practices, outlining a new pedagogical model to align learning with the dynamic realities of the digital and generative AI eras.
The shift from digital to generative pedagogy
The research positions the evolution of education within a broad historical and technological framework, explaining how learning models must keep pace with societal and digital transformations. The authors describe how the transition from Web 2.0 to Web 4.0, combined with the rise of generative AI (GAI), large language models (LLMs), and immersive technologies like AR, VR, and IoT, has ushered education into a new era – Education 5.0.
Education 4.0, driven largely by digital pedagogy and the integration of online platforms, was sufficient for the early 21st century but no longer meets the needs of learners shaped by hyper-connectivity and intelligent systems. Generation Alpha, those born between 2010 and 2024, are fundamentally different from previous generations, acquiring information in complex, dynamic ways and demanding new teaching approaches that merge human intelligence with advanced technological tools. According to the authors, Education 5.0 is not just an incremental improvement; it represents a paradigm shift toward generative pedagogy, a hybrid system combining symbolic cognitive methods with AI-powered connectionist approaches.
This new framework emphasizes personalized learning paths, real-time feedback loops, and collaborative, interdisciplinary environments. Teachers, once positioned as transmitters of knowledge, must now act as mentors and interpreters, guiding students through the complexities of human–machine interaction. The model calls for cultivating not only cognitive skills but also emotional and ethical intelligence, ensuring that students remain critically aware and human-centered in a digital landscape increasingly mediated by AI.
Personalization, feedback, and collaboration in Education 5.0
Generative pedagogy harnesses the power of AI, machine learning, and big data to enhance the learning process. Adaptive platforms analyze student performance and behavior to create individualized learning experiences, ensuring that content delivery aligns with personal learning styles and needs. This hyper-personalization marks a significant departure from the one-size-fits-all approach that defined traditional education.
Real-time feedback has also emerged as a defining characteristic of Education 5.0. AI-powered systems, such as chatbots and smart tutors, are capable of instantly assessing student progress, providing immediate insights, and adjusting learning pathways in response to performance metrics. By integrating sensor technologies like eye-tracking and facial recognition, these systems can measure engagement and attention, offering educators actionable data to refine teaching methods and optimize outcomes.
Collaboration, a cornerstone of contemporary pedagogy, is also being transformed. The research highlights how digital platforms now enable interactive and collaborative learning spaces that transcend geographical barriers. These systems facilitate group discussions, joint projects, and peer-to-peer learning, fostering an environment where knowledge creation becomes a shared, participatory process. The integration of immersive technologies, such as extended reality and virtual environments, further supports active learning and deep engagement, ensuring students are not passive recipients of information but active co-creators of knowledge.
A case study within the research demonstrates how these concepts can be practically applied. In a vocational robotics program, students engaged in immersive digital classrooms that combined traditional teaching methods with VR-enhanced learning modules. The results were compelling: the experimental group outperformed the control group in task completion, demonstrated higher levels of motivation, and achieved better retention of complex concepts. This evidence reinforces the potential of generative pedagogy to enhance both technical proficiency and critical thinking in real-world educational settings.
Challenges, implications, and the road ahead
The study acknowledges significant resistance within educational institutions. Many educators remain skeptical, echoing earlier skepticism during the advent of digital tools, while others express concern over the reliability and ethical implications of AI systems. The rapid pace of technological evolution has also created a gap between industry capabilities and classroom realities, with schools often lagging behind in adopting and integrating cutting-edge innovations.
The authors argue that this resistance underscores the need for a balanced, critically informed approach to integrating AI into education. Generative AI, while powerful, is not infallible. It can produce errors, perpetuate biases, and generate misleading outputs, making critical engagement an essential component of modern learning. Students must be taught to treat AI as a partner rather than an unquestioned authority, applying rigorous sensemaking and analytical skills to every interaction.
The paper calls for redefining teachers’ roles in a hybrid human–machine learning environment. They will no longer be the sole arbiters of knowledge but will function as co-navigators, helping students critically navigate AI-mediated information ecosystems. This shift requires educators to adopt reflective practices and maintain a commitment to pedagogical integrity, ensuring that technology enhances rather than diminishes the human essence of education.
HTF MI just released the Global Voice AI in Education Market Study, a comprehensive analysis of the market that spans more than 143+ pages and describes the product and industry scope as well as the market prognosis and status for 2025-2032. The marketization process is being accelerated by the market study’s segmentation by important regions. The market is currently expanding its reach.
Major companies profiled in Voice AI in Education Market are: Google, Amazon (Alexa), Microsoft, IBM Watson, Nuance, Baidu, iFlytek, SoundHound, Sensory Inc, Smart Sparrow, Carnegie Learning, LinguaLeo, Speechace, Deepgram, ReadSpeaker, ELSA Speak, Sonantic, Soapbox Labs, Voiceitt, Sestek.
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HTF Market Intelligence projects that the global Voice AI in Education market will expand at a compound annual growth rate (CAGR) of 26.5% from 2025 to 2033, from USD 1.2 Billion in 2025 to USD 7.9 Billion by 2033.
The following Key Segments Are Covered in Our Report
By Type
Voice-enabled tutoring, Language learning assistants, Smart classrooms, Speech-to-text systems, Interactive reading tools
By Application
Language learning, Accessibility for disabled, Virtual classrooms, Personalized tutoring, Test prep
Definition: Voice AI in education refers to the use of speech recognition and natural language processing (NLP) technologies to facilitate learning and communication between students and educational platforms. Voice AI enables interactive language learning, assists students with disabilities, automates administrative tasks, and supports personalized tutoring. With the rise of smart devices, conversational AI, and accessibility requirements, educational institutions are adopting voice-based solutions to make learning more inclusive and engaging.
Market Trends:
• Conversational AI in classrooms, Multilingual support, Gamified voice learning, Integration with LMS, Voice biometrics
Market Drivers:
• AI adoption in education, Rising need for personalized learning, Growth of voice-enabled devices, Inclusivity goals, EdTech boom
Market Challenges:
• Privacy concerns, Accent recognition issues, High development cost, Data security, Low adoption in rural schools
Dominating Region:
North America
Fastest-Growing Region:
Asia-Pacific
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The titled segments and sub-section of the market are illuminated below:
In-depth analysis of Voice AI in Education market segments by Types: Voice-enabled tutoring, Language learning assistants, Smart classrooms, Speech-to-text systems, Interactive reading tools
Detailed analysis of Voice AI in Education market segments by Applications: Language learning, Accessibility for disabled, Virtual classrooms, Personalized tutoring, Test prep
Geographically, the detailed analysis of consumption, revenue, market share, and growth rate of the following regions:
• The Middle East and Africa (South Africa, Saudi Arabia, UAE, Israel, Egypt, etc.)
• North America (United States, Mexico & Canada)
• South America (Brazil, Venezuela, Argentina, Ecuador, Peru, Colombia, etc.)
• Europe (Turkey, Spain, Turkey, Netherlands Denmark, Belgium, Switzerland, Germany, Russia UK, Italy, France, etc.)
• Asia-Pacific (Taiwan, Hong Kong, Singapore, Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia).
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Voice AI in Education Market Research Objectives:
– Focuses on the key manufacturers, to define, pronounce and examine the value, sales volume, market share, market competition landscape, SWOT analysis, and development plans in the next few years.
– To share comprehensive information about the key factors influencing the growth of the market (opportunities, drivers, growth potential, industry-specific challenges and risks).
– To analyze the with respect to individual future prospects, growth trends and their involvement to the total market.
– To analyze reasonable developments such as agreements, expansions new product launches, and acquisitions in the market.
– To deliberately profile the key players and systematically examine their growth strategies.
FIVE FORCES & PESTLE ANALYSIS:
In order to better understand market conditions five forces analysis is conducted that includes the Bargaining power of buyers, Bargaining power of suppliers, Threat of new entrants, Threat of substitutes, and Threat of rivalry.
• Political (Political policy and stability as well as trade, fiscal, and taxation policies)
• Economical (Interest rates, employment or unemployment rates, raw material costs, and foreign exchange rates)
• Social (Changing family demographics, education levels, cultural trends, attitude changes, and changes in lifestyles)
• Technological (Changes in digital or mobile technology, automation, research, and development)
• Legal (Employment legislation, consumer law, health, and safety, international as well as trade regulation and restrictions)
• Environmental (Climate, recycling procedures, carbon footprint, waste disposal, and sustainability)
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Points Covered in Table of Content of Global Voice AI in Education Market:
Chapter 01 – Voice AI in Education Executive Summary
Chapter 02 – Market Overview
Chapter 03 – Key Success Factors
Chapter 04 – Global Voice AI in Education Market – Pricing Analysis
Chapter 05 – Global Voice AI in Education Market Background or History
Chapter 06 – Global Voice AI in Education Market Segmentation (e.g. Type, Application)
Chapter 07 – Key and Emerging Countries Analysis Worldwide Voice AI in Education Market
Chapter 08 – Global Voice AI in Education Market Structure & worth Analysis
Chapter 09 – Global Voice AI in Education Market Competitive Analysis & Challenges
Chapter 10 – Assumptions and Acronyms
Chapter 11 – Voice AI in Education Market Research Methodology
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This release was published on openPR.
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