Why AI is unlikely to replace teachers

The allure of a tech-driven utopia in education is not new. Radios, televisions, the internet, MOOCs – each has been heralded as a harbinger of the traditional teacher's obsolescence. Today, AI steps into this familiar spotlight, with some prophesizing a future with fewer educators. Understanding this perspective isn't challenging, given the enormity of public education's budget, the stubborn inequalities it harbors, and its notorious resistance to reform. However, the notion of significantly reducing teacher numbers through AI implementation seems, at best, a distant fantasy.

Chatbots, the latest prodigies of AI, have proven to be exceptional personal tutors. They can tailor information delivery to individual needs, offering a level of customization that traditional education struggles to match. But here's the rub: education is not merely about transferring information. It's about fostering a unique educational relationship that optimizes learning. For all its sophistication, AI lacks the capacity to replicate this.

AI indeed creates a paradise for autodidacts. Those with a natural inclination towards self-directed learning, armed with motivation and discipline, find in AI a boundless resource. However, the majority aren't autodidacts. They thrive in a relational context that not only motivates but also facilitates learning. This is a foundational principle in major learning theories, from Vygotsky's social development theory to Bandura's social learning theory and Bruner's constructivist theory. The invisible labor of a teacher or a college instructor lies in creating and nurturing this context. Presently, there is nothing in AI that can substitute this critical human element.

Furthermore, educational institutions have become integral to societal fabric, not merely as centers of learning but as community hubs. Imagining what millions of children and young adults would do without the structure of schools and colleges opens a Pandora's box of societal and developmental questions. These institutions require adult presence, not just for educational delivery, which AI might partly assume, but for the overarching environment of care and socialization they provide.

My prognosis? Unlike other industries where automation has resulted in significant workforce reductions, the field of education, particularly the teaching staff, will likely remain unscathed in this aspect. There's no need for panic among educators, but there is a need for adaptation. Learning to harness AI's capabilities will be crucial, not to replace teachers, but to complement them, freeing up time for the more nuanced, relational, and affective aspects of their roles. Additionally, educators must remain agile, adapting curricula to include skills that future employers will value, ensuring students are well-equipped for the evolving workforce.

In essence, AI in education is not a replacement, but a tool – one that, if used wisely, can enhance the educational experience without displacing its most vital component: the human educator.

Four principles of public and philanthropic support of AI adoption

Governments and philanthropists can play a role in supporting AI diffusion across various sectors. Their involvement is as critical as that of businesses and researchers in driving forward this technological revolution. However, forging a public strategy for AI adoption remains a complex and unresolved task.

The rapid embrace of AI technology calls for a focus on leveraging the collective experiences of its extensive user base, in conjunction with market forces and entrepreneurial innovation. The United States, and California in particular, stands out for its globally admired technology innovation ecosystem. This environment, driven by dynamic market forces and a spirit of entrepreneurship, creates an ideal setting for AI development. Nevertheless, the lack of a cohesive public strategy in managing this evolution might lead to varied and possibly conflicting outcomes and objectives in AI adoption.

At the heart of this matter is the public's significant interest in the effective deployment of AI. The technology holds the potential to substantially boost the economy, revolutionize public services, reshape education, and enhance social welfare systems. Yet, it is essential to balance these advancements with equitable and efficient technology adoption, ensuring that AI contributes to resolving rather than exacerbating societal inequities.

Moreover, the integration of AI in public services presents a dual advantage: improving efficiency and extending service accessibility to a wider population segment. The key challenge is to deploy these technologies inclusively, considering the diverse needs of the community. While the swift adoption of AI offers numerous opportunities, it also demands strategic and thoughtful planning. This strategy must aim not only to capitalize on AI's benefits for economic and service improvements but also to guarantee that its societal integration is equitable and inclusive, aligning technological progress with the greater public interest.

1.  Get real

The first guiding principle in AI adoption is the pursuit of a balanced perspective, essential in navigating between two extreme viewpoints. On one side, there's the dystopian view that envisions AI as a catalyst for catastrophic job losses. This narrative often emerges from a fear of the unknown, harking back to historical instances where technological advancements initially disrupted the job market. However, this perspective tends to overlook how technological evolution has historically opened up new job opportunities and industries. There is also fear that AI poses an existential threat to humanity. These two mutually exclusive dooms day scenarios are amplified by the media.

On the other side lies the overly optimistic view that sees AI as a cure-all for every challenge, and that we quickly transition to labor free economies and enjoy abundance without work. This standpoint emerges from recognizing AI's immense potential to improve efficiency, solve complex issues, and bring novel solutions to various sectors. However, it can underestimate the challenges and limitations of implementing AI technologies, such as ethical considerations, the need for comprehensive data, and crucial human oversight.

A more realistic outlook suggests that the future of AI will likely follow historical trends, presenting both opportunities and challenges. Similar to the impact of the internet and mobile technology, AI is expected to enhance productivity and stimulate economic growth, but not bring us quickly into the world without scarcity. This advancement could manifest through more streamlined operations, improved data analysis, and innovation in diverse sectors.

Both extremes discourage pragmatic, thoughtful planning. The society cannot control a change that it cannot fathom. A balanced approach to AI adoption acknowledges AI's significant potential to contribute to productivity and economic growth. Simultaneously, it recognizes the importance of strategic management to facilitate a smooth transition in the job market and society at large. This approach avoids the pitfalls of extreme views, opting instead for a nuanced and realistic understanding of AI's role in shaping the future.

2.  Democratize technology

The second principle in AI adoption emphasizes the democratization of AI technology. This concept is based on the idea that AI's benefits should be broadly accessible, not just limited to a small group of experts. This approach to democratizing technology mirrors developments in fields like web design, which has evolved from a specialized skill for programmers to a more universally accessible tool. The devolution of expertise has been a steady trend, and we must not allow it to reverse with AI.

In AI, democratization means expanding access beyond tech experts to include educational institutions, public agencies, and businesses. This approach prevents potential monopolization by  a few vendors who might control the market with proprietary platforms and high licensing fees, which could shift the focus of AI from innovation to profit, limiting its societal benefits.

Democratizing AI fosters grassroots innovation, reducing vendor-dependency, enabling individuals and organizations to develop custom AI solutions for specific needs and challenges. This can spur a wave of creativity and problem-solving in sectors like education, healthcare, social services, and public administration.

Additionally, democratizing AI plays a critical role in reducing the risks of AI reinforcing existing inequalities or biases. When AI is accessible and understandable to a diverse group, it is more likely to be used inclusively, considering a wide range of perspectives and needs.

In essence, democratizing AI is about making it a tool for many, empowering a wide user base to understand, engage with, and apply AI in ways that enhance their work and lives. This approach ensures that AI's benefits are widely distributed and its development reflects a variety of voices and needs.

3.  Regulate fine-tuning

The third principle in AI adoption underscores the vital role of governments and philanthropic organizations in regulating AI's "fine-tuning" process. This principle acknowledges their significant influence in shaping AI's ethical development.

Fine-tuning in AI involves refining algorithms and their outputs to align with specific ethical guidelines and objectives. This step is crucial to ensure AI systems adhere to societal norms. A key part of fine-tuning is filtering out harmful or inappropriate content, such as pornography, conspiracy theories, or explicit violence. This process not only prevents the spread of such content but also ensures AI's positive contribution to society.

However, fine-tuning goes beyond just excluding harmful content. It also includes identifying and rectifying inherent biases within AI systems. AI models, trained on vast datasets, can inadvertently reflect societal biases. Left unchecked, these biases may reinforce or exacerbate societal inequalities. For example, AI by default generates images of unspecified  people as white males, reflecting a bias in training data. Correcting such biases is essential to make AI inclusive and representative of global diversity. Governments must compel IT companies to spend more on fine-tuning, and make their fine-tuning practices more transparent.

However, governments and philanthropist may play an active role in funding AI ethics research, promoting diversity in training data, or setting up bodies to oversee and evaluate AI systems for biases.

4.  Support equity

The fourth principle in AI adoption is about addressing areas where market forces alone may not suffice, particularly concerning the equity implications of AI. This principle calls for targeted support in segments where the private sector might not adequately invest due to limited market incentives.

A critical area of focus is technology for people with disabilities. Market mechanisms often fail to address these needs adequately, as the market for assistive technologies can be too small to lure significant private investment. This gap necessitates government or philanthropic intervention to develop AI solutions that are innovative, inclusive, and accessible to everyone, regardless of their physical or cognitive abilities.

Another area is AI's role in bridging language barriers and aiding language learners and linguistically marginalized communities. Here again, market forces may not be enough to drive the development of AI tools tailored for these groups. Government and philanthropic support is essential in creating AI applications that meet diverse linguistic needs, promoting inclusivity and understanding.

In education, AI's impact is particularly profound. Traditional reliance on written assignments and assessments means integrating AI into education is not just about investment but also about understanding learning theories and pedagogical practices. While entrepreneurs are adept at crafting innovative tech solutions, they may lack the necessary expertise in learning sciences to ensure these tools are effective in a learning context. Thus, additional support in research, development, and training is crucial for AI to positively transform educational practices.

Contrastingly, sectors like entertainment, which are more adaptable and resource-rich, are likely to manage AI-driven disruption independently. However, more public-oriented sectors such as social services, education, and medicine require substantial support from governments and philanthropic organizations. These sectors are pivotal to societal well-being and equity; their effective navigation of AI integration is crucial for the equitable distribution of AI benefits.

In summary, strategic AI adoption and integration is imperative, especially in sectors where market forces are insufficient. This strategy should include support for education, social services, and disability support to ensure AI serves the public good effectively. The involvement of governments and philanthropic organizations is critical in providing necessary resources, guidance, and regulatory frameworks. This ensures the development and implementation of AI in ethical, equitable, and universally beneficial ways.

Advice for Entrepreneurs Developing AI Products for Educators

Hundreds if not thousands of start-ups have emerged to leverage the potential of generative AI, which is a good thing. This surge in innovation is crucial for ensuring a fair and beneficial transition to this new technology. Among these companies, many focus on the education sector. It's not just start-ups that are diving into this arena; established companies are also adopting an entrepreneurial approach.

First, let's talk about products that are likely to fail. A common pitfall is exploiting the current lack of AI expertise among teachers and professors. These models act as basic intermediaries, providing minimal assistance in crafting better AI prompts. However, generative AI's true allure lies in its democratic nature and ease of use. It lowers barriers by understanding natural language, eliminating the need for coding or complex interface navigation. Businesses that merely enhance prompt writing and inflate the concept of "prompt engineering" to promote their services are not just unethical but also unsustainable. Their low-value proposition is a short-term strategy at best; users will eventually see through it.

Another error is developing solutions without a deep understanding of educational practices. Merely interviewing a few educators doesn't suffice. To genuinely grasp the needs of the sector, companies should either include experienced educators in their teams or seek continuous, in-depth feedback on their product prototypes. This approach prevents creating solutions that lack a real problem to solve. Unfortunately, few outsiders truly understand the core challenges AI poses in education, with only a handful of products like Khanmigo addressing these issues effectively. One of the core problems for educators is the inability to calibrate AI tools for the Zone of Proximal Development.

Despite these pitfalls, the field of AI in education is ripe for innovation. Instead of creating superficial aids, there's a need for specialized, high-value tools. Each academic discipline has foundational skills critical for further learning, and some aspects of these disciplines can be aided by AI without compromising the development of higher skills. Developing numerous, level-specific assignments that integrate AI use while safeguarding these essential skills is vital. Another significant area for innovation is enhancing student experiences. Many educational institutions, particularly large universities, struggle to provide consistent advising and support in understanding academic regulations, schedule planning, and general wellbeing. Moreover, both K-12 and higher education systems face inefficiencies in their operations. Addressing these real issues and contributing meaningful, high-value solutions is where the true opportunity lies.

No time to learn AI? Use authentic learning

No time to to delve into the world of  AI? If so, you're not alone. Many of us feel a pang of guilt for not being able to spare the time to explore generative AI tools. However, it is much easier than you may think. 

The trick is to use chatbots for the regular tasks life brings to you. Anything, especially tasks you are not looking forward to do is a fair game. Think about updating syllabi, brainstorming assignments, developing grading rubrics, and planning lessons. The list extends to crafting administrative emails, organizing research data, summarizing articles, generating content ideas, and preparing meeting agendas. Try to ask a chatbot first. 

Now, it's important to temper expectations with a dose of reality. In about 70-80% of cases, AI will save you time right off the bat. However, in the remaining tasks, you might not see immediate gains, and they turn out to be easier done by hand. The effectiveness of AI heavily depends on the nature of your work and your willingness to stick to it and learn its nuances.

There's a learning curve, for sure, but it is not very steep.  The results are well worth the effort. For instance, AI's ability to generate first drafts, suggest edits, and even brainstorm ideas can significantly streamline your workflow.

However, it's crucial to understand the limitations of AI. It's not a magical solution to all your problems. Think of it more as a collaborative partner that can take on the heavy lifting of routine tasks, allowing you to focus on the more creative and complex aspects of your work. Literally, ChatGPT and its cousins shine in the most routine, most boring tasks, leaving you more time for creative work. 

The key to effectively integrating AI into your professional life is to start small, use the natural flow of tasks, and gradually expand its role as you become more comfortable with its capabilities. This will allow you to stay ahead of most students. Eventually you will also see how it could be used in instruction. But getting some first-hand experience is the first step. 

AI use is not a sin

The enduring influence of Puritan ethics in American culture presents an intriguing dichotomy. This historical ethos, with its deep roots in hard work and discipline, colors modern perspectives on technology and learning. I am really worried about the disproportional efforts to catch students using AI, as if it was somehow sinful on its own.

Puritan ethics, born from 16th and 17th-century religious reformers, celebrated hard work as a moral virtue. This belief, that success must be earned through effort and toil, subtly shapes American attitudes towards technology, including AI in education. Critics of AI in this realm often argue that it makes learning 'too easy', equating ease with moral decay. They yearn for the 'authenticity' of traditional learning methods, where struggle is seen as the only legitimate path to knowledge.

However, it's crucial to acknowledge that learning does indeed require effort; growth is impossible without it. But this effort need not be synonymous with drudgery. Suffering and effort are not interchangeable. The assumption that struggle is inherently valuable and that ease is inherently suspect is a limited view, overlooking the broader purpose of education.

The Puritanical echo in the debate over AI in education is ironic. The ethos was about self-improvement, yet rejecting AI tools seems counterproductive. AI can democratize and personalize education, making it more accessible and tailored to individual needs.

The overuse of ethical judgments in this context reflects a broader issue. Ethics is often oversimplified, leaving little room for the complexities of life. This misuse of ethics, particularly in education, can hinder innovation.

In re-evaluating these inherited ethical frameworks, it's essential to recognize that ease in learning isn't antithetical to the values of hard work and achievement. Education's true goal is empowerment and enlightenment, and AI offers a transformative potential in reaching this goal.

I'll tell you what's unethical (a rant)

Ah, the great ethical quandary of our times in education – the use of AI! Picture this: earnest educators standing as the last bastion of traditional wisdom, decreeing “Thou shalt not use AI,” with a fervor that's almost admirable, if it weren't so quaintly misplaced. This isn't just a classic case of misunderstanding technology; it's like watching someone trying to ward off a spaceship with a broomstick.

Now, let's talk about restrictions. In education, where reason should reign supreme, the rationale for any restriction must be more substantial than "because it’s always been this way." When an educator waves the flag of prohibition against AI, one can't help but wonder: where’s the logic? It’s a bit like saying you shouldn’t use a calculator for fear it might erode your abacus skills.

Here's a thought to ponder: the only justifiable ground for restricting AI use in education is if, and only if, it hinders the development of a foundational skill – one that's essential for crafting more complex abilities required for advanced learning. And, let’s not forget, the burden of proof rests with the person setting the limits. Which skill, exactly, is prevented from being developed by the use of AI? If you can explain it to students, then yes, be my guest, ban away.

AI is a very good tutor. Yes, it makes mistakes sometimes, but it is infinitely patient and always available, no appointment necessary. No need to be embarrassed when asking for the umpteenth example to illustrate an elusive concept. To withhold this resource from students isn't just a tad unethical; it's like hiding the key to a treasure chest of knowledge and saying, “Oops, did I forget to mention where it is?”

So, what's ethical and what's not in this grand AI debate? Anything that facilitates learning and growth is a big yes in the ethical column. Casting aspersions on AI without a valid reason or depriving students of its benefits is unethical.

The larger, real question we should be asking is this: What defines ethical practice in education? Is it clinging to the past because it’s comfortable, or is it embracing the future and all the tools it brings to help our students soar? At the end of the day, what’s truly unethical is anything that hinders progress under the guise of misguided caution. After all, isn't education all about unlocking doors, not closing them?

What does AI reveal about relational pedagogy?

In the ongoing narrative of education's transformation, AI's integration has prompted a profound reassessment of what constitutes uniquely human skills. Stephen Wolfram astutely observed that AI hasn't so much humanized computers as it has highlighted the machine-like aspects of certain human abilities, such as writing. This insight extends powerfully into the realm of education, reshaping our understanding of teaching and its intrinsic human elements.

Traditionally, teaching has been viewed primarily as a process of instruction, a transmission of knowledge from teacher to student. However, the advent of AI in education challenges this perspective. AI's ability to deliver instruction, personalize learning, and even interact with students reveals that the instructional aspect of teaching is not exclusively human after all. Machines can replicate, and in some cases, surpass human efficiency in these areas. This realization prompts a crucial question: if machines can instruct, what then is the unique value that human educators bring to the table?

The answer lies in the relational aspect of teaching, an area where AI cannot succeed. AI's emergence has inadvertently cast a spotlight on the importance of relationship-building in education, underscoring its irreplaceability. The human teacher's role evolves from being a mere conveyor of knowledge to a mentor, a guide, a catalyst for emotional and social growth. In this light, the human educator's value is redefined, emphasizing those qualities that machines cannot replicate: empathy, emotional intelligence, and the ability to inspire and motivate.

This shift in perspective is part of a broader redefinition of what it means to be human in an age increasingly dominated by machines. As AI takes over tasks that were once thought to require human intelligence, we are compelled to re-examine and emphasize those domains that are uniquely human. The essence of humanity is being recalibrated, focusing more on emotional, social, and creative capacities - areas where humans excel and machines falter.

In the context of education, this recalibration has profound implications. It suggests that the future of teaching lies not in competing with AI in cognitive tasks but in embracing and enhancing the relational, emotional, and creative aspects of education. Teachers, liberated from the routine cognitive aspects of their work by AI, can focus more on developing deep, meaningful relationships with students, fostering their emotional and social growth, and nurturing their creativity.

This evolution does not diminish the teacher's role; rather, it elevates it. Educators become the custodians of those aspects of learning that are quintessentially human. The classroom becomes a place where not just intellectual but emotional and social skills are developed, where students learn not just from machines but from the rich, complex interactions with their teachers and peers.

AI's integration into education does more than just streamline teaching; it prompts a reevaluation of the teaching profession and a redefinition of humanity itself. As AI assumes more cognitive tasks, the unique value of human educators comes into sharper focus, centering on the relational and emotional aspects of teaching. This shift heralds a new era in education, one where the human element is not just preserved but celebrated and elevated, defining a future where humans and machines work in tandem to create a richer, more holistic educational experience.