AI and Education
AI is not fixing the fundamental problem with education, which is not content delivery, it is access, relevance, and the signal that credentials send to employers. It is, however, creating new paths around the problem.
Founder, Majhi Group & Majhi OS
My mother has been a teacher in government schools in Odisha for most of her working life. When AI tutoring tools started appearing, the immediate conversation in education circles was about academic integrity, students using AI to cheat. That was not what she was thinking about. She was thinking about the thirty-five students in her classroom whose learning was bottlenecked by how many hours she had, and whether a tool existed that could extend her reach.
The conversation about AI and education has been dominated by two concerns: that AI will make it easier for students to cheat, and that AI tutors will eventually replace teachers. Both concerns are real in some form. Neither is the most important thing happening.
The most important thing happening is that AI is creating new paths around the credentialing system, and those paths are beginning to matter in ways that will change who gets access to economic opportunity.
What the existing system does
The formal education system does three distinct things that are often conflated.
It transmits knowledge and builds capability. A student who learns mathematics, writing, programming, or any other discipline is developing a capability that has genuine value.
It signals capability to employers. The credential, the degree, the institution, the GPA, is a signal that employers use to make hiring decisions because they cannot directly observe the underlying capability.
It provides access to networks. The alumni network of a well-regarded institution, the relationships formed with peers and professors, the exposure to professional contexts that educational environments provide, these are genuine assets that graduates carry.
AI is relevant to the first of these in meaningful ways. It is almost irrelevant to the second and third, which is where most of the economic value of formal education is concentrated.
What AI actually changes about learning
Access to expert-level explanation is no longer scarce. A student in a well-resourced school has always had access to teachers who can explain concepts at the level the student needs, identify where understanding has broken down, and adapt the explanation accordingly. A student in an under-resourced school has had access to the same curriculum but lower-quality explanation, feedback, and adaptation. AI tutoring tools are beginning to close this gap, providing the kind of responsive, adaptive explanation that was previously available only to students with access to high-quality teachers or expensive tutors.
This is genuinely important for students in under-resourced educational environments, including the rural India context that I know directly. The student who couldn't afford a tutor and whose school had 60 students per class now has access to a patient, infinitely available, highly capable explainer. This matters.
The cost of reskilling is dropping. Learning a new technical skill, programming, data analysis, digital marketing, a new language, has historically required either formal education (expensive, time-consuming) or self-study (possible but high-effort, high-failure-rate). AI tutoring systems make self-directed skill acquisition more accessible and more effective. The person who wants to learn Python can now do so faster, with better feedback, and at lower cost than was possible five years ago.
The feedback loop on writing and thinking has shortened. One of the most valuable things a good teacher does is give specific, actionable feedback on student work, not just "this is good" or "this needs work" but "the argument breaks down here because the causal claim isn't supported by the evidence you've cited." AI writing tools are beginning to provide this level of feedback at scale. For students who haven't had access to teachers who give this kind of feedback, this is a new capability.
What AI does not change
The credential still signals. Employers hiring at scale use credentials to filter candidates because they have no better tool. An employer processing 500 applications for a software engineering role will not individually assess every applicant's coding capability from a portfolio. They will filter for candidates from institutions they recognise, which is a proxy for the capability they actually want. AI learning tools produce capability. They do not produce credentials that employers recognise.
The network advantage is unchanged. A graduate of a well-regarded institution has access to alumni networks, institutional brand associations, and the professional contacts formed during their education. These advantages do not exist for someone who learned the same skills via AI tools. The network advantage is structural, it depends on membership in a social institution, and AI does not create social institutions.
The paths being created
What AI is creating is not a replacement for formal education. It is a set of paths around the credentialing bottleneck for the specific cases where the bottleneck can be bypassed.
Software development is the most established example. The ability to demonstrate capability through code, through open source contributions, through portfolio projects, through technical assessments, has created a path to well-paying employment that doesn't require a formal credential from a prestigious institution. AI tools that make learning to code faster and more accessible are expanding this path.
Similar dynamics are developing in data analysis, digital marketing, content creation, and other fields where the work product is directly evaluable. They are not yet developing in fields where the credential is regulatory (medicine, law) or where the network is the product (finance, consulting).
The people who benefit most from the paths AI is creating are those who have the capability and motivation to develop real skills but have lacked access to the environments where those skills are taught well.
The people who benefit most from AI in education are those who had the capability but lacked access to the environments where it could be developed. That is a significant population in many parts of the world, including the parts I come from.
The ceiling on how far those paths can take someone is still set by the credentialing system's grip on the most lucrative professional pathways. AI loosens it in some domains. It does not break it. The WEF Future of Jobs Report 2025 projects 170 million new roles created by 2030 even as 92 million are displaced, the skills needed to fill those new roles will require learning environments that can actually build them. The gap between AI creating demand for new skills and credential systems adapting to signal those skills is where the real tension lies.
See also: AI and Human Potential, AI and India, The Global Hiring Floor
Sources
WEF Future of Jobs Report 2025: displacement, creation, and reskilling projections
Indeed Hiring Lab: Educational Requirements in US Job Postings (2024)
Frequently Asked Questions
What does AI actually change about learning, and what does it not change?
AI changes access to expert-level explanation and feedback, the kind of responsive, adaptive instruction previously available only to students with excellent teachers or expensive tutors. A student in an under-resourced school in rural Odisha now has access to a patient, highly capable explainer at low cost. What AI does not change is the credential system and the network advantage: employers hiring at scale still filter by institution, and the alumni networks of well-regarded colleges remain structural advantages that AI learning tools cannot create. The economic value of formal education is concentrated in these two dimensions. AI is most relevant to the third, the actual knowledge transfer, and least relevant to the dimensions where it matters most economically.
Does AI risk replacing teachers, or does it extend them?
The extension scenario is more likely and more consequential than the replacement scenario. A teacher with thirty-five students and limited hours faces a bandwidth problem, AI tutoring tools address that bandwidth constraint, not the teacher. The work that requires human presence, motivation, mentoring, the development of character alongside capability, the relationship that makes a student persist through difficulty, is not what AI is doing. The replacement discussion is loudest in systems where teaching is already well-resourced. In the systems where the binding constraint is teacher-to-student ratio, the extension case is the important one.
Who benefits most from AI in education, and who doesn't?
The people who benefit most are those who had the capability and motivation to develop real skills but lacked access to environments where those skills are taught well. For students in under-resourced schools, in Tier 2 and Tier 3 cities, in countries where the gap between the best and worst educational environments is large, AI tutoring represents a genuine broadening of access to quality instruction. The people who benefit least are those who need the social institution of education, the credentialing, the network, the professional exposure, rather than the knowledge transfer. AI provides the latter and cannot provide the former.
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