Why using AI can’t replace teachers
Education technology is undergoing a transformation: more and more processes are digitized and automated. AI-powered “tutors”, built upon LLMs (large language models), can deliver accurate answers, instant feedback, and access to vast educational resources.
On the surface level, such tools, surpassing teachers in breadth of knowledge and consistency of performance, seem like ideal tutors.
But despite extensive capabilities, these algorithms fall short at the very essence of teaching.
AI and teaching not ready for each other?
Let’s imagine this scenario: a student is using AI tuition to receive quick answers to intricate algebra problems, historical inquiries, geopolitical questions, and even writing recommendations, both to-the-point and creative. No more library trips and flipping old books – for the younger generation, AI became the well of knowledge.
On the first glance, AI tutors appear superior to traditional, human instruction – fast, easy, and comprehensive. But perfect on paper, AI solutions are falling behind face-to-face human interaction – they don’t learn anything about students using them.
Great teachers create, so to say, “mental models” of students and understand their strengths and struggles. They personalize provided instructions, track progress, sense confusion even before it’s spoken, and adjust learning programs when necessary.
While some might argue that this primarily applies to teachers working privately, even lecturers that address large audiences instinctively sense attention lapses and adjust to bring back engagement and effectiveness. Humans, being highly social, are wired for connection and mirror each other to create deeper interactions.
It’s not just about knowledge transfer – it’s almost a synchronization of minds.
AI-powered teaching assistants treat each interaction as an isolated event, thus limiting the depth and richness of dialogue.
AI and teaching merged: why simple workarounds fail
AI tutors, built upon LLMs (large language models), are stateless by design and built to not preserve memory. AI solutions inherently lack the ability to process complex context – an aspect that’s essential to achieve efficient teaching.
To fill this gap, some use Retrieval-Augmented Generation (RAG), Chain-of-Thought Reasoning (CoT), or other. These might superficially replicate human memory and reasoning, but they are workarounds, not solutions – these methods fundamentally miss what defines efficient teaching: intuition, empathy, and personalization.
What’s more, AI tutors don’t track learning progress or evolve their methods – they simply deliver answers. Boiled down, AI solutions don’t care about students.
To put it briefly, AI-powered assistants don’t provide long-term success.
AI teachers for students: what’s missing?
To become more than answer-and-question machines, AI-based platforms must acquire several capabilities:
- Persistent memory: to remember prior interactions and track learning progress without requiring manual intervention
- Goal-oriented instruction: to consider long-term success and adapt learning strategies if necessary
- & continuous feedback loops: to use prior interactions to shape future lessons, just like human teachers do naturally
Let’s get into detail about the key limitations of today’s AI-powered solutions:
| Shared context | AI tutors don’t have the ability to develop a deeper mental model of students over time |
| Physical awareness | They can’t directly observe the behavior of students, which limits their effectiveness in real-world educational contexts (for example, lab experiments) |
| Self-reflective learning | AI tutors don’t improve over time or adjust their methodologies |
| Collective learning | They don’t pass on educational insights, so there’s no collective knowledge sharing between “teachers” |
But breakthroughs are underway.
At the time this blog post was in the making, two notable developments emerged in the AI landscape:
- OpenAI introduced its new Study Mode, explicitly designed as an educational aid
- Mark Zuckerberg has announced Meta’s plan to develop a personal Artificial Superintelligence
We anticipated OpenAI’s ambitious Study Mode to address the limitation of shared context development. However, after two hours of engaging Study Mode on the topic of Pulse Radar, it became clear that it struggled to identify and address the lapses in comprehension.
In contrast, the vision of personalized ASI seems remarkably aligned with resolving the problem we mentioned. This vision does not only address existing gaps but also fundamentally transforms the potential of introducing AI-assisted teaching.
Shared context
A volatile, short-term memory without true dialogue integration.
- No personalization – the algorithm can’t recall past progress, which causes generic interactions
- Limited engagement – the algorithm doesn’t remember personal details and thereby doesn’t adapt
- No continuity, which means the inability to build a model of how different students perceive information and failure to guide the process within their conceptual paradigm
Physical awareness
A disembodied, text-bound intelligence.
- No support for processes that require spatial understanding (for example, lab experiments)
- No understanding of clues and context, which limits the ability to engage the student
- No schooling going beyond abstract instructions (a problem for those who need hands-on experience)
Self-reflective learning
A static, programmed model, which cannot autonomously upgrade.
- No improvement through experience, which causes mistake repetition
- A struggle to absorb new knowledge or changes in curriculum unless being manually retained
- The inability to discover better methods of explaining a topic when needed
Collective learning
No ability of sharing obtained knowledge between models.
- Slow propagation of improvements: each “tutor” works alone; a breakthrough or fix that’s discovered in one individual interaction doesn’t transfer to others, which causes redundant failures
- Missed opportunity for rich educational scenarios (for example, group problem-solving or debates), which could enhance teaching by exposing the student to diverse views or collaborative reasoning
- Each tutor-student pair exists in an isolated silo, so there’s no community knowledge building
AI and the future of teaching and learning
In the coming years, big changes are coming: AI assistants won’t stay question-and-answer machines for long. They’ll remember their students, track progress, adapt approaches, and learn from thousands of interactions, thus becoming more tailored to the individual student.
It might seem overblown, but leading-edge AI solutions could guide the entire learning journey from A to Z, from school to university and through professional training – with support across classrooms and workplaces. They won’t replace teachers, but close learning gaps and make quality schooling more accessible than ever.
How we can help
The first educational platforms to solve the dilemma will transform the industry and surpass their competitors. The goal is building a platform that doesn’t just respond, but teaches and adapts to each individual student.
So, will you lead the transition from superficial AI assistants to adaptive AI teachers or settle for imitation?
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FAQ
AI-based tutors can deliver instant answers and feedback, but they fall short of the very essence of teaching. Built upon LLMs (large language models), AI-based solutions are lacking the capability of building a meaningful learning relationship.
The dilemma is simple: AI might look impressive on the surface level, yet misses the backbone of teaching – understanding, connection, and improvement.
Yes, using AI in teaching scenarios is safe when applied with responsibility – but there are some big limitations. While supporting learning sessions by providing instant assistance, AI can’t truly replace human specialists.
Educators remain still essential for guiding critical thinking, spotting confusion, and providing emotional support.
- No shared context – such tools can’t remember past conversations
- No physical awareness – these solutions can’t understand hands-on tasks, nonverbal cues, and other real-world nuances
- No self-sufficient improvement – AI tutors don’t reflect on mistakes or improve over time
- No collective learning – AI models don’t share insights with each other
The risks include overreliance on innovation and absence of personalization, as well as bigger educational gaps. There’s also potential danger of using advanced technology for replacement rather than just complementation of traditional education methods.