This post is a quick overview of an Abto Software blog article about AI and teaching.
AI is transforming education: more learning processes are digitized and automated. AI-powered “tutors,” built on large language models (LLMs), offer accurate answers, instant feedback, and access to vast knowledge resources.
At first glance, these tools, with their broad knowledge and consistent performance, may seem like ideal tutors.
However, despite their impressive capabilities, AI systems miss the core of teaching: understanding, guiding, and connecting with students.
AI and teaching: are they ready for each other?
Imagine a student using AI to solve algebra problems, explore historical events, examine geopolitical issues, or receive writing tips—both practical and creative. No library trips, no dusty textbooks—AI becomes a limitless knowledge source for young learners.
AI tutors may seem superior to human teachers—fast, comprehensive, and always available. But they fall short in real interaction: they don’t truly understand the student.
Great teachers build “mental models” of each student. They track strengths and weaknesses, personalize instructions, sense confusion even before it’s spoken, and adapt lessons accordingly.
This isn’t limited to private teaching. Lecturers in large classrooms instinctively notice attention lapses and adjust their approach to maintain engagement. Human teachers connect socially, mirror students’ behavior, and foster deeper understanding.
Teaching is more than transferring knowledge; it’s almost a synchronization of minds.
AI tutors, in contrast, treat each interaction as isolated. This limits dialogue depth and reduces the richness of the learning experience.
AI and teaching merged: why simple workarounds fall short
AI tutors, based on LLMs, are stateless. They don’t retain memory by design, which makes processing complex context difficult—a key component of effective teaching.
To address this, some use methods like Retrieval-Augmented Generation (RAG) or Chain-of-Thought reasoning (CoT). While these can mimic memory and reasoning superficially, they don’t capture the essence of teaching: intuition, empathy, and personalized guidance.
Additionally, AI tutors don’t track student progress or refine their teaching strategies. They provide answers—but they don’t care about long-term success.
Simply put, AI assistants today can’t guarantee meaningful learning outcomes.
AI teachers for students: what’s missing?
For AI to go beyond a Q&A tool, it needs several key features:
- Persistent memory: to track student progress over time without manual input
- Goal-oriented instruction: to focus on long-term success and adjust strategies
- Continuous feedback loops: to use past interactions to improve future lessons
Limitations of current AI teaching solutions
Shared context: AI tutors can’t develop an evolving mental model of a student.
Physical awareness: They can’t observe behavior or perform tasks that require hands-on understanding, like lab experiments.
Self-reflective learning: They don’t improve over time or adapt methods.
Collective learning: Each AI tutor is isolated. Knowledge gained in one interaction doesn’t transfer to others.
Despite these limitations, advances are emerging.
Two recent developments show promise:
- OpenAI’s Study Mode, designed as an educational aid.
- Meta’s plan for a personal Artificial Superintelligence (ASI), announced by Mark Zuckerberg.
OpenAI’s Study Mode aims to improve shared context. However, after testing it on the topic of Pulse Radar, it struggled to identify comprehension gaps.
Meta’s vision of a personalized ASI is more ambitious. It promises to address these gaps and fundamentally enhance AI-assisted learning.
Shared context
Current AI tutors have volatile, short-term memory with no true dialogue integration.
- They can’t recall past progress, leading to generic responses.
- They don’t adapt to personal preferences or learning styles.
- Each student’s journey remains isolated, limiting effective guidance.
Physical awareness
AI operates as disembodied intelligence.
- It can’t handle tasks requiring spatial understanding, such as lab experiments.
- It misses nonverbal cues and contextual hints, limiting engagement.
- Practical, hands-on learning is difficult to convey through abstract instructions alone.
Self-reflective learning
AI models are static and cannot autonomously improve.
- They repeat mistakes without learning from experience.
- They need manual updates to adapt to new curricula.
- They cannot discover better ways to explain concepts independently.
Collective learning
AI tutors work in silos.
- Improvements made in one instance do not propagate to others.
- Collaborative educational scenarios, such as group problem-solving, remain unexplored.
- Students miss exposure to diverse perspectives and peer reasoning.
AI and the future of teaching and learning
AI in education will evolve beyond simple Q&A tools. In the near future, AI assistants may:
- Remember students’ past interactions
- Track progress
- Adapt strategies to individual learning needs
- Learn from thousands of interactions to optimize teaching
Such AI could guide the learning journey from school to university and professional training. While AI won’t replace teachers, it can help close learning gaps and make high-quality education more accessible.
How we can help
The first platforms to combine AI with adaptive teaching will reshape education. The goal is an AI that not only responds but guides, teaches, and adapts to every student.
Will you lead the transition from superficial AI tutors to adaptive AI teachers—or settle for imitation?
Our services:
- LLM strategy and consulting
- LLM development and integration
- LLM fine-tuning
- LLM support and maintenance
- AI development
- CV development
- AI agents
- AI solutions for business
FAQ
What’s the AI in teaching dilemma?
AI tutors deliver instant answers and feedback but cannot build a meaningful learning relationship. LLM-based AI lacks understanding, personalization, and empathy—essential elements of teaching.
Is using AI for teaching safe?
Yes, when used responsibly. AI can support learning but cannot replace human educators, who guide critical thinking, spot confusion, and provide emotional support.
What are the limitations of AI tutors in education?
- No shared context: AI can’t remember prior conversations
- No physical awareness: it misses hands-on tasks and nonverbal cues
- No self-improvement: AI doesn’t refine methods over time
- No collective learning: insights aren’t shared between tutors
What are the risks of AI teaching in schools?
Overreliance on AI can widen learning gaps, reduce personalization, and risk replacing rather than complementing human teachers.
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