Generative AI (GenAI) tools, such as ChatGPT or other large language models (LLMs), are hailed as groundbreaking innovations for generating human-like responses to diverse prompts. They excel at mimicking natural language, summarizing information, and even assisting with creative tasks. However, their technical foundation raises significant concerns about their suitability as reliable tools for learning and knowledge acquisition.
The Technical Foundations of Generative AI
To understand why GenAI tools may not be suitable for learning, it’s important to grasp how they generate answers. Generative AI models operate on a statistical foundation built through extensive training on massive datasets. Here’s a simplified explanation of the underlying mechanics:
Pretraining on Vast Datasets
GenAI models are trained on large corpora of text sourced from the internet, including books, websites, articles, and forums. The training process focuses on predicting the next word in a sequence, a method called transformer-based language modeling. This means the models learn patterns, structures, and probabilities of words appearing together based on context.
Tokenization and Probability Distribution
Text input is tokenized into smaller units (words, subwords, or characters), which the model processes. When generating a response, the AI assigns probabilities to potential tokens that could follow the input, selecting the most probable ones to construct coherent sentences.
Example: Tokenization and Correct Probability
Suppose you prompt a GenAI model with the sentence:
„The capital of France is“
The model tokenizes this input into smaller units and predicts the next token based on probabilities:
„Paris“ (90% probability)
„London“ (5% probability)
„Berlin“ (3% probability)
Other tokens (2% combined probability)
Based on these probabilities, the model selects „Paris“ as the next token and proceeds to predict subsequent tokens accordingly. However, the model doesn’t know that Paris is the capital of France—it selects this token because it appeared most frequently in relevant contexts during training.
Example: Tokenization and False Probability
Now consider the prompt:
„The capital of Brazil is“
If the training data disproportionately contains references associating „Rio de Janeiro“ with Brazil (due to its cultural prominence and historical association), the model might assign higher probabilities as follows:
„Rio de Janeiro“ (70% probability)
„Brasília“ (20% probability)
Other tokens (10% combined probability)
In this case, the model could incorrectly select „Rio de Janeiro“ as the capital of Brazil. While Rio is a well-known city, the correct answer is Brasília. This error stems from the model’s reliance on token probabilities rather than an understanding of geographical facts.
Lack of Semantic Understanding
Critically, GenAI models do not „understand“ content the way humans do. They lack a knowledge graph or conceptual framework to validate information against real-world facts. Instead, they rely on pattern recognition to emulate coherence and relevance, which may result in technically incorrect or contextually misleading answers.
Fine-Tuning with Reinforcement Learning
Many GenAI systems are fine-tuned to align with user expectations through Reinforcement Learning with Human Feedback (RLHF). This process adjusts the model to generate responses that seem helpful, polite, or contextually appropriate. While this improves usability, it does not guarantee factual accuracy.
Why This Matters for Learning
Given the technical mechanisms above, the limitations of GenAI for educational purposes become apparent:
Prone to Hallucination
One of the most significant drawbacks of GenAI is its propensity to hallucinate—that is, generate plausible but incorrect or fabricated information. This occurs because the model selects responses based on probabilities rather than verified facts.
No True Comprehension
Learning involves understanding concepts, applying them in various contexts, and questioning underlying principles. GenAI lacks comprehension and cannot engage in genuine dialectical reasoning, often providing surface-level answers without depth or critical examination.
Contextual Missteps
Because GenAI models are trained on data that may be outdated or contextually irrelevant, their responses might not reflect the most current knowledge. For dynamic fields like science, technology, or law, relying on such tools could lead to the assimilation of obsolete or inaccurate information.
Encourages Passive Consumption
Effective learning is an active process involving critical thinking, problem-solving, and iterative questioning. GenAI tools encourage passive consumption of information, which can impede the development of analytical skills and independent thinking.
Bias and Ethical Concerns
GenAI models inherit biases from their training data. Learners who are unaware of these biases may unknowingly internalize skewed perspectives, perpetuating stereotypes or misinformed worldviews. There are several studies researching how historical data influences common HR software to preferer candidates or decline candidates based on gender and religion.
Conclusion
We can summarize it pretty simple – GenAI is a Supplement, but not a Substitute.
While GenAI tools offer immense value in certain applications—such as drafting content, brainstorming ideas, or automating routine tasks—they are fundamentally unsuited as standalone tools for learning. Their lack of semantic understanding, reliance on probabilistic methods, and susceptibility to inaccuracies make them unreliable for acquiring knowledge or developing deep insights.
For learners, human guidance, structured educational materials, and critical engagement with information remain irreplaceable. GenAI can augment learning by acting as a supplementary tool for exploration, but the responsibility for vetting, contextualizing, and understanding information ultimately rests with the learner.
In an era dominated by GenAI, fostering critical thinking and an appreciation for the nuances of learning has never been more important.