I will provide an example solution for the challenge description you provided, focusing on the AI-driven content generation aspect. We will use Python and an AI library named Transformers, developed by Hugging Face, which is widely used for natural language processing applications. This example will provide a solid foundation for AI-driven content generation and its integration into the educational content system.

Before proceeding, ensure you have installed the Transformers library with `pip install transformers`.

Here's the example solution:

File: [ai\_content\_generator.py](https://ai_content_generator.py)

```python
import random
import torch
from transformers import pipeline

class AIContentGenerator:
    """Class for AI-driven content generation for educational purposes."""

    def __init__(self, model_name: str = "distilbert-base-uncased"):
        """Initialize the class instance with the selected pre-trained model.

        :param model_name: Name of the pre-trained model to use for AI content generation.
            Defaults to `distilbert-base-uncased`.
        """
        self.content_generator = pipeline("text-generation", model=model_name)

    def generate_content(
            self,
            input_text: str,
            max_length: int = 50,
            num_return_sequences: int = 1,
            do_sample: bool = True,
            temperature: float = 1,
            top_k: int = 50,
            top_p: float = 0.95,
            repetition_penalty: float = 1.2,
            **kwargs,
    ) -> str:
        """Generate content using the AI language model.

        :param input_text: The initial text that the content generation will be based on.
        :param max_length: The maximum length of the generated text. Defaults to 50.
        :param num_return_sequences: Number of random sequences to generate.
            Defaults to 1.
        :param do_sample: Whether to sample from the model's probability distribution.
            Defaults to True.
        :param temperature: Controls the randomness of the sampling process.
            Higher values will result in more random output. Defaults to 1.
        :param top_k: Only consider the top k most likely tokens during sampling.
            Defaults to 50.
        :param top_p: Only consider the smallest set of top-p most likely tokens
            during sampling. Defaults to 0.95.
        :param repetition_penalty: A penalty for generating the same word multiple times.
            Defaults to 1.2.
        :param kwargs: Additional keyword arguments that might be used for future extensions.
        :return: The generated content based on the input text and other parameters.
        """
        response = self.content_generator(
            input_text,
            max_length=max_length,
            num_return_sequences=num_return_sequences,
            do_sample=do_sample,
            temperature=temperature,
            top_k=top_k,
            top_p=top_p,
            repetition_penalty=repetition_penalty,
            **kwargs,
        )
        return response[0]["generated_text"]
```

File: [educational\_content.py](https://educational_content.py)

```python
from typing import List
from .ai_content_generator import AIContentGenerator

class EducationalContent:
    """Class to represent educational content."""

    def __init__(self, language_model: AIContentGenerator):
        """Initialize educational content.

        :param language_model: AI language model for content generation.
        """
        self.language_model = language_model
        self.content_pieces = []

    def add_content(self, text: str, **kwargs):
        """Add a piece of textual content.

        :param text: The text to be added.
        :param kwargs: Additional parameters that can be used for customizing
            the content generation when text is generated by the content generator.
            These parameters will be passed to the AIContentGenerator.
        """
        content_piece = {
            "text": text,
            "parameters": kwargs,
        }
        self.content_pieces.append(content_piece)

    def generate_content(self, input_text: str, **kwargs):
        """Generate and add new content.

        :param input_text: The initial text that the content generation will be based on.
        :param kwargs: Additional keyword arguments for AI content generation.
        """
        generated_text = self.language_model.generate_content(
            input_text, **kwargs
        )
        self.add_content(generated_text)

# Usage
if __name__ == '__main__':
    ai_content_generator = AIContentGenerator()
    educational_content = EducationalContent(ai_content_generator)

    # Add content manually
    educational_content.add_content('Aardvarks are little-known creatures.')

    # Generate content using the AI language model
    example_initial_text = "What are Aardvarks?"
    educational_content.generate_content(example_initial_text, max_length=100)
    
    # Print generated content
    for content_piece in educational_content.content_pieces:
        print(content_piece['text'])
```

With this AI-driven content generation component in place, the educational content system can efficiently produce content in a flexible manner. The `generate_content()` method in the `EducationalContent` class can be used to generate new content based on the given parameters. Moreover, users can add content manually via the `add_content()` method, in case there is a need to include existing content.

The provided code example showcases a robust AI-driven content generation implementation using the Transformers library. Adapting this implementation as a foundation for more specific use cases or advanced features can lead to an exceptional, cutting-edge solution for AI-driven educational content systems.