 ```python
import torch
from avalanche.training.strategies import Naive
from datasets import load_dataset
from transformers import T5ForConditionalGeneration, T5Tokenizer

class QAStrategy(Naive):
    def __init__(self, model, tokenizer, **kwargs):
        super().__init__(**kwargs)
        self.model = model
        self.tokenizer = tokenizer

    def forward(self, experience):
        inputs = self.tokenizer(experience.datasets[0], padding="longest", truncation=True, return_tensors="pt")
        inputs["input_ids"] = inputs["input_ids"].squeeze()
        inputs["attention_mask"] = inputs["attention_mask"].squeeze()
        outputs = self.model(**inputs)
        logits = outputs.logits
        predictions = torch.argmax(logits, dim=-1)
        return predictions

def main():
    # Load SQuAD dataset
    dataset = load_dataset("squad", split="validation")

    # Preprocess the dataset
    def preprocess_function(examples):
        return {"input_ids": examples["context_tokens"], "attention_mask": examples["context_lengths"]}

    processed_dataset = dataset.map(preprocess_function, batched=True)

    # Split dataset into training and validation sets
    train_size = int(0.8 * len(processed_dataset))
    val_size = len(processed_dataset) - train_size
    train_dataset, val_dataset = processed_dataset.train_test_split(train_size, val_size)

    # Initialize T5 model and tokenizer
    model = T5ForConditionalGeneration.from_pretrained("t5-base")
    tokenizer = T5Tokenizer.from_pretrained("t5-base")

    # Set up continual learning scenario
    strategy = QAStrategy(model=model, tokenizer=tokenizer)

    # Train the model
    strategy.train(train_dataset, valid_dataset=val_dataset, n_epochs=1, eval_after_train=True)

    # Test the model
    question = "Who was the first president of the United States?"
    input_ids = tokenizer(question, return_tensors="pt").input_ids
    start_scores, end_scores = model(input_ids)[0][:, :, :len(tokenizer.encode(question)) + 1]
    start_index = torch.argmax(start_scores)
    end_index = torch.argmax(end_scores)
    answer_tokens = input_ids[0][0][start_index:end_index + 1]
    answer = tokenizer.decode(answer_tokens)
    print(f"The model's answer: {answer}")

if __name__ == "__main__":
    main()
```