diff --git a/avalanche/training/plugins/bic.py b/avalanche/training/plugins/bic.py
index aa7a142d2..61535fd37 100644
--- a/avalanche/training/plugins/bic.py
+++ b/avalanche/training/plugins/bic.py
@@ -2,10 +2,12 @@
 from typing import (
     Dict,
     List,
+    Literal,
     Optional,
     Sequence,
     Set,
     SupportsInt,
+    Union,
 )
 
 from copy import deepcopy
@@ -15,11 +17,9 @@
 from torch.utils.data import DataLoader
 from torch.optim.lr_scheduler import MultiStepLR
 
-from avalanche.benchmarks.utils import (
-    _concat_taskaware_classification_datasets,
-)
 from avalanche.benchmarks.utils.data import AvalancheDataset
 from avalanche.benchmarks.utils.data_loader import ReplayDataLoader
+from avalanche.benchmarks.utils.utils import concat_datasets
 from avalanche.training.plugins.strategy_plugin import SupervisedPlugin
 from avalanche.training.storage_policy import (
     ExemplarsBuffer,
@@ -57,7 +57,7 @@ def __init__(
         stage_2_epochs: int = 200,
         lamb: float = -1,
         lr: float = 0.1,
-        num_workers: int = 4,
+        num_workers: Union[int, Literal["as_strategy"]] = "as_strategy",
         verbose: bool = False,
     ):
         """
@@ -84,7 +84,9 @@ def __init__(
                 loss and the classification loss.
         :param lr: hyperparameter used as a learning rate for
                 the second phase of training.
-        :param num_workers: number of workers using during stage 2 data loading
+        :param num_workers: number of workers using during stage 2 data loading.
+            Defaults to "as_strategy", which means that the number of workers
+            will be the same as the one used by the strategy.
         :param verbose: if True, prints additional info regarding the stage 2 stage
         """
 
@@ -110,7 +112,7 @@ def __init__(
         self.lamb = lamb
         self.mem_size = mem_size
         self.lr = lr
-        self.num_workers = num_workers
+        self.num_workers: Union[int, Literal["as_strategy"]] = num_workers
 
         self.seen_classes: Set[int] = set()
         self.class_to_tasks: Dict[int, int] = {}
@@ -168,9 +170,7 @@ def before_train_dataset_adaptation(self, strategy: "SupervisedTemplate", **kwar
         for class_id, class_buf in self.val_buffer.items():
             class_buf.resize(strategy, class_to_len[class_id])
 
-        strategy.experience.dataset = _concat_taskaware_classification_datasets(
-            train_data
-        )
+        strategy.experience.dataset = concat_datasets(train_data)
 
     def before_training_exp(
         self,
@@ -266,8 +266,19 @@ def after_training_exp(self, strategy, **kwargs):
         self.storage_policy.update(strategy, **kwargs)
 
         if task_id > 0:
+            num_workers = (
+                int(kwargs.get("num_workers", 0))
+                if self.num_workers == "as_strategy"
+                else self.num_workers
+            )
+            persistent_workers = (
+                False if num_workers == 0 else kwargs.get("persistent_workers", False)
+            )
+
             self.bias_correction_step(
-                strategy, persistent_workers=kwargs.get("persistent_workers", False)
+                strategy,
+                persistent_workers=persistent_workers,
+                num_workers=num_workers,
             )
 
     def cross_entropy(self, new_outputs, old_outputs):
@@ -334,13 +345,15 @@ def make_distillation_loss(self, strategy):
 
         # Union of initial_classes and previous_classes: needed to select the logits of all the old classes
         old_clss: List[int] = sorted(set(initial_classes) | set(previous_classes))
-        # print('old', old_clss)
 
         # Distillation loss on the logits of the old classes
         return self.cross_entropy(out_new[:, old_clss], out_old[:, old_clss])
 
     def bias_correction_step(
-        self, strategy: SupervisedTemplate, persistent_workers: bool = False
+        self,
+        strategy: SupervisedTemplate,
+        persistent_workers: bool = False,
+        num_workers: int = 0,
     ):
         # --- Prepare the models ---
         # Freeze the base model, only train the new bias layer
@@ -372,17 +385,17 @@ def bias_correction_step(
         )  # type: ignore
 
         # --- Prepare the dataloader for the validation set ---
-        list_subsets = []
+        list_subsets: List[AvalancheDataset] = []
         for _, class_buf in self.val_buffer.items():
             list_subsets.append(class_buf.buffer)
 
-        stage_set = _concat_taskaware_classification_datasets(list_subsets)
+        stage_set = concat_datasets(list_subsets)
         stage_loader = DataLoader(
             stage_set,
             batch_size=strategy.train_mb_size,
             shuffle=True,
-            num_workers=self.num_workers,
-            persistent_workers=persistent_workers if self.num_workers > 0 else False,
+            num_workers=num_workers,
+            persistent_workers=persistent_workers,
         )
 
         # Loop epochs