diff --git a/keras_core/trainers/epoch_iterator.py b/keras_core/trainers/epoch_iterator.py
index 397e3cff7..4f40cf9c3 100644
--- a/keras_core/trainers/epoch_iterator.py
+++ b/keras_core/trainers/epoch_iterator.py
@@ -42,6 +42,7 @@ import types
 import warnings
 
 import tensorflow as tf
+from tensorflow import nest
 
 from keras_core.trainers.data_adapters import array_data_adapter
 from keras_core.trainers.data_adapters import data_adapter_utils
@@ -67,7 +68,8 @@ class EpochIterator:
         if steps_per_epoch:
             self._current_iterator = None
             self._insufficient_data = False
-        if isinstance(x, data_adapter_utils.ARRAY_TYPES):
+        first_element = next(iter(nest.flatten(x)), None)
+        if isinstance(first_element, data_adapter_utils.ARRAY_TYPES):
             self.data_adapter = array_data_adapter.ArrayDataAdapter(
                 x,
                 y,
diff --git a/keras_core/trainers/trainer_test.py b/keras_core/trainers/trainer_test.py
index 073f52fe0..f682ee04e 100644
--- a/keras_core/trainers/trainer_test.py
+++ b/keras_core/trainers/trainer_test.py
@@ -29,7 +29,7 @@ class ExampleModel(layers.Dense, Trainer):
         Trainer.__init__(self)
 
 
-class OutputStructModel(layers.Layer, Trainer):
+class StructModel(layers.Layer, Trainer):
     def __init__(self, units):
         layers.Layer.__init__(self)
         Trainer.__init__(self)
@@ -46,8 +46,8 @@ class OutputStructModel(layers.Layer, Trainer):
 
     def call(self, x):
         return {
-            "y_one": self.dense_1(x),
-            "y_two": self.dense_2(x),
+            "y_one": self.dense_1(x["x_one"]),
+            "y_two": self.dense_2(x["x_two"]),
         }
 
 
@@ -193,12 +193,15 @@ class TestTrainer(testing.TestCase):
         outputs = model.predict(x, batch_size=batch_size)
         self.assertAllClose(outputs, 4 * np.ones((100, 3)))
 
-        # Test with output struct
-        model = OutputStructModel(units=3)
+        # Test with input/output structs
+        model = StructModel(units=3)
         model.run_eagerly = run_eagerly
         model.jit_compile = jit_compile
 
-        x = np.ones((100, 4))
+        x = {
+            "x_one": np.ones((100, 4)),
+            "x_two": np.ones((100, 4)),
+        }
         batch_size = 16
         outputs = model.predict(x, batch_size=batch_size)
         self.assertTrue(isinstance(outputs, dict))