diff --git a/src/ragas/dataset_schema.py b/src/ragas/dataset_schema.py
index 47d475493..7c0df3cfc 100644
--- a/src/ragas/dataset_schema.py
+++ b/src/ragas/dataset_schema.py
@@ -128,7 +128,7 @@ def validate_user_input(
         messages: t.List[t.Union[HumanMessage, AIMessage, ToolMessage]],
     ) -> t.List[t.Union[HumanMessage, AIMessage, ToolMessage]]:
         """Validates the user input messages."""
-        if not (
+        if not all(
             isinstance(m, (HumanMessage, AIMessage, ToolMessage)) for m in messages
         ):
             raise ValueError(
diff --git a/tests/unit/test_dataset_schema.py b/tests/unit/test_dataset_schema.py
index bcea4a67f..5a619c834 100644
--- a/tests/unit/test_dataset_schema.py
+++ b/tests/unit/test_dataset_schema.py
@@ -196,3 +196,31 @@ def test_evaluation_dataset_type():
 
     dataset = EvaluationDataset(samples=[multi_turn_sample])
     assert dataset.get_sample_type() == MultiTurnSample
+
+
+def test_multiturn_sample_validate_user_input_invalid_type():
+    """Test that MultiTurnSample validation correctly rejects invalid message types."""
+    from pydantic import ValidationError
+
+    with pytest.raises(ValidationError):
+        MultiTurnSample(
+            user_input=[
+                HumanMessage(content="Hello"),
+                "invalid_string",  # This should be rejected by Pydantic
+            ]
+        )
+
+
+def test_multiturn_sample_validate_user_input_valid_types():
+    """Test that MultiTurnSample validation accepts valid message types."""
+    from ragas.messages import AIMessage
+
+    sample = MultiTurnSample(
+        user_input=[
+            HumanMessage(content="Hello"),
+            AIMessage(content="Hi there"),
+        ]
+    )
+    assert len(sample.user_input) == 2
+    assert isinstance(sample.user_input[0], HumanMessage)
+    assert isinstance(sample.user_input[1], AIMessage)