diff --git a/openml/datasets/functions.py b/openml/datasets/functions.py
index 970331cf1..d32b1210b 100644
--- a/openml/datasets/functions.py
+++ b/openml/datasets/functions.py
@@ -577,13 +577,18 @@ def attributes_arff_from_df(df: pd.DataFrame) -> list[tuple[str, list[str] | str
             categories = df[column_name].cat.categories
             categories_dtype = pd.api.types.infer_dtype(categories)
             if categories_dtype not in ("string", "unicode"):
-                raise ValueError(
-                    f"The column '{column_name}' of the dataframe is of "
-                    "'category' dtype. Therefore, all values in "
-                    "this columns should be string. Please "
-                    "convert the entries which are not string. "
-                    f"Got {categories_dtype} dtype in this column.",
-                )
+                try:
+                    # [ENH] Attempt to convert categories to string automatically
+                    new_categories = categories.astype(str)
+                    df[column_name] = df[column_name].cat.rename_categories(new_categories)
+                    categories = df[column_name].cat.categories
+                except Exception:
+                    raise ValueError(
+                        f"The column '{column_name}' of the dataframe is of "
+                        "'category' dtype. Therefore, all values in "
+                        "this columns should be string. Automatic conversion failed. "
+                        f"Got {categories_dtype} dtype in this column.",
+                    )
             attributes_arff.append((column_name, categories.tolist()))
         elif column_dtype == "boolean":
             # boolean are encoded as categorical.
diff --git a/tests/test_datasets/test_dataset_functions.py b/tests/test_datasets/test_dataset_functions.py
index fae4a1c4e..ed74d77ca 100644
--- a/tests/test_datasets/test_dataset_functions.py
+++ b/tests/test_datasets/test_dataset_functions.py
@@ -663,7 +663,7 @@ def test_attributes_arff_from_df_numeric_column(self):
         ]
 
     def test_attributes_arff_from_df_mixed_dtype_categories(self):
-        # liac-arff imposed categorical attributes to be of sting dtype. We
+        # liac-arff imposed categorical attributes to be of string dtype. We
         # raise an error if this is not the case.
         df = pd.DataFrame([[1], ["2"], [3.0]])
         df[0] = df[0].astype("category")
@@ -671,6 +671,22 @@ def test_attributes_arff_from_df_mixed_dtype_categories(self):
         with pytest.raises(ValueError, match=err_msg):
             attributes_arff_from_df(df)
 
+    def test_attributes_arff_from_df_integer_categories(self):
+        df = pd.DataFrame({"category": [1, 2, 1]})
+        df["category"] = df["category"].astype("category")
+
+        attributes = attributes_arff_from_df(df)
+        assert attributes == [("category", ["1", "2"])]
+
+    def test_attributes_arff_from_df_missing_values(self):
+        df = pd.DataFrame({"category": [1, None, 2]})
+        df["category"] = df["category"].astype("category")
+
+        attributes = attributes_arff_from_df(df)
+
+        assert attributes == [("category", ["1", "2"])]
+        assert df["category"].isna().sum() == 1
+
     def test_attributes_arff_from_df_unknown_dtype(self):
         # check that an error is raised when the dtype is not supptagorted by
         # liac-arff