diff --git a/src/awkward/operations/ak_mean.py b/src/awkward/operations/ak_mean.py
index a9b38ce1f0..3b6552c521 100644
--- a/src/awkward/operations/ak_mean.py
+++ b/src/awkward/operations/ak_mean.py
@@ -225,8 +225,8 @@ def _impl(x, weight, axis, keepdims, mask_identity, highlevel, behavior, attrs):
             sumw = ak.operations.ak_sum._impl(
                 x * 0 + weight,
                 axis,
-                keepdims,
-                mask_identity,
+                keepdims=True,
+                mask_identity=True,
                 highlevel=True,
                 behavior=ctx.behavior,
                 attrs=ctx.attrs,
diff --git a/tests/test_3285_ak_mean_weighted_row_wise.py b/tests/test_3285_ak_mean_weighted_row_wise.py
new file mode 100644
index 0000000000..2c1bc1db9b
--- /dev/null
+++ b/tests/test_3285_ak_mean_weighted_row_wise.py
@@ -0,0 +1,65 @@
+# BSD 3-Clause License; see https://github.com/scikit-hep/awkward/blob/main/LICENSE
+
+from __future__ import annotations
+
+import math
+
+import pytest
+
+import awkward as ak
+
+
+@pytest.mark.parametrize(
+    "keepdims, expected_result",
+    [
+        pytest.param(False, ak.Array([2.25, 6.5])),
+        pytest.param(True, ak.Array([[2.25], [6.5]])),
+    ],
+)
+def test_keepdims(keepdims: bool, expected_result: ak.Array):
+    data = ak.Array(
+        [
+            [1, 2, 3],
+            [4, 7],
+        ]
+    )
+    weight = ak.Array(
+        [
+            [1, 1, 2],
+            [1, 5],
+        ]
+    )
+    assert ak.all(
+        ak.mean(data, weight=weight, axis=1, keepdims=keepdims) == expected_result
+    )
+
+
+@pytest.mark.parametrize(
+    "mask_identity, expected_result",
+    [
+        pytest.param(False, ak.Array([1.5, math.nan, 8])),
+        pytest.param(True, ak.Array([1.5, None, 8])),
+    ],
+)
+def test_mask_identity(mask_identity: bool, expected_result: ak.Array):
+    data = ak.Array(
+        [
+            [1, 2],
+            [],
+            [6, 9],
+        ]
+    )
+    weight = ak.Array(
+        [
+            [1, 1],
+            [],
+            [1, 2],
+        ]
+    )
+    result = ak.mean(data, weight=weight, axis=1, mask_identity=mask_identity)
+    assert result[0] == expected_result[0]
+    if mask_identity:
+        assert result[1] is None
+    else:
+        assert math.isnan(result[1])  # NaN is not equal to itself per IEEE!
+    assert result[2] == expected_result[2]