diff --git a/NEWS.md b/NEWS.md
index 3539917c..8ed86d81 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -4,6 +4,10 @@
 
 * `vfold_cv()` now utilizes the `breaks` argument correctly for repeated cross-validation (@ZWael, #471).
 
+## Breaking changes
+
+* The class of grouped MC splits is now `group_mc_split` instead of `grouped_mc_split`, aligning it with the other grouped splits (#478).
+
 * The `rsplit` objects of an `apparent()` split now have the correct class inheritance structure. The order is now `apparent_split` and then `rsplit` rather than the other way around (#477).
 
 
diff --git a/R/mc.R b/R/mc.R
index bb172746..198f90ba 100644
--- a/R/mc.R
+++ b/R/mc.R
@@ -239,7 +239,7 @@ group_mc_splits <- function(data, group, prop = 3 / 4, times = 25, strata = NULL
       indices,
       make_splits,
       data = data,
-      class = c("grouped_mc_split", "mc_split")
+      class = c("group_mc_split", "mc_split")
     )
 
   all_assessable <- purrr::map(split_objs, function(x) nrow(assessment(x)))
diff --git a/tests/testthat/test-initial.R b/tests/testthat/test-initial.R
index b513a4fc..bf7ccf96 100644
--- a/tests/testthat/test-initial.R
+++ b/tests/testthat/test-initial.R
@@ -45,7 +45,7 @@ test_that("default time param with lag", {
 
 test_that("default group param", {
   rs1 <- group_initial_split(dat1, c)
-  expect_equal(class(rs1), c("group_initial_split", "initial_split", "grouped_mc_split", "mc_split", "rsplit"))
+  expect_equal(class(rs1), c("group_initial_split", "initial_split", "group_mc_split", "mc_split", "rsplit"))
   tr1 <- training(rs1)
   ts1 <- testing(rs1)
   expect_equal(nrow(tr1), nrow(dat1) * 3 / 4)
@@ -103,7 +103,7 @@ test_that("grouping -- strata", {
 
 test_that("`prop` computes the proportion for group analysis", {
   rs1 <- group_initial_split(dat1, c, prop = 1 / 2)
-  expect_equal(class(rs1), c("group_initial_split", "initial_split", "grouped_mc_split", "mc_split", "rsplit"))
+  expect_equal(class(rs1), c("group_initial_split", "initial_split", "group_mc_split", "mc_split", "rsplit"))
   tr1 <- training(rs1)
   ts1 <- testing(rs1)
   expect_equal(nrow(tr1), nrow(dat1) * 1 / 2)