diff --git a/.Rbuildignore b/.Rbuildignore
index 11d3f73..44b7552 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -15,4 +15,4 @@
 ^registered_agents\.json$
 ^task_agent_mapping\.json$
 ^\.gitleaks\.toml$
-^\.jules$
+^\.jules(/.*)?$
diff --git a/.jules/bolt.md b/.jules/bolt.md
index 379e5cd..22d58d2 100644
--- a/.jules/bolt.md
+++ b/.jules/bolt.md
@@ -1,3 +1,7 @@
+## 2026-06-30 - Pre-calculate Theta Variables to Avoid Redundant MAP estimation
+**Learning:** `mirt::fscores(..., method = 'MAP')` is called redundantly multiple times in `R/aFIPC.R`. It's an expensive operation and avoiding duplicate calls by pre-calculating and reusing the variables significantly improves performance.
+**Action:** Always pre-calculate and reuse the resulting Theta variables rather than calling it redundantly.
+
 ## 2024-05-18 - R 언어에서 루프 내 정규식 탐색 병목 최적화
 **Learning:** R에서 데이터 프레임의 크기가 커질수록 루프 내에서 컬럼명을 추출하고 정규식을 이용해(`grep`) 문자열을 탐색하는 작업이 상당한 성능 오버헤드를 발생시킨다. 특히 O(N) 탐색을 루프 안에서 반복할 경우 O(N^2)의 비효율성을 초래한다.
 **Action:** 루프 내부에서 자주 호출되는 컬럼명이나 데이터 프레임 구조 탐색을 루프 밖으로 빼서 한 번만 계산하여 벡터로 저장하도록 한다. 정규식보다는 완전 일치 탐색(`%in%`, `match`)이 가능하도록 벡터 연산을 활용해 O(1) 수준으로 성능을 끌어올려야 한다.
diff --git a/R/aFIPC.R b/R/aFIPC.R
index d14dcaa..d48e55a 100644
--- a/R/aFIPC.R
+++ b/R/aFIPC.R
@@ -986,28 +986,28 @@ autoFIPC <-
     #   stop('Estimation failed. Please check test quality.')
     # }
 
+    # calculate theta
+    ThetaOldform <- fscores(oldFormModel, method = 'MAP')
+    ThetaLinkedform <- fscores(LinkedModel, method = 'MAP')
+    ThetaNewform <- fscores(newFormModel, method = 'MAP')
+
     # calculate expected score
     ExpectedScoreOldform <-
       mirt::expected.test(
         x = oldFormModel,
-        Theta = fscores(oldFormModel, method = 'MAP')
+        Theta = ThetaOldform
       )
     ExpectedScoreLinkedform <-
       mirt::expected.test(
         x = LinkedModel,
-        Theta = fscores(LinkedModel, method = 'MAP')
+        Theta = ThetaLinkedform
       )
     ExpectedScoreNewform <-
       mirt::expected.test(
         x = newFormModel,
-        Theta = fscores(newFormModel, method = 'MAP')
+        Theta = ThetaNewform
       )
 
-    # calculate theta
-    ThetaOldform <- fscores(oldFormModel, method = 'MAP')
-    ThetaLinkedform <- fscores(LinkedModel, method = 'MAP')
-    ThetaNewform <- fscores(newFormModel, method = 'MAP')
-
     # save results as object
     modelReturn <- new.env()
     modelReturn$oldFormModel <- oldFormModel
diff --git a/tests/testthat/test-package-api.R b/tests/testthat/test-package-api.R
index 3366f55..7f0ecf5 100644
--- a/tests/testthat/test-package-api.R
+++ b/tests/testthat/test-package-api.R
@@ -2,3 +2,41 @@ test_that("autoFIPC is exported", {
   expect_true("autoFIPC" %in% getNamespaceExports("aFIPC"))
   expect_true(is.function(aFIPC::autoFIPC))
 })
+
+test_that("autoFIPC executes without errors in non-interactive environment", {
+  skip_if_not_installed("mirt")
+
+  set.seed(123)
+  dat_old <- mirt::simdata(
+    a = matrix(runif(10, 0.8, 2)),
+    d = matrix(rnorm(10)),
+    N = 100,
+    itemtype = "2PL"
+  )
+  dat_new <- mirt::simdata(
+    a = matrix(runif(10, 0.8, 2)),
+    d = matrix(rnorm(10)),
+    N = 100,
+    itemtype = "2PL"
+  )
+
+  colnames(dat_old) <- paste0("Item", 1:10)
+  colnames(dat_new) <- c(paste0("Item", 1:5), paste0("NewItem", 6:10))
+
+  old_mod <- mirt::mirt(dat_old, 1, itemtype = "2PL", SE = FALSE, verbose = FALSE)
+  new_mod <- mirt::mirt(dat_new, 1, itemtype = "2PL", SE = FALSE, verbose = FALSE)
+
+  res <- aFIPC::autoFIPC(
+    newformXData = new_mod,
+    oldformYData = old_mod,
+    newformCommonItemNames = paste0("Item", 1:5),
+    oldformCommonItemNames = paste0("Item", 1:5),
+    itemtype = "2PL",
+    checkIPD = FALSE,
+    confirmCommonItems = TRUE
+  )
+
+  expect_type(res, "list")
+  expect_true("ExpectedScoreOldform" %in% names(res))
+  expect_true("ThetaOldform" %in% names(res))
+})