diff --git a/README.md b/README.md
index 40efa05..fdc9885 100644
--- a/README.md
+++ b/README.md
@@ -12,8 +12,8 @@ repo and are served by GitHub Pages:
 
 Data files:
 
-- `data/matrix.csv` — coarse matrix (114 topics × courses). Columns: `Category, Topic, <course…>`
-- `data/granular.csv` — granular matrix (277 sub-topics). Columns: `Category, ID, Sub-topic, <course…>`
+- `data/matrix.csv` — coarse matrix (120 topics × courses). Columns: `Category, Topic, <course…>`
+- `data/granular.csv` — granular matrix (284 sub-topics). Columns: `Category, ID, Sub-topic, <course…>`
 
 Cell values: blank (not covered) · `I` introduced · `D` developed · `E` emphasized.
 A GitHub Action (`.github/workflows/validate-csv.yml`) validates every PR: rectangular CSV,
diff --git a/config.json b/config.json
index 2e3abd7..aa8f19c 100644
--- a/config.json
+++ b/config.json
@@ -12,13 +12,13 @@
   "datasets": [
     {
       "id": "coarse",
-      "label": "Coarse matrix (114 topics)",
+      "label": "Coarse matrix (120 topics)",
       "path": "data/matrix.csv",
       "keyCols": 2
     },
     {
       "id": "granular",
-      "label": "Granular matrix (277 sub-topics)",
+      "label": "Granular matrix (284 sub-topics)",
       "path": "data/granular.csv",
       "keyCols": 3
     }
diff --git a/data/granular.csv b/data/granular.csv
index aa657ba..6f588e6 100644
--- a/data/granular.csv
+++ b/data/granular.csv
@@ -24,6 +24,7 @@ C2. Computing environment and developer tools,C2.9,Jupyter,,,I,E,E,D,,,E,D,,I,,D
 C2. Computing environment and developer tools,C2.10,Google Colab,,,I,I,I,,,,I,D,,,,,,,E,,,,,,,,,,,,,
 C2. Computing environment and developer tools,C2.11,WSL,,,,D,I,,,,,,,,,,,,,,,,,,,,,,,,,
 C2. Computing environment and developer tools,C2.12,repositories and reproducible handoff of code,,,,I,,,,,,,,I,I,I,,,E,,D,,D,,,,,D,,,,
+C2. Computing environment and developer tools,C2.13,GPU and parallel computing,,,,,,,,,,,,,,,,,,,,,,,,,,D,,,,
 C3. Python programming and software construction,C3.1,variables and types,,,,E,D,,,,,,,,,E,,,,,,,,,,,,,,,,
 C3. Python programming and software construction,C3.2,syntax,,,,E,D,,,,,,,,,E,,,,,,,,,,,,,,,,
 C3. Python programming and software construction,C3.3,conditional statements,,,,E,E,,,,,,,,,E,,,,,,,,,,,,,,,,
@@ -154,6 +155,7 @@ C9. Machine learning foundations and classical supervised learning,C9.17,K-neare
 C9. Machine learning foundations and classical supervised learning,C9.18,SVM,I,,,I,,,,,,,,,,I,,,,,,,D,,,,,,D,D,,
 C9. Machine learning foundations and classical supervised learning,C9.19,baseline models,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 C9. Machine learning foundations and classical supervised learning,C9.20,demand prediction,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+C9. Machine learning foundations and classical supervised learning,C9.25,kernel methods and the kernel trick,,,,,,,,,,,,,,,,,,,,,D,,,D,,,,,,
 C9. Machine learning foundations and classical supervised learning,C9.21,decision trees,I,,,,,E,,,D,,,I,,I,,,,,,E,,,,,,,D,D,,
 C9. Machine learning foundations and classical supervised learning,C9.22,"ensembles (bagging, random forest, boosting)",,,,,,D,,,D,D,,I,,I,,,I,,,,,,,,,,D,D,,
 C9. Machine learning foundations and classical supervised learning,C9.23,discriminant analysis (LDA/QDA),,,,,D,,,,,,,,,,E,,,,,,,,,,,,,,,
@@ -276,3 +278,8 @@ C13. Computer vision and image analysis,C13.19,confusion matrix–style evaluati
 "C15. Capstone, research, communication, and career-readiness rows",C15.23,LeetCode-style practice,,,,,,,,,,,I,,,I,,,,,,,,,,,,,,,,
 "C15. Capstone, research, communication, and career-readiness rows",C15.24,SQL interview questions,,,,,,,,,,,I,,,I,,,,,,,,,,,,,,,,
 "C15. Capstone, research, communication, and career-readiness rows",C15.25,AI-assisted mock interviews,,,,,,,,,,,I,,,,,,,,,,,,,,,,,,,
+"C16. Modern AI, large language models, and agentic systems",C16.1,large language models,,,,,,,,,,,,,,,,,,,,,,,,,,,,,D,
+"C16. Modern AI, large language models, and agentic systems",C16.2,prompt engineering,,,,,,,,,,,,,,,,,,,,,,,,,,,,,D,
+"C16. Modern AI, large language models, and agentic systems",C16.3,retrieval-augmented generation (RAG),,,,,,,,,,,,,,,,,,,,,,,,,,,,,D,
+"C16. Modern AI, large language models, and agentic systems",C16.4,agentic AI systems,,,,,,,,,,,,,,,,,,,,,,,,,,,,,D,
+"C16. Modern AI, large language models, and agentic systems",C16.5,graph neural networks,,,,,,,,,,,,,,,,,,,,,,,,D,,,,,D,
diff --git a/data/matrix.csv b/data/matrix.csv
index e5f1480..2f4504c 100644
--- a/data/matrix.csv
+++ b/data/matrix.csv
@@ -11,6 +11,7 @@ Category,Topic,DATA 1301,DATA 2301,DATA 3311,DATA 3401,DATA 3402,DATA 3421,DATA
 2. Computing Environment & Developer Tools,Version control: Git / GitHub,D,,,E,D,,,,,,I,I,,,,,D,,,,,,,,,D,,,,
 2. Computing Environment & Developer Tools,Jupyter / Google Colab,,,I,E,E,D,,,E,D,,I,,D,,,E,,,,,D,,,,,,,,
 2. Computing Environment & Developer Tools,Reproducibility & code handoff,,,,I,,,,,,,,I,I,I,,,D,,D,,D,,,,,D,,,,
+2. Computing Environment & Developer Tools,GPU / parallel computing & hardware acceleration,,,,,,,,,,,,,,,,,,,,,,,,,,D,,,,
 3. Python Programming & Software Construction,"Variables, types, syntax, operators",,,,E,D,,,,,,,,,E,,,,,,,,,,,,,,,,
 3. Python Programming & Software Construction,Conditionals & loops / control flow,,,,E,E,,,,,,,,,E,,,,,,,,,,,,,,,,
 3. Python Programming & Software Construction,"Functions (args, recursion, scope)",,,,E,E,,,,,,,,,E,,,,,,,,,,,,,,,,
@@ -76,6 +77,7 @@ Category,Topic,DATA 1301,DATA 2301,DATA 3311,DATA 3401,DATA 3402,DATA 3421,DATA
 9. ML Foundations & Classical Supervised Learning,K-Nearest Neighbors,D,,,I,,D,,,,,,,,D,,,,,,D,,,,,,,,,,
 9. ML Foundations & Classical Supervised Learning,Decision trees,I,,,,,E,,,D,,,I,,I,,,,,,E,,,,,,,D,D,,
 9. ML Foundations & Classical Supervised Learning,Naive Bayes / SVM,I,,,I,,,,,,,,,,I,,,,,,D,D,,,,,,D,D,,
+9. ML Foundations & Classical Supervised Learning,"Kernel methods (kernel trick, kernelized models)",,,,,,,,,,,,,,,,,,,,,D,,,D,,,,,,
 9. ML Foundations & Classical Supervised Learning,Discriminant analysis (LDA/QDA),,,,,D,,,,,,,,,,E,,,,,,,,,,,,,,,
 9. ML Foundations & Classical Supervised Learning,"Ensembles (bagging, random forest, boosting)",,,,,,D,,,D,D,,I,,I,,,I,,,,,,,,,,D,D,,
 9. ML Foundations & Classical Supervised Learning,Regularization (Ridge/Lasso),,,,,,,,,D,I,,,,,,,D,,D,,E,I,,,,,D,D,D,
@@ -113,3 +115,7 @@ Category,Topic,DATA 1301,DATA 2301,DATA 3311,DATA 3401,DATA 3402,DATA 3421,DATA
 "15. Capstone, Communication & Career Readiness",Reproducibility & repo quality,,,,,,,,,,,I,D,,I,,,E,,,D,D,,I,,,,,,,
 "15. Capstone, Communication & Career Readiness",Resume / cover letter / LinkedIn,,,,,,,,,,,D,I,,,,,,,,,,,,,,,,,,
 "15. Capstone, Communication & Career Readiness","Technical & behavioral interview prep (STAR, LeetCode, SQL)",,,,,,,,,,,D,,,I,,,,,,,,,,,,,,,,
+"16. Modern AI, LLMs & Agentic Systems",Large language models (LLMs) & prompt engineering,,,,,,,,,,,,,,,,,,,,,,,,,,,,,D,
+"16. Modern AI, LLMs & Agentic Systems",Retrieval-augmented generation (RAG),,,,,,,,,,,,,,,,,,,,,,,,,,,,,D,
+"16. Modern AI, LLMs & Agentic Systems",Agentic AI systems,,,,,,,,,,,,,,,,,,,,,,,,,,,,,D,
+"16. Modern AI, LLMs & Agentic Systems",Graph neural networks / graph-based learning,,,,,,,,,,,,,,,,,,,,,,,,D,,,,,D,