AIGRA

AIGRA stands for Agentic Item Generation, Review, and Analysis.

AIGRA is an R package interface to a Python backend for generating, solving, reviewing, and exporting assessment items using retrieval-augmented multi-agent AI workflows.

Core workflow

AIGRA supports the following pipeline:

1. Parse source assessment items from a PDF, CSV, or Excel item bank.
2. Build a retrieval-augmented item store.
3. Retrieve similar examples by topic, difficulty, objective, and style.
4. Generate new clone items in a target language.
5. Solve generated items independently.
6. Review generated items with a critic agent.
7. Apply rule-based safety checks.
8. Export results to CSV and JSONL.
9. Summarise, plot, and report output quality.

Backend

The current Python backend lives at:

"C:/Users/OMOPEKUNOLA MOSES O/AIGRA_BACKEND"

Development setup

devtools::load_all()

aigra_set_backend(
  backend_path = "C:/Users/OMOPEKUNOLA MOSES O/AIGRA_BACKEND"
)

aigra_status()

Ensure Python backend

ensure_aigra_python(
  backend_path = "C:/Users/OMOPEKUNOLA MOSES O/AIGRA_BACKEND"
)

aigra_python_info(
  backend_path = "C:/Users/OMOPEKUNOLA MOSES O/AIGRA_BACKEND"
)

Parse PDF source items

items <- aigra_parse_items(
  source_language = "Russian",
  subject = "Physics",
  exam = "Kazakhstan UNT"
)

nrow(items)
head(items)

Create CSV or Excel item-bank templates

template <- aigra_template_items()
template

csv_path <- aigra_write_template_csv(
  file = "C:/Users/OMOPEKUNOLA MOSES O/AIGRA_BACKEND/data/aigra_item_template.csv",
  overwrite = TRUE
)

xlsx_path <- aigra_write_template_excel(
  file = "C:/Users/OMOPEKUNOLA MOSES O/AIGRA_BACKEND/data/aigra_item_template.xlsx",
  overwrite = TRUE
)

Required tabular item-bank columns

CSV and Excel item banks should include these columns:

• item_id
• stem
• option_A
• option_B
• option_C
• option_D
• correct_answer
• difficulty
• grade
• section
• topic
• objective
• source_language
• subject
• exam

Required minimum columns are:

• stem
• option_A
• option_B
• option_C
• option_D
• correct_answer

Parse CSV or Excel item banks

tab_items <- aigra_parse_tabular_items(
  file_path = "C:/Users/OMOPEKUNOLA MOSES O/AIGRA_BACKEND/data/aigra_item_template.csv",
  source_language = "English",
  subject = "Physics",
  exam = "Demo Item Bank"
)

nrow(tab_items)
head(tab_items)

Generate from PDF

Set your provider API key first.

Sys.setenv(OPENAI_API_KEY = "your_openai_key_here")

result <- aigra_generate_items(
  provider = "openai",
  model = "gpt-4.1",
  target_language = "English",
  n_clones = 1,
  max_items = 1
)

View(result)

Generate from CSV or Excel

result <- aigra_generate_tabular_items(
  file_path = "C:/Users/OMOPEKUNOLA MOSES O/AIGRA_BACKEND/data/aigra_item_template.csv",
  provider = "openai",
  model = "gpt-4.1",
  source_language = "English",
  target_language = "English",
  subject = "Physics",
  exam = "Demo Item Bank",
  n_clones = 1,
  max_items = 1
)

View(result)

Supported providers

AIGRA currently supports:

• OpenAI
• Gemini
• Groq
• Anthropic/Claude, through the provider-flexible backend client

Example provider keys:

Sys.setenv(OPENAI_API_KEY = "your_openai_key_here")
Sys.setenv(GEMINI_API_KEY = "your_gemini_key_here")
Sys.setenv(GROQ_API_KEY = "your_groq_key_here")
Sys.setenv(ANTHROPIC_API_KEY = "your_anthropic_key_here")

Read outputs

aigra_list_outputs()

latest <- aigra_read_latest_output()
View(latest)

Summarise output quality

aigra_print_summary()
aigra_plot_summary()

report_path <- aigra_write_report()
browseURL(report_path)

Development status

AIGRA is currently a research prototype. Generated assessment items should be reviewed by subject matter experts before operational use.