Chapter 5 Cloud-based LLMs for Educational Research

Overview

Large Language Models (LLMs) available through cloud APIs provide powerful text analysis, generation, and reasoning capabilities that can be applied to educational research.
This chapter introduces how researchers can connect to commercial or open LLM services — such as OpenAI GPT models, Anthropic Claude, Google Gemini, and Mistral — to automate data processing, assist qualitative coding, and explore mixed-methods analyses.

We will discuss: - Available cloud-based LLM APIs and their access mechanisms
- R-based workflows for sending prompts and receiving model outputs
- Example use cases in educational research contexts
- Considerations around privacy, ethics, and reproducibility

1. Available Cloud-based LLM APIs

Provider Example Models Access R or HTTP Wrapper
OpenAI GPT-4o, GPT-4-Turbo API key from platform.openai.com {httr2}, {openai}
Anthropic Claude 3, Claude 3.5 API key from console.anthropic.com {httr2}, {anthropic}
Google Gemini 1.5 AI Studio {googleGenerativeAI}
Mistral Mixtral, Mistral 7B mistral.ai HTTP calls via {httr2}

💡 Tip: Use .Renviron to store API keys securely. Avoid embedding them in public code repositories.

2. Connecting to an API in R

Example using the OpenAI API and the {httr2} package:

library(httr2)
library(jsonlite)

# Prepare request
resp <- request("https://api.openai.com/v1/chat/completions") |>
  req_headers(
    "Authorization" = paste("Bearer", Sys.getenv("OPENAI_API_KEY")),
    "Content-Type" = "application/json"
  ) |>
  req_body_json(list(
    model = "gpt-4o-mini",
    messages = list(
      list(role = "system", content = "You are an assistant helping with educational data analysis."),
      list(role = "user", content = "Summarize these student reflections in three themes.")
    )
  )) |>
  req_perform()

# Parse and print
content <- resp_body_json(resp)
cat(content$choices[[1]]$message$content)

3. Example: Automating Qualitative Coding

LLMs can support qualitative analysis by:

  • Suggesting initial codes or categories from open-ended responses
  • Clustering semantically similar responses
  • Generating summaries of student feedback
# Example: clustering reflective comments
responses <- c(
  "I learned how to write better R code.",
  "Collaborating with peers improved my understanding.",
  "I struggled with data visualization."
)

prompt <- paste("Cluster these reflections into themes:\n", paste(responses, collapse = "\n- "))

# Send to API (pseudocode)
# result <- openai::create_chat_completion(model="gpt-4o-mini", messages=list(...))

4. Reproducibility and Ethical Considerations

When using cloud-based LLMs:

  • Reproducibility: Model versions change frequently; document the model name, date, and API parameters.
  • Data Privacy: Avoid uploading identifiable student data. De-identify text prior to sending.
  • Cost and Rate Limits: Be aware of token-based billing; batch requests when possible.
  • Ethics and Transparency: Report clearly how LLMs were used in data analysis and interpretation.

5. Extending to Other APIs

You can explore:

  • {anthropic} or {claude} packages for Claude models
  • {googleGenerativeAI} for Gemini API
  • {mistralapi} for open-weight models hosted on Hugging Face or Mistral Cloud

Each provides similar request structures and response handling via JSON.

Summary

Cloud-based LLMs expand the researcher’s analytical toolkit, enabling scalable and automated text analysis in educational contexts.

By integrating these APIs with R-based workflows, researchers can:

  • Efficiently analyze large qualitative datasets
  • Prototype computational methods quickly
  • Maintain transparency through code-based documentation

In the next chapter, we will shift our focus to local LLMs — exploring how models can be deployed and fine-tuned entirely offline for educational research.