Research in an AI-Saturated Era: Elevating Quantitative TEL Research with Generative AI

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Research in an AI-Saturated Era: Elevating Quantitative TEL Research with Generative AI 📅 Thursday 21/05 10:30-12:00h 📍 Workshop Space A 🔎 Needs Analysis PhD candidates in TEL often face a dual challenge: managing increasingly complex datasets while maintaining high standards of statistical rigor. Traditional quantitative methods can be time-consuming and

Speakers

Eyal Rabin
Eyal Rabin
The Open University of Israel & Holon Institute of Technology

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Research in an AI-Saturated Era: Elevating Quantitative TEL Research with Generative AI

📅 Thursday 21/05 10:30-12:00h
📍 Workshop Space A
🔎 Needs Analysis

PhD candidates in TEL often face a dual challenge: managing increasingly complex datasets while maintaining high standards of statistical rigor. Traditional quantitative methods can be time-consuming and prone to human error. The emergence of GenAI and specialized tools like ChatGPT, Gemini, and Julius.ai has revolutionized the research workflow, making both basic and advanced statistical analyses more accessible and intuitive than ever before.

This workshop addresses the urgent need for doctoral students to transition from traditional “manual” analysis to an AI-augmented methodology. It bridges the gap between data collection and insight generation, empowering researchers to perform sophisticated analyses—ranging from fundamental descriptive statistics to complex inferential models and interactive dashboards—that were previously technically demanding. By streamlining these processes, the workshop helps increase research efficiency and quality in the competitive TEL landscape.

📒 Session Description

The 90-minute session is designed as a hands-on “Research Lab”:

  • The AI-Quant Evolution: Brief intro on the shift from traditional statistical software to AI-augmented workflows (Theory & Ethics).
  • Prompt Engineering & Data Cleaning: Live demonstration of transforming a “messy” TEL dataset into an organized codebook and cleaned dataframe using GenAI.
  • Collaborative “Deep Dive”: Participants work in small groups using their own data. Tasks include running correlations, T-tests/ANOVAs, and generating APA-style result sections. Mentors will provide real-time feedback.
  • The “Ah-Ha!” Moment (Visualization): Creating interactive dashboards and visual analytics (e.g., Heatmaps, Network Graphs) to uncover hidden patterns.
  • Wrap-up & Validation: Final discussion on the “Human-in-the-loop” principle and ensuring statistical integrity.
💡 Learning Objectives

By the end of the workshop, participants will be able to:

  • Engineer Precision Prompts: Craft advanced prompts for statistical analysis, result interpretation, and APA-style reporting.
  • Automate Data Workflows: Use AI tools (ChatGPT-4o, Claude, and Julius.ai) for data cleaning, reverse-scaling, and codebook generation.
  • Design Interactive Visualizations: Generate dynamic dashboards and heatmaps to communicate complex TEL data findings.
  • Critically Validate AI Outputs: Identify and mitigate “hallucinations” in statistical results through cross-verification techniques.



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