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Related Experiment Video

Updated: Jun 7, 2025

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
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A generative AI-driven interactive listening assessment task.

Andrew Runge1, Yigal Attali1, Geoffrey T LaFlair1

  • 1Duolingo, Pittsburgh, PA, United States.

Frontiers in Artificial Intelligence
|November 19, 2024
PubMed
Summary
This summary is machine-generated.

Large language models enable automated item generation for interactive language assessments. This AI-driven approach creates complex tasks at scale, improving the assessment of interactional competence in English proficiency tests.

Keywords:
Duolingo English testautomatic item generationgenerative AIinteractional competenceinteractive listeninglistening assessmentpsychometrics

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Area of Science:

  • Natural Language Processing
  • Educational Technology
  • Artificial Intelligence in Education

Background:

  • Traditional language assessments struggle with evaluating interactional competence at scale.
  • Automated assessments often lack the complexity to capture authentic conversational abilities.
  • Existing digital formats have limitations inherited from paper-based tasks.

Purpose of the Study:

  • To describe the use of generative AI for creating large-scale interactive listening tasks.
  • To enhance the assessment of interactional competence in English proficiency testing.
  • To develop more authentic automated measures of listening ability.

Main Methods:

  • Utilized large language models for automated item generation (AIG).
  • Developed an interactive listening task for the Duolingo English Test (DET).
  • Employed a human-in-the-loop system for AI-driven assessment creation.

Main Results:

  • Successfully generated 713 complex tasks using generative AI.
  • Pilot results demonstrated feasibility for large-scale educational assessment creation.
  • Human review confirmed the effectiveness of the AI-driven approach.

Conclusions:

  • Generative AI offers a scalable solution for creating complex, interactive language assessment tasks.
  • AI-driven methods can overcome limitations of traditional and existing automated assessments.
  • This approach supports richer, more authentic evaluations of interactional competence.