AIED24 - Workshop on Automated Evaluation of Learning and Assessment Content (EvalLAC)

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We are happy to announce the first edition of the Workshop on Automated Evaluation of Learning and Assessment Content will be held in Recife (Brazil) & online during the AIED 2024 conference.

Workshop website:

  • Paper Submission Deadline: May 17, 2024
  • Author notification: June 4, 2024
  • Camera-ready version deadline: June 11, 2024

About the workshop

The evaluation of learning and assessment content has always been a crucial task in the educational domain, but traditional approaches based on human feedback are not always usable in modern educational settings. Indeed, the advent of machine learning models, in particular Large Language Models (LLMs), enabled to quickly and automatically generate large quantities of texts, making human evaluation unfeasible. Still, these texts are used in the educational domain – e.g., as questions, hints, or even to score and assess students – and thus the need for accurate and automated techniques for evaluation becomes pressing. This hybrid workshop aims to attract professionals from both academia and the industry, and to to offer an opportunity to discuss which are the common challenges in evaluating learning and assessment content in education.

Topics of interest include but are not limited to:

  • Question evaluation (e.g., in terms of alignment to learning objectives, factual accuracy, language level, cognitive validity, etc.).
  • Estimation of question statistics (e.g., difficulty, discrimination, response time, etc.).
  • Evaluation of distractors in Multiple Choice Questions.
  • Evaluation of reading passages in reading comprehension questions.
  • Evaluation of lectures and course material.
  • Evaluation of learning paths (e.g., in terms of prerequisites and topics taught before a specific exam).
  • Evaluation of educational recommendation systems (e.g., personalised curricula).
  • Evaluation of hints and scaffolding questions, as well as their adaptation to different students.
  • Evaluation of automatically generated feedback provided to students.
  • Evaluation of techniques for automated scoring.
  • Evaluation of bias in educational content and LLM outputs. Human-in-the-loop approaches are welcome, provided that there is also an automated component in the evaluation and there is a focus on the scalability of the proposed approach. Papers on generation are also very welcome, as long as there is an extensive focus on the evaluation step.


Luca Benedetto Andrew Caines George Dueñas Diana Galvan-Sosa Anastassia Loukina Shiva Taslimipoor Torsten Zesch