Course Description:
Moodle is widely used for quiz-based assessment, with strict import formats for creating and managing question banks. As large language models (LLMs) are increasingly used to support assessment authoring, their practical usefulness in Moodle depends on their ability to generate quiz files that conform to these format requirements and can be imported with minimal instructor intervention.
This study proposes a comparative evaluation of how seamlessly different LLMs can generate Moodle-compatible quizzes when provided with explicit instructor instructions. Specifically, it examines three commonly supported quiz import formats: Aiken, GIFT, and XML. In parallel, the study will compare outputs produced by three LLMs: ChatGPT, Gemini, and DeepSeek. Using a controlled prompting design, each model will be tasked with generating quizzes in each format under identical instructional conditions.
The results section will present insightful comparisons across quiz formats and language models. This includes Moodle import results, format-related error patterns, required corrections, and measures of instructor effort. The findings will compare how different LLMs perform across formats, as well as how each format behaves across models.
By outlining a structured framework for comparing quiz formats and LLMs within a Moodle-based assessment workflow, this study aims to provide a practical reference for instructors and institutions exploring AI-assisted quiz generation while maintaining alignment with established LMS constraints.
Date & Time: June 18, 2026 | 10:45 AM - 11:45 AM
Course Description:
In this session, we will look at the goldmine of data already sitting inside your Moodle site and discover how to turn it into a crystal ball for student success. While many institutions use "Learning Analytics" to look at what has already happened, we will explore how Predictive AI can identify at-risk students weeks before they fall behind.
We will walk through a real-world framework for building a predictive model—from selecting the right data points (participation pattern, online assessment, and submission patterns) to the ethical implementation of automated interventions. Participants will leave with a clear roadmap on how to move from "passive reporting" to "proactive teaching" without needing a PhD in Data Science. This is not about a specific tool; it’s about a new strategy for student retention in the age of AI.
Date & Time: June 18, 2026 | 9:10 AM - 10:30 AM