Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics Cycle
Project Overview
The document explores the transformative impact of Generative Artificial Intelligence (GenAI) in the educational landscape, particularly focusing on Learning Analytics (LA). It outlines several key applications of GenAI, including the analysis of unstructured data, generation of synthetic learner data, enhancement of multimodal interactions, and facilitation of personalized learning interventions. These advancements aim to create more tailored educational experiences that cater to individual learner needs. However, the document also raises important challenges and ethical considerations linked to the adoption of GenAI in education, underscoring the necessity for a careful and nuanced understanding of learners in an increasingly AI-driven environment. Overall, while GenAI presents significant opportunities for innovation in educational practices, it also calls for a critical examination of its implications to ensure that it supports equitable and effective learning outcomes.
Key Applications
ChatGPT, Midjourney, Whisper, Codex
Context: Educational contexts including university courses, MOOCs, and training programs across various disciplines. Target audience includes educators, students, and learning analytics researchers.
Implementation: Integration of GenAI tools into the learning analytics cycle for analyzing data, generating insights, and personalizing learning experiences.
Outcomes: Enhanced engagement, improved learning analytics, more comprehensive feedback and assessments, dynamic content generation for teaching resources.
Challenges: Issues of learner identity and agency, potential for ethical concerns and biases, and the risk of widening the digital divide.
Implementation Barriers
Ethical
Concerns regarding privacy, data security, and the potential for bias in AI-generated content.
Proposed Solutions: Establishing clear ethical guidelines, ensuring transparency in data usage, and promoting equitable access to GenAI technologies.
Economic
The high costs associated with developing and maintaining GenAI technologies that may limit accessibility for under-resourced institutions.
Proposed Solutions: Exploring open-source alternatives and funding mechanisms to support equitable access to GenAI tools.
Project Team
Lixiang Yan
Researcher
Roberto Martinez-Maldonado
Researcher
Dragan Gašević
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Lixiang Yan, Roberto Martinez-Maldonado, Dragan Gašević
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai