Fostering Self-Directed Growth with Generative AI: Toward a New Learning Analytics Framework
Project Overview
The document explores the role of Generative Artificial Intelligence (GAI) in education, emphasizing its integration with Learning Analytics (LA) to enhance Self-Directed Learning (SDL). It presents the Aspire to Potentials for Learners (A2PL) framework, which is designed to empower learners by promoting agency and personalized learning experiences. The text highlights GAI's transformative potential in improving educational equity by making quality education more accessible and fostering self-directed learning skills among students. However, it also addresses challenges such as the risk of over-reliance on technology and the necessity for adaptive learning analytics that cater to individual learners' needs. Overall, the document underscores the promising applications of GAI in education while calling attention to the complexities involved in its effective implementation.
Key Applications
AI Chatbot Support for Self-Directed Learning
Context: K-12 and higher education environments focused on self-directed growth and personalized learning experiences for diverse learners, utilizing AI chatbots to provide real-time feedback and support.
Implementation: Integration of generative AI chatbots to facilitate personalized learning, provide immediate assistance, and enhance self-assessment frameworks, supporting learners in various educational settings.
Outcomes: Enhancement of learner agency, increased engagement and productivity, improved self-directed learning competencies, and academic achievement, along with more equitable access to educational resources.
Challenges: Concerns about academic integrity, potential over-reliance on AI tools, privacy violations, and ensuring that AI supports rather than directs learning.
Implementation Barriers
Technical
Challenges in effectively integrating GAI with learning analytics, along with the need for adaptive learning analytics that can provide meaningful feedback tailored to individual learner needs.
Proposed Solutions: Development of adaptive learning analytics that can provide meaningful feedback tailored to individual learner needs.
Educational
Over-reliance on GAI tools which may diminish learner agency, necessitating the design of GAI systems that promote guided discovery rather than merely providing answers.
Proposed Solutions: Designing GAI systems that promote guided discovery rather than merely providing answers.
Research
Lack of empirical studies on the efficacy of GAI in fostering self-directed learning, highlighting the need for future research to validate the proposed frameworks and their impact on learner outcomes.
Proposed Solutions: Encouraging future research to validate the proposed frameworks and their impact on learner outcomes.
Project Team
Qianrun Mao
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Qianrun Mao
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