Generative AI in Self-Directed Learning: A Scoping Review
- URL: http://arxiv.org/abs/2411.07677v1
- Date: Tue, 12 Nov 2024 09:46:40 GMT
- Title: Generative AI in Self-Directed Learning: A Scoping Review
- Authors: Jasper Roe, Mike Perkins,
- Abstract summary: This scoping review examines the current body of knowledge at the intersection of Generative Artificial Intelligence (GenAI) and Self-Directed Learning (SDL)
GenAI tools, including ChatGPT and other Large Language Models (LLMs) show promise in potentially supporting SDL through on-demand, personalised assistance.
There are still significant gaps in understanding the long-term impacts of GenAI on SDL outcomes.
- Score: 0.0
- License:
- Abstract: This scoping review examines the current body of knowledge at the intersection of Generative Artificial Intelligence (GenAI) and Self-Directed Learning (SDL). By synthesising the findings from 18 studies published from 2020 to 2024 and following the PRISMA-SCR guidelines for scoping reviews, we developed four key themes. This includes GenAI as a Potential Enhancement for SDL, The Educator as a GenAI Guide, Personalisation of Learning, and Approaching with Caution. Our findings suggest that GenAI tools, including ChatGPT and other Large Language Models (LLMs) show promise in potentially supporting SDL through on-demand, personalised assistance. At the same time, the literature emphasises that educators are as important and central to the learning process as ever before, although their role may continue to shift as technologies develop. Our review reveals that there are still significant gaps in understanding the long-term impacts of GenAI on SDL outcomes, and there is a further need for longitudinal empirical studies that explore not only text-based chatbots but also emerging multimodal applications.
Related papers
- From Automation to Cognition: Redefining the Roles of Educators and Generative AI in Computing Education [2.0628700367476203]
Generative Artificial Intelligence (GenAI) offers opportunities to revolutionise teaching and learning in Computing Education (CE)
However, educators have expressed concerns that students may over-rely on GenAI and use these tools to generate solutions without engaging in the learning process.
This paper describes our experiences with using GenAI in CS-focused educational settings and the changes we have implemented accordingly in our teaching.
arXiv Detail & Related papers (2024-12-16T03:36:25Z) - A Systematic Review of Generative AI for Teaching and Learning Practice [0.37282630026096586]
There are no agreed guidelines towards the usage of GenAI systems in higher education.
There is a need for additional interdisciplinary, multidimensional studies in HE through collaboration.
arXiv Detail & Related papers (2024-06-13T18:16:27Z) - Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions [101.67121669727354]
Recent advancements in AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment.
The lack of clarified definitions and scopes of human-AI alignment poses a significant obstacle, hampering collaborative efforts across research domains to achieve this alignment.
We introduce a systematic review of over 400 papers published between 2019 and January 2024, spanning multiple domains such as Human-Computer Interaction (HCI), Natural Language Processing (NLP), Machine Learning (ML)
arXiv Detail & Related papers (2024-06-13T16:03:25Z) - Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning [0.0]
This study introduces Professor Leodar, a custom-built, Singlish-speaking Retrieval Augmented Generation (RAG)
Professor Leodar offers a glimpse into the future of AI-assisted learning, offering personalized guidance, 24/7 availability, and contextually relevant information.
arXiv Detail & Related papers (2024-06-12T01:19:36Z) - Toward enriched Cognitive Learning with XAI [44.99833362998488]
We introduce an intelligent system (CL-XAI) for Cognitive Learning which is supported by artificial intelligence (AI) tools.
The use of CL-XAI is illustrated with a game-inspired virtual use case where learners tackle problems to enhance problem-solving skills.
arXiv Detail & Related papers (2023-12-19T16:13:47Z) - Generative Artificial Intelligence in Learning Analytics:
Contextualising Opportunities and Challenges through the Learning Analytics
Cycle [0.0]
Generative artificial intelligence (GenAI) holds significant potential for transforming education and enhancing human productivity.
This paper delves into the prospective opportunities and challenges GenAI poses for advancing learning analytics (LA)
We posit that GenAI can play pivotal roles in analysing unstructured data, generating synthetic learner data, enriching multimodal learner interactions, advancing interactive and explanatory analytics, and facilitating personalisation and adaptive interventions.
arXiv Detail & Related papers (2023-11-30T07:25:34Z) - Exploration with Principles for Diverse AI Supervision [88.61687950039662]
Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI.
While this generative AI approach has produced impressive results, it heavily leans on human supervision.
This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation.
We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
arXiv Detail & Related papers (2023-10-13T07:03:39Z) - Innovating Computer Programming Pedagogy: The AI-Lab Framework for
Generative AI Adoption [0.0]
We introduce "AI-Lab," a framework for guiding students in effectively leveraging GenAI within core programming courses.
By identifying and rectifying GenAI's errors, students enrich their learning process.
For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
arXiv Detail & Related papers (2023-08-23T17:20:37Z) - A Comprehensive Survey of AI-Generated Content (AIGC): A History of
Generative AI from GAN to ChatGPT [63.58711128819828]
ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC)
The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
arXiv Detail & Related papers (2023-03-07T20:36:13Z) - Deep Active Learning for Computer Vision: Past and Future [50.19394935978135]
Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions.
By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies.
arXiv Detail & Related papers (2022-11-27T13:07:14Z) - Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal.
In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
arXiv Detail & Related papers (2021-01-19T12:23:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.