A Semi-Automated Solution Approach Recommender for a Given Use Case: a Case Study for AI/ML in Oncology via Scopus and OpenAI
Project Overview
The document outlines the transformative role of generative AI in education, focusing on its applications, findings, and outcomes. It highlights the SARBOLD-LLM tool as an innovative semi-automated solution for literature reviews, particularly in the field of oncology, which demonstrates the efficiency of AI in processing vast amounts of information. By utilizing Scopus and OpenAI, this tool significantly reduces the time for manual literature reviews from weeks to mere hours, making it easier for researchers and engineers to identify relevant methods for their projects. Additionally, the document emphasizes the broader implications of generative AI in educational environments, illustrating its capacity to enhance learning experiences through personalized education and to streamline administrative tasks. As AI technologies become increasingly integrated into educational settings, the document underscores the importance for educators to adapt to these advancements, thereby enriching the learning ecosystem and fostering more effective educational practices. Overall, the findings suggest that generative AI not only aids in research efficiency but also holds the potential to revolutionize the educational landscape by improving accessibility and tailoring learning to individual needs.
Key Applications
AI-Driven Literature Review and Personalized Learning Tools
Context: Used in various educational settings, including K-12 and higher education, for literature review in research (particularly in oncology) and for personalized learning through AI-driven tutoring systems that provide real-time feedback and tailored learning paths.
Implementation: The implementation involves the use of AI technologies, including large language models (LLMs) for literature review and content generation, as well as AI tutors integrated into existing curricula. These systems leverage advanced algorithms to analyze literature and generate exercises or learning materials, while also deploying AI tutors to assist students in their learning journeys.
Outcomes: The combined tools significantly reduce literature review time from weeks to hours, enhance student engagement through personalized content, improve accessibility to learning materials, and result in improved student performance and individualized learning experiences.
Challenges: Challenges include reliance on existing literature and potential gaps in method relevancy for literature review, as well as the need for substantial computational resources, potential biases in generated content, limited understanding of AI by educators, and the risk of over-reliance on technology that may hinder traditional learning methods.
Implementation Barriers
Technical Barrier
Dependence on existing literature may limit the discovery of new methods for specific use cases. Additionally, insufficient technological infrastructure in some educational institutions poses challenges.
Proposed Solutions: Future enhancements may involve the integration of more diverse databases and automatic sensitivity analyses to broaden literature coverage. Investments in IT infrastructure and training for educators on the use of AI tools are also essential.
Human Factor Barrier
The current version requires human intervention for keyword selection and validation, which could hinder full automation.
Proposed Solutions: Future work aims to automate these tasks further and reduce the need for human input.
Cultural Barrier
Resistance from educators and administrators to adopt AI due to fear of job displacement.
Proposed Solutions: Awareness campaigns highlighting the complementary role of AI in education and retraining programs for educators.
Ethical Barrier
Concerns regarding data privacy and security with AI implementations.
Proposed Solutions: Establishing clear data governance policies and ensuring compliance with regulations.
Project Team
Deniz Kenan Kılıç
Researcher
Alex Elkjær Vasegaard
Researcher
Aurélien Desoeuvres
Researcher
Peter Nielsen
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Deniz Kenan Kılıç, Alex Elkjær Vasegaard, Aurélien Desoeuvres, Peter Nielsen
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