Navigating AI in Social Work and Beyond: A Multidisciplinary Review
Project Overview
The document examines the transformative role of generative AI in education, with a particular focus on its application in social work. It outlines the evolution of AI technology and its various uses within educational environments, emphasizing the Advanced Personalised Simulation Training (APST) as a significant application that generates customized simulations to enrich students' learning experiences. While the potential benefits of integrating AI in education are substantial, the document also addresses critical challenges, including ethical concerns related to data privacy and the risk of exacerbating access disparities among students. Overall, it presents a balanced view of generative AI’s promise and pitfalls, urging stakeholders to consider both its innovative capabilities and the ethical implications of its deployment in educational contexts.
Key Applications
AI-Supported Simulation Training
Context: Social work education, particularly targeting social work students undergoing clinical training in areas such as substance abuse and suicide assessment. This includes immersive simulations that provide realistic scenarios for students to navigate.
Implementation: AI generates high-fidelity simulations tailored to individual student needs, integrating decision-making trees and virtual reality environments. These simulations offer real-time feedback and allow students to engage in challenging conversations in a supportive environment.
Outcomes: ['Improved student confidence and preparedness for real-world practice.', 'Enhanced understanding of complex social issues.', 'Development of communication skills in sensitive contexts.']
Challenges: ['Ethical concerns regarding student privacy and algorithmic surveillance.', 'Technology acceptance among students and educators.', 'Disparities in access to technology for students from lower socioeconomic backgrounds.']
Implementation Barriers
Ethical Barrier
Concerns regarding student privacy and data management in AI applications.
Proposed Solutions: Co-designing programs in-house to ensure data is securely stored on-site and handled ethically.
Access Barrier
Disparities in access to AI tools for students from lower socioeconomic backgrounds or remote areas.
Proposed Solutions: Targeted interventions to improve access to technology and resources.
Technological Barrier
Resistance to adopting new technologies among students and instructors.
Proposed Solutions: Training and support to facilitate acceptance and integration of AI tools in the curriculum.
Project Team
Matt Victor Dalziel
Researcher
Krystal Schaffer
Researcher
Neil Martin
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Matt Victor Dalziel, Krystal Schaffer, Neil Martin
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