Course

Unit 1

Introduction to Digital Literacy

Digital literacy and its applications in social work; Components and concepts of digital literacy; Introduction to Artificial Intelligence (AI): meaning, scope, and applications in the social sector; Role of AI in enhancing digital and data literacy among social work professionals

Unit 2

Understanding the data basics

Types of data; Size of the data; Accuracy; Format; Access rights, Data analysis, Story telling with data; AI-enhanced data interpretation: understanding how AI tools support data visualization and pattern recognition

Unit 3

Introduction to Data collection and Data management

Types of data tools, Documentation of data, Digitalisation of data collection tools using Google forms, Excel sheets, and Word documents; Data dictionaries; AI-assisted data management: automation in data entry, sentiment analysis, and data cleaning.

Unit 4

Data sources and data quality

Identifying the authority of the data, credibility of the data, reliability, ethical considerations, data policies, and challenges: Data bias and discrimination in AI systems

Text Books / References

References:

• Learning practical digital skills with Google https://applieddigitalskills.withgoogle.com/en/learn
• Artificial Intelligence (AI) https://www.ibm.com/think/topics/artificial-intelligence

Course Objectives:

1. Introduce the concept of data literacy and its importance in social work.
2. To provide learners with essential knowledge and abilities related to digital tools and technologies.
3. Explore methods of data collection and management for social work applications.
4. Familiarize learners with the role of Artificial Intelligence (AI) in data analysis, management, and ethical social work practice..

Course Outcomes:

1. CO1: ability to explore social data and understand basic characteristics of the data.
2. CO2: ability to verify the available data source in terms of authenticity.
3. CO3: ability to use fundamental software applications to access and verify data.
4. CO4: ability to apply basic AI-based tools and approaches for data interpretation and to evaluate associated ethical challenges.

Skills:

• Competence to differentiate different forms of data
• Generate, process or manage data using Microsoft Excel, Word, and Google forms
• Search and Identify public data sources according to the requirments
• Awareness of AI applications, limitations, and ethical implications in social data analysis.

CO-PO Mapping: