Session 2: AI-based Digital and Social Media Services for the development of applied research in VET
Learning Objectives:
- Promote a critical interpretation of the use of AI-based digital services for applied research
- Identify the most suitable AI-based digital tools for the development of applied research in VET assignment
In addition to the digital services and social media most widely known and used for the development of applied research in VET, more and more tools and technologies based on Artificial Intelligence (AI) are becoming available that can significantly improve applied research in VET. These tools can help collect, analyse, predict and personalise data, thus improving the quality and efficiency of research. Here are some AI tools and technologies that can be used:
AI-based Digital Services
- Natural Language Processing (NLP) Tools
- Text Analysis and Sentiment Analysis: Tools like IBM Watson NLP, Google Cloud Natural Language API, and SpaCy can analyze large volumes of textual data from surveys, interviews, and open-ended responses to identify themes, sentiments, and trends.
- Transcription Services: AI-driven transcription tools such as Otter.ai and Rev.com can convert audio interviews and focus group discussions into text, making it easier to analyze qualitative data.
- Machine Learning Platforms
- Data Analysis and Predictive Modeling: Platforms like Google Cloud AI Platform, Amazon SageMaker, and Microsoft Azure Machine Learning allow researchers to build, train, and deploy machine learning models to analyze data, identify patterns, and make predictions.
- RStudio and Python Libraries: Libraries such as scikit-learn, TensorFlow, and Keras can be used for developing custom machine learning models tailored to specific research needs.
- AI-Driven Survey Tools
- Adaptive Surveys: Tools like SurveyMonkey Genius and Qualtrics iQ use AI to optimize survey questions in real-time based on respondent answers, improving data quality and response rates.
- Automated Analysis: These tools also offer AI-driven insights and automated analysis to quickly interpret survey results.
- Data Visualization and Reporting
- AI-Powered Visualization: Tools like Tableau with its AI feature Tableau Einstein Discovery and Power BI with Power BI AI capabilities can automatically generate insights and visualizations from complex datasets.
- Natural Language Generation: Narrative Science and Wordsmith can automatically generate written reports and summaries from data, making it easier to communicate findings.
- Collaboration and Project Management
- AI-Powered Collaboration: Platforms like Microsoft Teams and Slack integrate AI to streamline communication, automate routine tasks, and provide intelligent recommendations.
- Project Management: Tools like Asana and Trello use AI to optimize project workflows and predict project timelines and resource needs.
- Qualitative Data Analysis
- Thematic Analysis: NVivo and Atlas.ti incorporate AI features to assist with coding, theme identification, and pattern recognition in qualitative data.
- Voice and Video Analysis: Tools like VoiceBase and Deepgram can analyze audio and video content to extract key insights, detect emotions, and identify important topics.
- Learning Analytics and Personalization
- Learning Management Systems (LMS): Platforms like Moodle and Canvas use AI to analyze student performance data, personalize learning experiences, and provide recommendations for educators.
- Adaptive Learning Technologies: Tools like Knewton and Smart Sparrow adapt educational content based on individual learner needs, improving learning outcomes.
Harikrishna Kundariya (2023) “Best AI Tools To Empower Your Academic Research”
Are you using AI tools to enhance your academic research? That means you are taking one step forward in personalizing your learning experience. Today, AI has simplified research processes and learning, making the education process easier for students and scholars. Let’s explore some brilliant AI tools to help your research.
Activity 2: Example Workflow using AI tools for developing an applied research in VET assignment
Following this workflow, try to effectively integrate AI tools into the research process, enhancing data collection, analysis, and reporting processes, leading to more accurate insights and impactful outcomes.
- Data Collection:
- Design surveys using Qualtrics iQ or SurveyMonkey Genius for adaptive and optimized question sets.
- Transcribe interviews with Otter.ai or Rev.com.
- Data Analysis:
- Analyze qualitative data using NVivo or Atlas.ti.
- Perform sentiment analysis and text mining with Google Cloud Natural Language API.
- Use scikit-learn or TensorFlow for predictive modeling and machine learning analysis.
- Visualization and Reporting:
- Generate insights and visualizations with Tableau or Power BI.
- Create automated reports with Narrative Science or Wordsmith.
- Collaboration and Management:
- Use Microsoft Teams or Slack for team collaboration.
- Manage research projects with Asana or Trello.
- Personalization and Learning Analytics:
- Implement personalized learning solutions using Knewton or Smart Sparrow.
- Analyze learning data with LMS platforms like Moodle or Canvas.
Anand Y. Kenchakkanavar (2023) “Exploring the Artificial Intelligence Tools: Realizing the Advantages in Education and Research” in Journal of Advances in Library and Information Science. ISSN: 2277-2219 Vol. 12. No.4. 2023.
Artificial intelligence will play a role in research by enabling independent learning, helping teachers and students search for information and find better ways to access it, and creating tedious tasks such as searching to find information quickly. This article will discuss the potential of artificial intelligence in education and research and the problems arising in its application. Artificial intelligence has become a major challenge in the era of rapid and transparent technological progress. The main purpose of this study is to reveal the value of intellectual property in education and research. This article will discuss the potential of artificial intelligence in education and research and the problems arising from its use. Artificial intelligence will play a role in research by enabling independent learning, helping teachers and students search for information and find better ways to access it, and creating tedious tasks such as searching to find information quickly. This article will discuss the potential of artificial intelligence in education and research and the problems arising in its application.
Prokopis A. Christou (2023) “The Use of Artificial Intelligence (AI) in Qualitative Research for Theory Development” in The Qualitative Report. DOI: 10.46743/2160-3715/2023.6536
Theory development is an important component of academic research since it can lead to the acquisition of new knowledge, the development of a field of study, and the formation of theoretical foundations to explain various phenomena. The contribution of qualitative researchers to theory development and advancement remains significant and highly valued, especially in an era of various epochal shifts and technological innovation in the form of Artificial Intelligence (AI). Even so, the academic community has not yet fully explored the dynamics of AI in research and there are significant gaps in our understanding of how AI can be used most effectively in the context of theory building. The aim of this paper, which is driven by critical and conceptualization methodological dynamics, is to investigate the role of AI in the theory development process. As such, it critically evaluates the opportunities and limitations of AI in theory building, delivers a conceptual map of the nexus between AI and theory development, and presents key considerations for the use of AI in the creation of new theories or the advancement of existing ones. Though the necessity of AI tools in theory creation is contested given that the researchers’ cognitive and evaluative skills are regarded as critical in this process, the value of AI in advancing theory is not to be underestimated.
6 AI Tools You’ll ACTUALLY Use For Research: This YouTube video covers practical AI tools that can significantly enhance research productivity. It details tools for literature review, data analysis, and writing assistance, making it a valuable resource for researchers in any field.