Integration of AI in Qualitative Data Analysis
Integration of AI in Qualitative Data Analysis
There are currently a number of important technological changes taking place with pressing impacts on the ways of dealing with and analysing qualitative data in research contexts. The central objective of this course is to provide training in qualitative data analysis using, in a rigorous, reflective and critical way, various Artificial Intelligence (AI) instruments currently available. The technical potential of some of the main AI tools and resources will be presented, contextualised in the history of qualitative methods, in order to frame the main changes observed in recent decades and identify advantages, risks and impacts to be considered in the analysis processes.
This school will consist of 4 days of training and assisted practice sessions focused on qualitative data analysis. The aim is to focus on critical issues such as the place of researchers in the face of process automation, or the impacts of automation on the qualitative data analysis process itself. To this end, various support and analysis tools will be tested that not only allow the review and optimisation of procedures, but also understand the resulting changes.
Target Audience
The course is aimed at higher education students, teachers, researchers, senior company managers, specialists from different areas (e.g. marketing, market research, human resources, opinion polls, among others), who wish to obtain knowledge in terms of scientific research methodology in the analysis of qualitative data and that use or intend to use applications to carry out qualitative data analysis of different types.
Tuition Fees
| General Public | € 195 |
| ICS Community | € 97,50 |
These prices do not include the application (€ 10) and enrolment (€ 25) fees as well as the fee for a certificate in Portuguese (€ 10).
Francisco Freitas
MAXQDA Certified Professional Trainer
ORCID
E-mail
Temporary MAXQDA licenses will be made available to all participants for improved use of the program. A list of AI applications will be provided that participants can subscribe to for functional testing.
Day 1 - Qualitative data analysis: new possible paths
- Objectives and areas of application of qualitative data analysis;
- Evolution and trends in qualitative data analysis;
- Stages of qualitative data analysis;
- Qualitative data repositories;
- Incorporation of software into the analysis, including AI systems;
- Using language models: what they are, how they work, what are the limits
- Automated audio/video transcription;
- Translation services (e.g. DeepL, Grammarly).
Day 2 - Optimisation of computational support in qualitative data analysis
- Analysis methodologies:
- New ways of floating data reading;
- Integration of AI services in research (e.g. ChatGPT, Claude 2, Copilot, Jenni AI, Elicit);
- Interpretation and reading in depth vs. measurement and quantification;
- Preparation and preprocessing of data with AI;
- Strategies and procedures for analyzing textual data, including AI-assisted coding;
- Data analysis and systematization techniques with MAXQDA's AI Assist.
Day 3 - Critical assessment in solution integration
- Ethical impacts of using AI in analysis;
- Automation of tasks and the (new) role of the researcher;
- AI as a methodology?;
- Limits and new methodological biases;
- Get around the mere description of data: critical analysis of the data and the analysis process;
Day 4 - Analysis and reporting of qualitative data
- Data analysis with AI tools;
Day 5 - Analysis and reporting of qualitative data (Continued)
- Data analysis with AI toolsActivities/Contents;
- Human factor in data analysis and creativity;
- Auditing results obtained with AI tools;
- Reporting results and validation.
Summary: 5 sessions, minimum of 20 hours of contact and individual work. Obtaining 3 ECTS is subject to the presentation of a final project within 7 days of the end of the course.
Assessment Methodologies
In this course, the Moodle platform will be used to manage training content and centralise communication. The course will feature contact hours for group work, assisted by a set of support materials. Participants will be given the possibility of carrying out a small qualitative data analysis project according to their research and/or professional interests. Participants are guaranteed assistance in carrying out the proposed tasks
Course participants will have to carry out a small qualitative data analysis project according to their research and/or professional interests. This project will be discussed during additional sessions, which take place, preferably, in the afternoon. No summative assessment.
References
Bernard, Harvey Russel; Ryan, Gery Wayne (2010). Analyzing Qualitative Data: Systematic Approaches. London: SAGE Publications.
Christou, P. (2023). Ηow to Use Artificial Intelligence (AI) as a Resource, Methodological and Analysis Tool in Qualitative Research? The Qualitative Report. https://doi.org/10.46743/2160-3715/2023.6406
Kuckartz, U. (2014). Qualitative Text Analysis: A Guide to Methods, Practice & Using Software. London: SAGE Publications.
Kuckartz, U., & Rädiker, S. (2019). Analyzing qualitative data with MAXQDA: Text, audio, and video. London: SAGE Publications.
Lewins, Ann; Silver, Christina (2014), Using Software in Qualitative Research: A Step-by-Step Guide. London: SAGE Publications.
Mathew, A. . (2023). Is Artificial Intelligence a World Changer? A Case Study of OpenAI’s Chat GPT. Recent Progress in Science and Technology Vol. 5, 35–42. https://doi.org/10.9734/bpi/rpst/v5/18240D
Michael Huberman & Matthew B. Miles (Eds.) (2002). The Qualitative Researcher's Companion. Thousand Oaks: SAGE Publications.
Paul, R., & Elder, L. (2020). The miniature guide to critical thinking: Concepts and tools (Eighth edition). Rowman & Littlefield.
Qiu, R. (2023), "Editorial: GPT revolutionizing AI applications: empowering future digital transformation", Digital Transformation and Society, Vol. 2 No. 2, pp. 101-103. https://doi.org/10.1108/DTS-05-2023-066
Silverman, David (2013). Doing Qualitative Research. London: SAGE Publications.
Sousa, S., & Kern, R. (2022). How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing. https://doi.org/10.48550/ARXIV.2205.10095
Applications
Applications until 30 June 2025.
Applications through the ICS FenixEdu platform: https://fenix.ics.ulisboa.pt.
To create a registration, please access https://fenix.ics.ulisboa.pt/accountCreation.
In case you already have a registration, you can recover the access at https://fenix.ics.ulisboa.pt/passwordResetRequest.
In case you already have a student number at ICS, you should use your Campus account credentials. You may recover your access to this account at https://utilizador.ulisboa.pt.
Requirements
To attend the course, the candidate must be 18 years of age or over and fit into the defined target audience. If all vacancies are filled, preference will be given to candidates preparing theses or dissertations involving research projects related to the proposed themes. A good command of the English language is recommended to use the supporting bibliography. Internet access required. There must be prior knowledge of qualitative analysis methodology using software.
Admission
The course operates with a minimum of 10 participants and a maximum of 30 participants. If all vacancies are filled, preference will be given to candidates preparing theses or dissertations involving research projects related to the proposed methodologies.
