Generative AI Courses
Learning materials and training for researchers and analysts on generative AI
Previous Courses
Previous Courses
Current courses
Current courses
Generative AI Analyses
The aim of this course is to provide a comprehensive overview of the capabilities, limitations, and responsible use of generative artificial intelligence. After introducing the foundations, we demonstrate how these tools can support everyday workflows, creative content production, and organisational efficiency. The course covers effective prompting techniques, criteria for choosing the right tools, and the cost implications of different applications. Particular emphasis is placed on the expectations surrounding responsible use, including data management, transparency, and adherence to professional and ethical standards.
Generative AI for Literature Search and Literature Review Writing
The aim of this course is to demonstrate how generative language models can support the discovery, organisation, and summarisation of relevant academic literature, as well as the management and formatting of references. We will explore how to design effective search strategies and produce concise summaries with AI assistance, which models and tools are best suited to these tasks, and what limitations and challenges need to be considered when applying them.
Generative AI for Literature Search and Literature Review Writing
The aim of this course is to demonstrate how generative language models can support the discovery, organisation, and summarisation of relevant academic literature, as well as the management and formatting of references. We will explore how to design effective search strategies and produce concise summaries with AI assistance, which models and tools are best suited to these tasks, and what limitations and challenges need to be considered when applying them.
Other Courses
Other Courses
Generative AI for Researchers
The aim of this course is to explore the capabilities, limitations, and guidelines for the use of generative models, with a focus on language models. After reviewing the conceptual foundations, we examine how these tools can be applied to common research tasks through effective prompt design, how to choose the most suitable models for specific purposes, and the costs associated with different applications. The course also addresses the expectations that researchers should be aware of when using these tools, including data management, proper attribution of use, and compliance with journal requirements.
Text Mining with AI Support in R
This course introduces participants to the fundamentals of text mining and its applications in the social sciences. It provides an overview of the distinctions between information retrieval and information extraction, as well as key concepts such as the bag-of-words model and sentiment analysis. Alongside clarifying the theoretical and methodological foundations, the course offers practical insights into the use of R and RStudio through selected examples.
Customised Training Courses Based on Individual Request
genai@poltextlab.com
Instructors
Instructors
Miklós SEBŐK
Dr Miklós Sebők is a research professor of the ELTE Centre for Social Sciences and director of the poltextLAB artificial intelligence laboratory in Budapest. He earned an M.A. degree in politics at the University of Virginia and an M.A. degree in economics at the Corvinus University of Budapest. He received his Ph.D. in Political Science from ELTE University of Budapest.
He is the research director of the Hungarian Comparative Agendas Project, the research co-director of the Artificial Intelligence National Lab at CSS, and the principal investigator of the V-SHIFT Momentum research project (funded by the Hungarian Academy of Sciences). He also leads the "BABELGLOB" Excellence research project of the Hungarian National Research, Development and Innovation Office and serves as the executive director of the COMPTEXT conference.
In addition to his research, Miklós Sebők also engages in teaching. In addition to his research, Miklós Sebők maintains an active teaching portfolio. In 2024, he co-developed with Rebeka Kiss the "Generative Artificial Intelligence for Researchers" training programme and corresponding textbook.
Dr Miklós Sebők is a research professor of the ELTE Centre for Social Sciences and director of the poltextLAB artificial intelligence laboratory in Budapest. He earned an M.A. degree in politics at the University of Virginia and an M.A. degree in economics at the Corvinus University of Budapest. He received his Ph.D. in Political Science from ELTE University of Budapest.
He is the research director of the Hungarian Comparative Agendas Project, the research co-director of the Artificial Intelligence National Lab at CSS, and the principal investigator of the V-SHIFT Momentum research project (funded by the Hungarian Academy of Sciences). He also leads the "BABELGLOB" Excellence research project of the Hungarian National Research, Development and Innovation Office and serves as the executive director of the COMPTEXT conference.
In addition to his research, Miklós Sebők also engages in teaching. In addition to his research, Miklós Sebők maintains an active teaching portfolio. In 2024, he co-developed with Rebeka Kiss the "Generative Artificial Intelligence for Researchers" training programme and corresponding textbook.
Dr Miklós Sebők is a research professor of the ELTE Centre for Social Sciences and director of the poltextLAB artificial intelligence laboratory in Budapest. He earned an M.A. degree in politics at the University of Virginia and an M.A. degree in economics at the Corvinus University of Budapest. He received his Ph.D. in Political Science from ELTE University of Budapest.
He is the research director of the Hungarian Comparative Agendas Project, the research co-director of the Artificial Intelligence National Lab at CSS, and the principal investigator of the V-SHIFT Momentum research project (funded by the Hungarian Academy of Sciences). He also leads the "BABELGLOB" Excellence research project of the Hungarian National Research, Development and Innovation Office and serves as the executive director of the COMPTEXT conference.
In addition to his research, Miklós Sebők also engages in teaching. In addition to his research, Miklós Sebők maintains an active teaching portfolio. In 2024, he co-developed with Rebeka Kiss the "Generative Artificial Intelligence for Researchers" training programme and corresponding textbook.
Rebeka KISS
Dr Rebeka Kiss is a junior research fellow at the poltextLAB artificial intelligence laboratory of the ELTE Centre for Social Sciences. She is currently a PhD student at the Doctoral School of Public Administration Sciences at Ludovika University of Public Service, also a law student at the Faculty of Law at Eötvös Loránd University (ELTE).
Her areas of expertise include legislative studies, as well as the regulatory, ethical, and social challenges of artificial intelligence and emerging technologies. She has participated in various research projects, such as the Hungarian Comparative Agendas Project, the V-SHIFT Momentum research project, and the OPTED – Observatory for Political Texts in European Democracies project.
Alongside her research activities, Rebeka Kiss is also involved in education. In 2024, she co-developed the "Generative Artificial Intelligence for Researchers" training programme and related textbook in collaboration with Miklós Sebők.
Dr Rebeka Kiss is a junior research fellow at the poltextLAB artificial intelligence laboratory of the ELTE Centre for Social Sciences. She is currently a PhD student at the Doctoral School of Public Administration Sciences at Ludovika University of Public Service, also a law student at the Faculty of Law at Eötvös Loránd University (ELTE).
Her areas of expertise include legislative studies, as well as the regulatory, ethical, and social challenges of artificial intelligence and emerging technologies. She has participated in various research projects, such as the Hungarian Comparative Agendas Project, the V-SHIFT Momentum research project, and the OPTED – Observatory for Political Texts in European Democracies project.
Alongside her research activities, Rebeka Kiss is also involved in education. In 2024, she co-developed the "Generative Artificial Intelligence for Researchers" training programme and related textbook in collaboration with Miklós Sebők.
Dr Rebeka Kiss is a junior research fellow at the poltextLAB artificial intelligence laboratory of the ELTE Centre for Social Sciences. She is currently a PhD student at the Doctoral School of Public Administration Sciences at Ludovika University of Public Service, also a law student at the Faculty of Law at Eötvös Loránd University (ELTE).
Her areas of expertise include legislative studies, as well as the regulatory, ethical, and social challenges of artificial intelligence and emerging technologies. She has participated in various research projects, such as the Hungarian Comparative Agendas Project, the V-SHIFT Momentum research project, and the OPTED – Observatory for Political Texts in European Democracies project.
Alongside her research activities, Rebeka Kiss is also involved in education. In 2024, she co-developed the "Generative Artificial Intelligence for Researchers" training programme and related textbook in collaboration with Miklós Sebők.
Dávid LÁSZLÓ
Dávid László holds a BA in Philosophy, Politics and Economics from the University of York and an MSc in Economics from the London School of Economics (LSE). He previously worked as a predoctoral research fellow at LSE, focusing on environmental and development economics. His main interests lie in statistical machine learning and its applications to development policy.
Dávid László holds a BA in Philosophy, Politics and Economics from the University of York and an MSc in Economics from the London School of Economics (LSE). He previously worked as a predoctoral research fellow at LSE, focusing on environmental and development economics. His main interests lie in statistical machine learning and its applications to development policy.
Dávid László holds a BA in Philosophy, Politics and Economics from the University of York and an MSc in Economics from the London School of Economics (LSE). He previously worked as a predoctoral research fellow at LSE, focusing on environmental and development economics. His main interests lie in statistical machine learning and its applications to development policy.
Orsolya RING
Orsolya Ring is a senior research fellow at the Political and Legal Text Mining and Artificial Intelligence Laboratory (poltextLAB) of the ELTE Centre for Social Sciences and a lecturer at the Institute of Historical Studies at ELTE University. She received her Ph.D. in History from ELTE University of Budapest.
Her areas of expertise include quantitative methods, artificial intelligence, text mining, sentiment and emotion analysis, content analysis. She focuses on the longitudinal analysis of media texts and parliamentary documents. She is working in the poltextLAB Project on creation and classification of large-scale newspaper corpora and elaboration of a domain-specific method for emotion and sentiment analysis applying various machine learning methods.
Dr Ring has participated in various research projects, including the Hungarian Comparative Agendas Project, the V-SHIFT Momentum research project. She led the Visegrad Media Slant project, which analyzed media bias and political slant across Central European media outlets. She is also involved in the DISINFO-PROMPT project, where she conducts dynamic network analysis examining the spread of textual narratives on social media data. Additionally, she participates in the MORES (Moral Emotions in Politics: How They Unite, How They Divide) project, focusing on multilingual modeling and text analysis for emotion analysis of texts.
Orsolya Ring is a senior research fellow at the Political and Legal Text Mining and Artificial Intelligence Laboratory (poltextLAB) of the ELTE Centre for Social Sciences and a lecturer at the Institute of Historical Studies at ELTE University. She received her Ph.D. in History from ELTE University of Budapest.
Her areas of expertise include quantitative methods, artificial intelligence, text mining, sentiment and emotion analysis, content analysis. She focuses on the longitudinal analysis of media texts and parliamentary documents. She is working in the poltextLAB Project on creation and classification of large-scale newspaper corpora and elaboration of a domain-specific method for emotion and sentiment analysis applying various machine learning methods.
Dr Ring has participated in various research projects, including the Hungarian Comparative Agendas Project, the V-SHIFT Momentum research project. She led the Visegrad Media Slant project, which analyzed media bias and political slant across Central European media outlets. She is also involved in the DISINFO-PROMPT project, where she conducts dynamic network analysis examining the spread of textual narratives on social media data. Additionally, she participates in the MORES (Moral Emotions in Politics: How They Unite, How They Divide) project, focusing on multilingual modeling and text analysis for emotion analysis of texts.
Orsolya Ring is a senior research fellow at the Political and Legal Text Mining and Artificial Intelligence Laboratory (poltextLAB) of the ELTE Centre for Social Sciences and a lecturer at the Institute of Historical Studies at ELTE University. She received her Ph.D. in History from ELTE University of Budapest.
Her areas of expertise include quantitative methods, artificial intelligence, text mining, sentiment and emotion analysis, content analysis. She focuses on the longitudinal analysis of media texts and parliamentary documents. She is working in the poltextLAB Project on creation and classification of large-scale newspaper corpora and elaboration of a domain-specific method for emotion and sentiment analysis applying various machine learning methods.
Dr Ring has participated in various research projects, including the Hungarian Comparative Agendas Project, the V-SHIFT Momentum research project. She led the Visegrad Media Slant project, which analyzed media bias and political slant across Central European media outlets. She is also involved in the DISINFO-PROMPT project, where she conducts dynamic network analysis examining the spread of textual narratives on social media data. Additionally, she participates in the MORES (Moral Emotions in Politics: How They Unite, How They Divide) project, focusing on multilingual modeling and text analysis for emotion analysis of texts.
Anna TAKÁCS
Anna Takács is a data scientist at the poltextLAB artificial intelligence laboratory of the ELTE Centre for Social Sciences. She is currently a PhD student at the Doctoral Program in Economics at the Corvinus University of Budapest. She has earned her bachelor’s degree in Applied Economics from Corvinus University of Budapest, and her master’s degree in International Economy and Management at John von Neumann University.
In the poltextLAB, she has participated in the V-SHIFT Momentum research project, as well as the creation of the ParlText and ParlLawSpeech datasets.
She also partook in several educational and training programs as a presenter, such as "Text mining and Artificial Intelligence in R".
Anna Takács is a data scientist at the poltextLAB artificial intelligence laboratory of the ELTE Centre for Social Sciences. She is currently a PhD student at the Doctoral Program in Economics at the Corvinus University of Budapest. She has earned her bachelor’s degree in Applied Economics from Corvinus University of Budapest, and her master’s degree in International Economy and Management at John von Neumann University.
In the poltextLAB, she has participated in the V-SHIFT Momentum research project, as well as the creation of the ParlText and ParlLawSpeech datasets.
She also partook in several educational and training programs as a presenter, such as "Text mining and Artificial Intelligence in R".
Anna Takács is a data scientist at the poltextLAB artificial intelligence laboratory of the ELTE Centre for Social Sciences. She is currently a PhD student at the Doctoral Program in Economics at the Corvinus University of Budapest. She has earned her bachelor’s degree in Applied Economics from Corvinus University of Budapest, and her master’s degree in International Economy and Management at John von Neumann University.
In the poltextLAB, she has participated in the V-SHIFT Momentum research project, as well as the creation of the ParlText and ParlLawSpeech datasets.
She also partook in several educational and training programs as a presenter, such as "Text mining and Artificial Intelligence in R".
Feedback on Our Courses and Instructors
It was very comprehensive; as a non-expert I found it highly informative, and it has inspired me to start learning more about it.
Participant of the “Introduction to Prompt Writing” course
8 October 2024
It was very comprehensive; as a non-expert I found it highly informative, and it has inspired me to start learning more about it.
Participant of the “Introduction to Prompt Writing” course
8 October 2024
It was very comprehensive; as a non-expert I found it highly informative, and it has inspired me to start learning more about it.
Participant of the “Introduction to Prompt Writing” course
8 October 2024
It was so great, the only thing missing was the scones!
Participant of the Generative AI training
15 October 2024
It was so great, the only thing missing was the scones!
Participant of the Generative AI training
15 October 2024
It was so great, the only thing missing was the scones!
Participant of the Generative AI training
15 October 2024
I did not attend as a researcher, but I gained a very comprehensive and up-to-date overview of the current state of generative AI.
Participant of the Generative AI training
22 May 2025
I did not attend as a researcher, but I gained a very comprehensive and up-to-date overview of the current state of generative AI.
Participant of the Generative AI training
22 May 2025
I did not attend as a researcher, but I gained a very comprehensive and up-to-date overview of the current state of generative AI.
Participant of the Generative AI training
22 May 2025
There was a lot of information in a short time; I will need more time to process it, but that is not a bad thing!
Participant of the Generative AI training
22 May 2025
There was a lot of information in a short time; I will need more time to process it, but that is not a bad thing!
Participant of the Generative AI training
22 May 2025
There was a lot of information in a short time; I will need more time to process it, but that is not a bad thing!
Participant of the Generative AI training
22 May 2025
I would not have minded if the course had lasted a few hours longer (of course, if you could also manage it), so that we could hear more about different prompts, common mistakes, and so on.
Researcher at the Institute of Experimental Medicine
6 June 2025
I would not have minded if the course had lasted a few hours longer (of course, if you could also manage it), so that we could hear more about different prompts, common mistakes, and so on.
Researcher at the Institute of Experimental Medicine
6 June 2025
I would not have minded if the course had lasted a few hours longer (of course, if you could also manage it), so that we could hear more about different prompts, common mistakes, and so on.
Researcher at the Institute of Experimental Medicine
6 June 2025
On our part, we could have provided more tasks to work on, whereas I found the teaching on your side to be very good.
Researcher at the Institute of Experimental Medicine
6 June 2025
On our part, we could have provided more tasks to work on, whereas I found the teaching on your side to be very good.
Researcher at the Institute of Experimental Medicine
6 June 2025
On our part, we could have provided more tasks to work on, whereas I found the teaching on your side to be very good.
Researcher at the Institute of Experimental Medicine
6 June 2025