Research background: I obtained my PhD at KULeuven on: (1) Model-Driven Engineering (generating soft/web backend/frontend/UI/database) to support simulation/testability, semantic/syntactic validation of business requirements represented as models (UML/XML/text), and (2) feedback based on behavior and process analytics (nominated for university-wide educational prize on innovative feedback at KU Leuven, 2016). During my PhD research I obtained research grants on novel data-driven feedback approach enabling research visits at Learning & Educational Technology research group at Oulu University in Finland (2015), and Welten Institute for Teaching and Technology in the Netherlands (2016) where I explored biofeedback opportunities with multi-modal data from wearable sensors. These collaborations served an inspiration for my PROFEELEARN project, for which I subsequently obtained an individual postdoc funding (KULeuven).
Education background: Prior to my PhD research I followed 4 studies with degrees in Management Information Systems (KU Leuven, Belgium, 2012), Computer & Information Science (American University of Armenia, 2007), post-university study in Public Administration (Armenia, 2000), Philosophy & Logic (Yerevan State University, 1998), Physics & Mathematics (High School at Yerevan State University, 1993). Committed to lifelong learning, I recently completed a short course on AI in Medicine: Foundations and Applications from Harvard Medical School, USA, (2025).
Research expertise: My research areas include process/behavior- analytics, feedback & visual analytics dashboards, recommenders, model-based engineering (low code), business intelligence applications, educational technology, explainable AI, human-centered intelligence and risk aware AI integration in sectors such healthcare, food, education.
Teaching: Currently my teaching areas include (1) (Business) Information Systems and Information Management (2) Business Intelligence topics (e.g. Business Analytics, Databases, Big Data and Data Mining, Visual Analytics & Dashboards, Process Analytics, Text Analytics, Predictive Analytics, AI) and (3) Low Code application development.
- Tutorial presenter:
- Boosting requirements analysis and validation skills through feedback-enabled semantic prototyping (IEEE RE, 2015)
- Novel way of training conceptual modeling skills by means of feedback-enabled simulation (ER conference, 2015)
Industry experiences: Next to research in academia, I also combine professional experiences from government, banking and software industries (soft/web app development for semi-conductor benchmarking, automotive, research & education management).
Other professional expertise: In addition to research in academia, I combine professional experiences from government, banking and software industries (5 years professional programming, 6 years using programming languages and technologies in daily research using JAVA, #C and related technologies in daily research activities).
Work in academia: Currently I work in the intersection of IEBIS and CODE sections researching shared areas of interest on information and education technologies. Next to UT, I combine research experiences from several other research groups:
- PhD/postdoc - Information systems / Augment (KU Leuven, Belgium)
- Research project manager - IDLab (imec / UGent, Belgium)
- Postdoc - KNOW center (Technical University of Graz, Austria)
- Visiting researcher - Learning and Educational Technology (Oulu University, Finland)
- Visiting researcher - Welten Institute Research Centre for Learning, Teaching and Technology (OUNL)
Awards - The results of my PhD research were nominated in the context of university-wise educational prize for innovative feedback at KU Leuven (2016). As a computer science student I was nominated and awarded with “Best master student in Information Technologies Award (2007)” by the President of Armenia and Synopsys corporation.
Scientific services:
- Associate Editor: Frontiers in Digital Health, Section Health Informatics
- Guest Editor: Interactive visualizations, special issue of the Information journal (ISSN 2078-2489)
- Reviewer: Engineering Applications of Artificial Intelligence (Elsevier), Intelligent Information Systems (Springer), Expert Systems with Applications (Elsevier), Knowledge-Based Systems (Elsevier), SoftwareX (Elsevier), International Journal of Artificial Intelligence in Education (Springer), Computers in Human Behavior (Elsevier), Journal of Medical Internet Research (JMIR), Neuorocomputing (Elsevier), Computers & Education (Elsevier), Transactions on Learning Technologies (IEEE), Learning Analytics (Springer), Computer-Supported Collaborative Learning (Springer), Conferences - LAK, CAiSE.
- Expert: Human-centered AI: responsible integration, risk assessment (TechMed)
- Co-chair: Workshop on controlled vocabularies and data platforms for Smart Food Systems (SmartFood) at ER'23
- Program committee: AMARETTO 2017 at Modelsward, Online Measures for Learning Processes at EARLI SIG 2016
Organisaties
Publicaties
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Overige bijdragen
- Cognitive feedback and behavioral feedforward perspectives for modeling and validation in a learning context, G Sedrakyan, M Snoeck, 2017, Model - Driven Engineering and Software Engineering, Springer
- Evaluating emotion visualizations using AffectVis, an affect-aware dashboard for students, L Derick, G Sedrakyan, PJ Munoz-Merino, C Delgado Kloos, K Verbert, 2017, Journal of Research in Innovative Teaching & Learning 10 (2), 107-125
- Process-mining enabled feedback: “tell me what I did wrong” vs.“tell me how to do it right”, G Sedrakyan, J De Weerdt, M Snoeck, 2016, Computers in human behavior 57, 352-376
- Assessing the influence of feedback-inclusive rapid prototyping on understanding the semantics of parallel UML statecharts by novice modellers, 2016, G Sedrakyan, S Poelmans, M Snoeck, Information and Software Technology 82, 159-172
- Assessing the effectiveness of feedback enabled simulation in teaching conceptual modeling, G Sedrakyan, M Snoeck, S Poelmans, 2014, Computers & Education 78, 367-382
- Do we need to teach testing skills in courses on requirements engineering and modelling? G Sedrakyan, M Snoeck, 2014, CEUR Workshop Proceedings 1217, 40-44
Onderzoeksprofielen
Technically Curious? Turn your skills into cross-domain research-based solutions with graduation theses within Living Models Lab:
Available topics include Recommender systems (education, food, healthcare), LLM-based applications (RAG, AgenticAI), Explainable AI, Business Intelligence, Learning Analytics Dashboards, Process/Behavior analytics, Next generation chatbots for education, workflow automation (healthcare, education) and more.
Verbonden aan opleidingen
Vakken collegejaar 2025/2026
Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.
Vakken collegejaar 2024/2025
Current projects
- Principal researcher - HiCARE framework: Rethinking Reproductive Healthcare with AI: Balancing Opportunities and Risks with Human-Centric AI Recommenders for Responsible Innovations - AI offers significant promise in enhancing diagnostic accuracy enabling data-driven decision-making for personalized preventive and therapeutic interventions. However, its deployment also raises ethical and operational concerns, such as data privacy risks, algorithmic bias, legal complexities, cultural sensitivity, overreliance on AI-generated recommendations, and the potential deskilling of clinicians. Addressing these challenges requires inclusive frameworks for responsible integration. This research explores pathways for responsible and sustainable AI adoption while mitigating associated risks. It introduces the (H)iCARE framework, which advocates for (1) human-centric hybrid models that integrate AI-driven innovations with clinical expertise to ensure balanced innovations. The framework also (2) embraces a broader, humanity-oriented perspective to ensure inclusivity beyond a limited subset of stakeholders, and (3) fosters a learning-driven approach that prioritizes continuous skill development to prevent cognitive complacency. While developed in the context of reproductive healthcare, its principles extend across the healthcare sector, providing a foundation for AI-integrated information system design and a roadmap for ethical, sustainable advancements, additionally fostering discussion on future research priorities. The framework has been presented at International Conference Innovation in Medicine and Healthcare, Solin, Croatia. encouraged by the positive reception from the community and are actively seeking collaborations for operationalizing the framework through partnerships with research and medical centers to advance cross-domain applications.
- Contributor - Redesign4TU: High-tech & data-driven agri-food of the future (Smart FoodTech https://www.4tu.nl/redesign/): The significance of food is evident in the myriad challenges confronting contemporary society, including the increasing prevalence of diet-related diseases, food waste with its adverse economic, environmental, and social impacts, and the significant impact of food production on environmental issues, among others. As the negative health and environmental impacts of dietary patterns become more evident, there is a growing demand for personalized and sustainable food recommendations to promote healthier and planet-friendly choices. In the context of this project I contribute to the domain of food recommenders aiming explainable recommendations for sustainable food production/consumtion based on data from varied sources such as food domain and healthcare. Recently published articles: Design Implications Towards Human-Centric Semantic Recommenders for Sustainable Food Consumption | SpringerLink
- Contributor - Application of LLMs for product management in the automotive aftermarket (To be updated)
- TeToMoCo: The goal of the project is three-fold: 1. TeToMoCo (Text-To-Model-To-Code) framework that combines the state-of-the-art natural language processing approaches and techniques for identifying potential architecture elements candidates out of business requirements articulated in natural language textual description. 2. A subsequent prototype implementation that can assist a knowledge construction process through (semi-) automatic generation and validation of UML models. 3. Automatic web application code generation (backend/frontend/UI) out of generated UML/XML models following principles of Model Driven Engineering. Recently published articles: (1) Towards LowDevSecOps Framework for Low-Code Development: Integrating Process-Oriented Recommendations for Security Risk Management (2) Text-To-Model (TeToMo) Transformation Framework to Support Requirements Analysis and Modeling - University of Twente Research Information
Previous projects
- (Postdoc) PROFEELEARN: Process-oriented assessment and feedback based on learning behavior/process data analytics grounded on the links between information/data analytics and learning sciences (Postdoctoral research funding)
- CITADEL H2020: CITADEL is a European H2020 Project involving twelve partners. These are research institutes, universities, public sector entities and IT companies from five different European countries. The project’s objective is to create an ecosystem of best practices for a transparent, innovative and cooperative public sector and to provides more efficient and inclusive tools to respond to citizen requirements. The CITADEL ecosystem combines and promotes a set of technologies (e.g. semantics, mobile, analytics, sentiment analysis, open linked data) to both empower Public Administrations (PAs) to improve their offering and the engagement of citizens, as well as to foster cooperation among PAs and users of public services in local, regional and national environments. Main contributions as a partner included: 1. an ecosystem architectural guidelines, 2. design and development of a semantic dashboard to support improving public services for e-government at EU public administration organizations.
- HOBBIT H2020: Holistic Benchmarking of Big Linked Data aims at abolishing the barriers in the adoption and deployment of Big Linked Data by European companies, by means of open benchmarking reports that allow them to assess the fitness of existing solutions for their purposes. These benchmarks are based on data that reflects reality and measures industry-relevant Key Performance Indicators (KPIs) with comparable results using standardized hardware. Main contributions as a partner included: 1. benchmark on query answering features for live time series in the form of multidimensional interfaces 2. establishing a taskforce subgroup for Benchmarking under the umbrella of Big Data Value Association with the aim to provide a scalable and FAIR benchmarking platform for data-driven solutions with a focus on AI (especially ML) solutions, corresponding benchmarks, key performance indicators, benchmarking tools and services for the independent, repeatable and scalable benchmarking of data-driven (especially AI) technologies, detecting potential use cases and categories of users as well as potential synergies with existing benchmarking organizations.
Lopende projecten
SMART4L (NWO): een leergemeenschap en platform om (inter)regionale samenwerking voor ICT-gedreven innovatie in logistiek te versnellen
The logistics workforce often lacks the knowledge and skills to utilize the rapidly developing ICT-innovations. Addressing this problem, our SMART4L proposal aims to increase the learning capacity of logistics companies and the employability of the logistics workforce by (re)designing, optimizing, and substantiating three regional learning communities (Port of Twente, Logistics Lab Zwolle and Kansen met Data-Datalab) in which companies, knowledge-, educational-, and sector institutions collaborate. SMART4L will do so with key insights about effective design principles and social processes for collaborative learning and innovation adoption, and by developing a ICT platform to facilitate knowledge sharing and learning among stakeholders.
BuddyGPT:Adapting Large Language Models for a Study Buddy System at UT
This project aims at strategic integration of AI-driven conversational agents, such as ChatGPT, into learning environments like Canvas. The focus is on enhancing both students' and educators' ability to access reliable, personalized information while aligning with learning objectives and course-specific content. By adopting a buddy system approach, proactively, the system would reach out to students with reminders about upcoming deadlines or key tasks, encouraging better time management and engagement. By linking AI to course materials and incorporating learning theories for optimal information transmission, the project seeks to prevent over-reliance on AI for quick answers, instead promoting self-regulation and deep learning, foster collaboration, reflection and critical thinking. Additionally, it aims to equip both educators and students with AI literacy to ensure ethical and responsible AI use.
Recently published articles:
Sedrakyan, G., Borsci S., Machado, M., Rogetzer, P., Mes, M. (2025). Design Implications for Integrating AI-based Chatbots with Learning Management Systems: A Study-based Analysis on Perceived Benefits and Challenges in Higher Education, In International Conference on Artificial Intelligence and Teacher Education.
Sedrakyan, G., Borsci, S., van den Berg, S. M., van Hillegersberg, J., & Veldkamp, B. P. (2024, January). Design implications for next generation chatbots with education 5.0. In International Conference on Advances in Education and Information Technology (pp. 1-12). Singapore: Springer Nature Singapore.
How educational feedback needs changed during the times of Covid pandemics and what are the long-term effects ?
While it is evident that digitalization will be pivotal for accomplishing a transition to post-pandemics educational environments, where hybrid classroom/campus uniting the physical and digital learning experiences will most likely define the new norms, the field lacks insights to guide informed decisions in the domain of feedback digitalization. Despite the importance of this transition world-wide, still questions such as “what is the type of digital feedback that worked best during lockdown education?”, “which new formats used by teachers proved effective among students?”, “are there preferences in these new formats/elements of feedback to continue even when the lockdown education disappears?” remain unanswered. This research seeks to address these questions through empirical studies.
Recently published articles:
Recent talks:
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