Background and General Information
AIDE (AI Driving Education) responds to the growing presence of large language model (LLM) tools like ChatGPT, Cursor, and Copilot in academic environments. These tools are increasingly used by students, influencing both how they learn and how their work is assessed. The proposal addresses this shift by embedding responsible AI tool usage into the learning and assessment structure of two courses: the large-scale Bachelor course “Foundations of Data Analytics” (2IAB1) and the Graduate School course “Process Intelligence in Action” (2AMI30). In 2IAB1, over 1,000 first-year students from multiple study programs learn to analyze data and work on a real-world assignment related to societal challenges. In 2AMI30, students tackle complex data analysis projects in small teams, preparing for real-world applications. Traditional assessment methods have become less effective due to students’ reliance on AI-generated answers, which often lack understanding and critical thought. Rather than prohibiting use, AIDE proposes structured integration of these tools, guiding students to critically assess AI outputs and use them as learning aids. By doing so, the project fosters both digital literacy and reflective thinking, aligning with educational goals and future professional demands.
Goal of the Project
The goal of the AIDE project is to design and implement a responsible and reflective approach to integrating AI tools into university-level teaching and learning. Instead of treating AI use as a form of misconduct, the project aims to help students use these tools effectively while maintaining academic integrity. In both pilot courses, students will complete assignments that incorporate the use of AI tools in a structured and purposeful way. For example, they might use AI to explore data or generate code, but will then be expected to critically assess and revise these outputs, demonstrating their understanding. The project will provide prompts, guidance, and assessment criteria to support this reflective use. By doing so, students will strengthen their reasoning, problem-solving, and critical evaluation skills. The pilot will also examine the effect of AI tool integration on student engagement, learning outcomes, and teaching workload. Feedback and anonymized student-AI interactions will be collected to inform continuous improvement. Ultimately, AIDE aims to build a future-proof educational model that enhances learning through AI while promoting ethical, autonomous, and critical thinking. These are skills that are increasingly vital in both academic and professional contexts.
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Internal projects
- Improving Conformance Checking Algorithms with Ground Truth-Driven Insights - Conformance checking helps us understand how well the process behavior recorded in event logs matches the process behavior prescribed by a process model. The goal of this project is to improve conformance checking methods by using a more reliable way of evaluating and validating them. We will:
- Conformance Checking: Timed, Stochastic, and Systemic Trace Insights - Conformance checking helps us understand how well the process behavior recorded in event logs matches the process behavior prescribed by a process model. The goal of this project is to advance conformance checking methods by integrating timed and stochastic aspects of process behavior and optimizing alignments across entire event logs. We will:
Staff
- Natalia Sidorova - Dr. Natalia Sidorova is assistant professor at the PA group. She actively works on topics related to process modeling and verification. The application domains include business processes and distributed systems. She has published more than 70 conference and journal papers. She is active in the Health and Wellbeing Action Line of EIT ICT Labs, taking Read More ...
- Renata Medeiros de Carvalho - Position: UD Room: MF 7.067 Tel (internal): 4144 Links: Courses External assignments Projects Publications External links: Google Scholar page Scopus page ORCID page DBLP page TU/e page Recent awards Recent courses Recent external assignments Recent projects Recent publications
- Mădălina Rogozan - Mădălina Rogozan is a Ph.D. student at the Eindhoven University of Technology – Data Science Domain – Process Analytics cluster. She works under the supervision of Natalia Sidorova. Position: PhD Room: MF 7.060 Tel (internal): Links: External links Recent courses Recent presentations Recent projects Recent publications