Professur für Technology Enhanced Learning (deutsch)
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Imen Azaiz

Imen Azaiz, M.Sc.

Doctoral Researcher

Kontakt

Ludwig-Maximilians-Universität München
Institut für Informatik
Lehr- und Forschungseinheit für Programmiersprachen und KI
Oettingenstraße 67
D-80538 München

Raum: D U104
Telefon: +49-89-2180-9318

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Imen Azaiz received her Master’s degree in Computer Science from LMU Munich in 2020 and is a certified Artificial Intelligence Engineer. She is a research assistant and a Ph.D. candidate in Computer Science. She is part of the BMBF-funded AIM@LMU project, where she contributes to the development of the new minor in Artificial Intelligence, with a focus on programming education, including the design and implementation of the AI-Programming Practicum.

Research Interests:

  • Technology-Enhanced learning / Learning Analytics
  • (Competency-oriented) Feedback and E-assessment Systems
  • Generative AI in Computer Science Education
  • Process Mining

Publications:

ORCiD: https://orcid.org/0009-0005-6458-4169

 

Publikationen

  • Azaiz, Imen; Kiesler, Natalie; Strickroth, Sven; Zhang, Anni (2025, July). Open, Small, Rigmarole – Evaluating Llama 3.2 3B's Feedback for Programming Exercises. In: International Journal of Engineering Pedagogy (iJEP) 5/15, pp. 57–73, DOI 10.3991/ijep.v15i5.55359. [ BibTex ]
  • Azaiz, Imen; Kiesler, Natalie; Strickroth, Sven (2025, to appear). Generating Feedback for Programming Exercises with OpenAI’s o1-preview. In: Future of Learning with Large Language Models: Applications and Research in Education, CRC Press, pp. 225–241.
  • Azaiz, Imen; Felippo, Konrad; Strickroth, Sven (2025, accepted). Small but Competitive – Evaluating DeepSeek-R1 Among Diverse Open LLMs for Formative Programming Feedback. In (Greubel, André; Strickroth, Sven; Striewe, Michael (eds.)): Proceedings of the 7th Workshop "Automatische Bewertung von Programmieraufgaben" (ABP2025), 7 pages.
  • Azaiz, Imen; Strickroth, Sven (2025, accepted). Aligning Teaching Objectives with Formative Assessment to Uncover Student Learning Challenges. In: Proceedings of DELFI 2025 – Die 23. Fachtagung Bildungstechnologien, Gesellschaft für Informatik e.V..
  • Strickroth, Sven; Azaiz, Imen (2025). Qualitative Analysis of Peer Reviews of a Large Introductory Programming Course. In: Computer Science Education, Routledge, 21 pages, DOI 10.1080/08993408.2025.2450587. [ BibTex, PDF ]
  • Azaiz, Imen; Kiesler, Natalie; Strickroth, Sven (2024). Feedback-Generation for Programming Exercises With GPT-4. In: Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1, ITiCSE 2024, Association for Computing Machinery, pp. 31–37, DOI 10.1145/3649217.3653594. [ BibTex, PDF ]
  • Azaiz, Imen; Strickroth, Sven (2024). Supporting Constructive Alignment: Visualization of the Alignment of Teaching Goals and Developed Assignments. In: Proceedings of DELFI 2024 – Die 22. Fachtagung Bildungstechnologien, Gesellschaft für Informatik e.V., DOI 10.18420/delfi2024_34. [ BibTex ]
  • Azaiz, Imen; Deckarm, Oliver; Strickroth, Sven (2023, December). AI-enhanced Auto-Correction of Programming Exercises: How Effective is GPT-3.5? In: International Journal of Engineering Pedagogy (iJEP) 8/13, pp. 67–83, DOI 10.3991/ijep.v13i8.45621. [ BibTex, PDF ]
  • Richter, Florian; Zellner, Ludwig; Azaiz, Imen; Winkel, David; Seidl, Thomas (2019). LIProMa: Label-Independent Process Matching. In (Di Francescomarino, Chiara; Dijkman, Remco; Zdun, Uwe (eds.)): Business Process Management Workshops, Springer International Publishing, pp. 186–198. [ BibTex ]