House of Labour | Eschersheimer Landstr. 155/157
3.OG | Raum 306
60323 Frankfurt am Main

Mail: rasheed@sd.uni-frankfurt.de
Profile: ORCID | LinkedIn | GoogleScholar

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Hasan Abu-Rasheed promovierte 2025 in Informatik an der Universität Siegen, nachdem er seinen Master in Mechatronik ebenfalls an der Universität Siegen abgeschlossen hatte.

Während seiner Promotion arbeitete er an der Erforschung und Entwicklung erklärbarer, kontextbasierter Empfehlungssysteme für die berufliche Weiterbildung sowie für die Hochschulbildung. Dabei entwickelte er sowohl algorithmische Lösungen als auch interaktive Schnittstellen, um den Zielgruppen Erklärungen und Empfehlungen zu bieten. Seine Arbeit bezog Akteure aus dem pädagogischen und psychologischen Bereich mit ein, wobei ein Teil seiner Forschung die Entwicklung von Ansätzen zur Integration von Fachexpert*innen in die Gestaltung von erklärenden Empfehlungssystemen war.

Arbeitsschwerpunkte

• Aufbau des AI-ToolLabs

• Weiterentwicklung und Anpassung von KI-Services und -Technologien für den Einsatz in Lehr- und Lernprozessen

• Entwicklung von Prozessen zur strategischen und operativen Implementierung von KI-Technologien (ChatBots, KI-Tools, Erklärbarkeit, Feedback) an der Goethe-Universität

• Umsetzung der gesetzlichen Vorschriften und Richtlinien zur Nutzung von KI-Technologien in der Lehre (z. B. AI-Act, Datenschutz, Urheberrecht, Studienordnungen, ...)

Forschungsschwerpunkte

• Entwicklung von Methoden zur Erforschung der Integration von KI in Lehr- und Lernprozesse

• Forschung und Entwicklung von Wissensgraphen als Kontextualisierung und semantische Grundlage für KI-gestützte Systeme

• Forschung und Entwicklung von Erklärbarkeit für Entscheidungsunterstützungssysteme und Feedback in der Hochschulbildung

Veröffentlichungen

• Abu-Rasheed, H., Weber, C., & Fathi, M. (2025). Tell Me Why I Should “Not” Follow Your Recommendation: On the Role of Explainable AI in Collaborative Human-AI Decision Making.

• Ilkou, E., Abu-Rasheed, H., Chaves-Fraga, D., Engelbrecht, E., Jiménez-Ruiz, E., & Labra-Gayo, J. E. (2025). Teaching knowledge graph for knowledge graphs education. Semantic Web (Under Review).

• Abu-Rasheed, H., Jumbo, C., Al Amin, R., Weber, C., Wiese, V., Obermaisser, R., & Fathi, M. (2025). Llm-assisted knowledge graph completion for curriculum and domain modelling in personalized higher education recommendations. 2025 IEEE Global Engineering Education Conference (EDUCON), 1–5. IEEE.

• Abu-Rasheed, H., Abdulsalam, M. H., Weber, C., & Fathi, M. (2024). Supporting student decisions on learning recommendations: An llm-based chatbot with knowledge graph contextualization for conversational explainability and mentoring. arXiv Preprint arXiv:2401. 08517.

• Abu-Rasheed, H., Weber, C., & Fathi, M. (2024a). Experimental Interface for Multimodal and Large Language Model Based Explanations of Educational Recommender Systems. arXiv Preprint arXiv:2402. 07910.

• Abu-Rasheed, H., Weber, C., & Fathi, M. (2024b). Knowledge graphs as context sources for llm-based explanations of learning recommendations. 2024 IEEE Global Engineering Education Conference (EDUCON), 1–5. IEEE.

• Abu-Rasheed, H., Ikeda, R., Ferriyan, A., Weber, C., Fathi, M., Okawa, K., & Thamrin, A. H. (2024). Problem-Based Learning-Path Recommendations Through Integrating Knowledge Graphs and Large Language Models.

• Abu-Rasheed, H., Nadeem, M., Dornhöfer, M., Zenkert, J., Weber, C., & Fathi, M. (2024). TExKG in health domain: The application of knowledge graph based framework for explainable recommendations in the contexts of elderly care, mental health, and emergency responses. In Integrated Systems: Data Driven Engineering (pp. 265–285). Springer Nature Switzerland.

• Engelbrecht, E., Ilkou, E., Abu-Rasheed, H., Chaves-Fraga, D., Jiménez-Ruiz, E., & Labra-Gayo, J. E. (2024). Teaching Knowledge Graph for Knowledge Graphs Education. Semantic Web: Interoperability, Usability, Applicability.

• Abu-Rasheed, H., Weber, C., & Fathi, M. (2023). Context Based Learning: A Survey of Contextual Indicators for Personalized and Adaptive Learning Recommendations-A Pedagogical and Technical Perspective. Frontiers in Education, 8. Frontiers.

• Abu-Rasheed, H., Efthymiou, Y., Fathi, M., Ghadamighalandari, P., Medina, J. L., García, C. O., … Zgeras, G. (2023). Supporting Remote Students Through Utilizing Web-Based Exercise-Templates and a Mobile Learning Chatbot for Creating and Interacting with Learning Materials. European Conference on Technology Enhanced Learning, 668–673. Springer Nature Switzerland Cham.

• Abu-Rasheed, H., Weber, C., Dornhöfer, M., & Fathi, M. (2023). Pedagogically-informed implementation of reinforcement learning on knowledge graphs for context-aware learning recommendations. European Conference on Technology Enhanced Learning, 518–523. Springer Nature Switzerland Cham.

• Abu-Rasheed, H., Dornhöfer, M., Weber, C., Kismihók, G., Buchmann, U., & Fathi, M. (2023). Building contextual knowledge graphs for personalized learning recommendations using text mining and semantic graph completion. 2023 IEEE International Conference on Advanced Learning Technologies (ICALT), 36–40. IEEE.

• Kismihók, G., Gaspar, I., Delaney, J., Schroijen, M., Tavakoli, M., Faraji, A., … Others. (2022). OSCAR conceptual and technical framework for researcher well-being and career development training and mentoring. Zenodo.

• Weber, C., Abu-Rasheed, H., & Fathi, M. (2022). Adding context to industry 4.0 analytics: A new document driven knowledge graph construction and contextualization approach. 2022 IEEE International Conference on Electro Information Technology (eIT), 550–555. IEEE.

• Abu-Rasheed, H., Weber, C., Zenkert, J., Dornhöfer, M., & Fathi, M. (2022). Transferrable framework based on knowledge graphs for generating explainable results in domain-specific, intelligent information retrieval. Informatics, 9, 6. Multidisciplinary Digital Publishing Institute.

• Reichow, I., Buntins, K., Paaßen, B., Abu-Rasheed, H., Weber, C., & Dornhöfer, M. (2022). Recommendersysteme in der beruflichen Weiterbildung. Grundlagen, Herausforderungen und Handlungsempfehlungen. Ein Dossier im Rahmen des INVITE-Wettbewerbs. Berlin.

• Abu-Rasheed, H., Weber, C., Zenkert, J., Krumm, R., & Fathi, M. (2022). Explainable graph-based search for lessons-learned documents in the semiconductor industry. Intelligent Computing: Proceedings of the 2021 Computing Conference, Volume 1, 1097–1106. Springer International Publishing.

• Ilkou, E., Abu-Rasheed, H., Tavakoli, M., Hakimov, S., Kismihók, G., Auer, S., & Nejdl, W. (2021). EduCOR: An educational and career-oriented recommendation ontology. International Semantic Web Conference, 546–562. Springer International Publishing Cham.

• Zenkert, J., Weber, C., Dornhöfer, M., Abu-Rasheed, H., & Fathi, M. (2021). Knowledge integration in smart factories. Encyclopedia, 1(3), 792–811.

• Upadhyay, C., Abu-Rasheed, H., Weber, C., & Fathi, M. (2021). Explainable job-posting recommendations using knowledge graphs and named entity recognition. 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 3291–3296. IEEE.

• Abu Rasheed, Hasan, Weber, C., Zenkert, J., Czerner, P., Krumm, R., & Fathi, M. (2020). A Text Extraction-Based Smart Knowledge Graph Composition for Integrating Lessons Learned During the Microchip Design. Proceedings of SAI Intelligent Systems Conference, 594–610. Springer, Cham.

• Tomar, S., Abu Rasheed, H., & Fathi, M. (2020). Educational Multimodal Data Mining and Fusion through Knowledge Graphs for Topic-relation Extraction in Study Recommendations. 12th International Conference on Education and New Learning Technologies (EDULEARN20).

• Abu Rasheed, H., Zenkert, J., Weber, C., Dornhöfer, M., Klahold, A., & Fathi, M. (2019). Language Learning Tool Based on Augmented Reality and the Concept for Imitating Mental Ability of Word Association (CIMAWA). 11th International Conference on Education and New Learning Technologies (EduLEARN19).

• Abu Rasheed, H., Zenkert, J., Weber, C., & Fathi, M. (2019). Conversational Chatbot System for Student Support in Administrative Exam Information. 12th Annual International Conference of Education, Research and Innovation (iCERi2019).

• Ishaq, I., Jayousi, R., Odeh, S., Edwan, E., Shaheen, A., Elnaggar, M., … Others. (2019). Work in Progress--Establishing a Master Program in Cyber Physical Systems: Basic Findings and Future Perspectives. 2019 International Conference on Promising Electronic Technologies (ICPET), 4–9. IEEE.

• Abu Rasheed, Hasan, Weber, C., Harrison, S., Zenkert, J., & Fathi, M. (2018a). Teacher, Student and Domain Based Educational Recommender System for Assessing Student’s Preferences on Multiple Recommendation Sources. The 2nd Annual Learning & Student Analytics Conference (LSAC2018). EasyChair.

• Abu Rasheed, Hasan, Weber, C., Harrison, S., Zenkert, J., & Fathi, M. (2018b). What to Learn Next: Incorporating Student, Teacher and Domain Preferences for a Comparative Educational Recommender System. 10th International Conference on Education and New Learning Technologies (EduLEARN18).