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Education Research Grants: congratulations to the 2026-27 winners!

Congratulations to all six winning teams of the 2026–27 EDLAB Education Research Grants. This year’s selected projects bring together colleagues from different faculties, expertise centres and disciplines to address timely questions in higher education, ranging from AI and assessment to self-regulated learning, Project-Centred Learning and tutorial dynamics. We look forward to following these collaborations over the coming year and sharing their insights with the wider UM community.

We received 15 applications in April 2026, covering a wide range of educational research topics. The proposals reflected strong collaboration across faculties and expertise centres, including UNU-MERIT, MSM and the University Library. Members of the EDLAB Education Research Sounding Board (ERSB) independently reviewed and discussed each application before selecting the six projects that will receive funding. Detailed feedback has been provided to all applicants.

Below is an overview of the winning projects. From October onwards, we will share videos in which the grant recipients introduce themselves and their research in more detail.

Name/FacultyProject proposal
Main applicant:
Kazem Banihashem (FHML/O&O)

Co-applicant: 
Desiree Joosten-ten Brinke (FHML/O&O)

Preserving teacher agency in AI-supported education: validating a pedagogical model for hybrid intelligent feedback in Problem-Based Learning

Providing personalised feedback is often one of the most time-intensive aspects of teaching. This project evaluates a pedagogical framework that integrates AI into feedback practices within Problem-Based Learning. Through expert validation and consultation with UM teachers, the project aims to develop a structured framework for the responsible and effective use of AI in feedback.

 

Main applicant: 
Sina Gottschlich (FHML)

Co-applicant: 
Lisa Goller (FPN)		Exploring students’ use of generative AI to plan and reflect on their study process

As generative AI becomes increasingly embedded in students’ learning practices, understanding how it influences self-regulated learning is becoming more important. This project explores how students use generative AI to plan, monitor and reflect on their learning processes through personal portfolios. The findings will inform recommendations for educational practice and help identify where additional support may be needed to encourage meaningful and effective use of AI in learning.
 
Main applicant: Evgueni Smirnov (FSE/DACS)

Co-applicant: 
Stefan Straetmans (FIN/SBE)

Reliable semi-automatic grading of open-ended exam questions with LLMs and conformal prediction

This project develops and evaluates a semi-automatic grading system for open-ended exam questions that combines large language models (LLMs) with reliable prediction methods. The aim is to reduce grading workload while maintaining grading quality, consistency, and teacher oversight. The findings will provide insights into how AI can support transparent and responsible assessment practices in higher education.

Main applicant: 
Xiaoling Zhang (FSE/ECO)

Co-applicant: 
Max Sondag (FSE/DACS)		Understanding the impact of Generative AI on learning in DACS semester projects: implications for Project-Centred Learning practice

This project investigates how students use generative AI (GenAI) in semester projects and how these tools influence self-directed and collaborative learning. The findings will inform practical recommendations for tutors and course coordinators, helping them align teaching practices with emerging technologies while preserving the core principles of Project-Centred Learning (PCL).
Main applicant: Irina Dolgopolova (MSCM)

Co-applicant: 
Maisha Fairuz (ALGEC)		The Rubric’s Cube: improving assessment one twist at a time

This project investigates how assessment rubrics can improve transparency, consistency, and student understanding of assessment practices in SBE courses. The findings will inform evidence-based guidelines to support educators in designing and implementing rubrics that promote fairer and clearer assessment across courses.
Main applicant: 
Luisa Bortesi (FSE/SBE)

Co-applicant: 
Khyrstyna Semen (FSE/UCV)		Introducing the Devil's Advocate role in PBL tutorials

This project investigates whether introducing a rotating student "devil's advocate" role can strengthen discussion quality in PBL tutorials. By encouraging students to challenge assumptions, consider alternative perspectives, and ask critical questions, the project aims to promote deeper learning, critical thinking, and more inclusive participation. The findings will inform practical guidelines and a teaching toolkit for wider adoption across Maastricht University.

 

The focus of a typical grant project is on short-term (one year) practice-oriented research into questions directly relating to UM education. Projects involve three modes of engagement:

  • Conducting theoretically informed, methodologically sound and appropriately focused inquiry into aspects of one’s (or a colleague’s) education practice.
  • Connecting with colleagues from both one’s own and other UM faculties/service centres to exchange knowledge and expertise across contexts and disciplines.
  • Communicating about and disseminating the research both within UM and beyond.

The 2026 call invited proposals for education research topics related with one or more of the following general categories:

  • Creative PBL formats optimising CCCS
  • Global Citizenship and transdisciplinary education
  • Staff and student wellbeing
    • Work/study load, Smarter Academic Year
    • Educational culture
    • Advising & mentoring

We wish all grant recipients every success with their projects and look forward to sharing their progress and findings throughout the coming academic year.

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