Rianne Conijn

Rianne Conijn is an assistant professor in the Human-Technology Interaction group at Eindhoven University of Technology (TU/e), Netherlands. Her research bridges data-driven methodologies (machine learning, statistical modeling) with human-centered design to enhance learning analytics, explainable AI, and writing process analysis. She holds a joint PhD (cum laude) from Antwerp University and Tilburg University, and an MSc (cum laude) in Human-Technology Interaction from TU/e.

• Academic Background: MSc (2015, TU/e, cum laude), PhD (2020, Antwerp University & Tilburg University, cum laude).
• Research Focus: Learning analytics, keystroke logging, explainable AI for education, data dashboards, and self-regulated learning dynamics.
• Teaching: Courses in Advanced Research Methods, Human-AI Interaction, Behavioral Research Methods, and AI ethics in education.

Her recent publications explore parallel language planning in writing, longitudinal self-regulated learning strategies, and generalizability of academic performance prediction models. She leads an NWO Veni project on Human-Centered AI in education, emphasizing tailored explanations for student-AI collaboration. Scientific awards include cum laude distinctions for her MSc and PhD, and the NWO Veni grant. Collaborative work spans institutions in the Netherlands, Norway, and the U.S., with applications in intelligent tutoring systems and ethical AI deployment in exams.

Key trends across her work: integration of machine learning with educational theory, leveraging keystroke data for cognitive process insights, and prioritizing actionable, explainable AI systems for student support. Publications span journals like the Journal of Experimental Psychology: General, Computers and Education, and IEEE Transactions on Learning Technologies.

• Scientific Awards:
  • NWO Veni grant for Human-Centered AI in education
  • Cum laude for MSc and PhD
• Grants & Collaborations:
  • National Science Foundation grants (2016868, 2302644) for biometric feedback in writing
  • UK Research and Innovation grant (ES/W011832/1) for real-time AI scaffolding
  • TU/e Boost! Program grant for self-regulated learning analysis
• Labs & Teams:
  • EAISI Foundational (Eindhoven AI Systems Institute)
  • Human Technology Interaction group at TU/e
  • Collaboration with Norwegian Reading National Center (University of Stavanger)
  • Project teams for Waterproof ITS and ProWrite grants