This portfolio explores how AI can support learning without hiding uncertainty, weakening learner agency, or replacing professional judgment. The projects focus on observable learning processes, transparent analytics, responsible decision support, and research designs that can be challenged and extended.
Generative AILearning AnalyticsHuman and AI CollaborationResponsible AIExplainable AIMultimodal AnalyticsPrivacy Preserving MLProcess MiningClassroom DiscourseCollaborative Learning
These repositories are working research prototypes rather than claims of completed empirical studies. Their demo outputs use synthetic data unless the repository states otherwise. Each project keeps assumptions, metrics, code, tests, and limitations visible so the next step can be real validation rather than a polished black box.
Generative AILearner agency
GenAI Learning Observatory
Analyzes learner interaction with generative AI through verification, revision, reflection, and adoption behavior.
Keeps event data, feature extraction, aggregation, and reporting separate so each signal can be traced to the underlying interaction.
Tests whether an educational AI explanation follows the model behavior it claims to explain.
Implements comprehensiveness, sufficiency, stability, and a combined faithfulness view while keeping plausible explanations separate from faithful ones.
Turns possible cognitive offloading into transparent behavioral hypotheses using revision, verification, recall, confidence change, and similarity signals.
Treats the resulting score as an analytic proxy rather than a diagnosis of what a learner is thinking.
Metrics should be connected to observable data and assumptions that another researcher can inspect.
Human judgment remains visible
AI is treated as a source of evidence or decision support, not as an unquestioned replacement for teachers, learners, or researchers.
Synthetic data are labeled
Demonstration outputs are clearly separated from empirical findings. Synthetic results are useful for testing a method, not for claiming an educational effect.
Reproducibility before complexity
Small auditable baselines make it easier to test assumptions before moving to larger models, real institutional data, or higher stakes deployment.
Contact
For AI in Education research collaboration, doctoral discussions, or questions about these prototypes, you can reach me through the channels below.