Entrepreneurship & Innovation
The same methods that anchor my research (field experiments, A/B testing, and applied machine learning) also shape how I build. I'm interested in ventures where empirical methods and real classroom or platform constraints meet.
Co-Founder | Ednius
June 2024 – Present
"A student, a professor, and a problem worth solving."
Students wait weeks for feedback that's often too generic to act on. Educators spend 10–22 hours a week grading per course, and by the time responses arrive, the learning moment is gone. Ednius is an AI-assisted grading platform for universities and certification bodies that closes that gap, generating personalized, rubric-aligned feedback on open-ended student work while keeping the educator firmly in the loop on every approval.
- Submissions graded
- 60,000+
- Accuracy
- 97%
- Time saved
- ~70%
- Student satisfaction
- 95%
The platform is in use across institutions in North America and Asia. It handles essays, equations, code, diagrams, and handwritten work; integrates with Canvas out of the box (Blackboard and Moodle available on request); and is aligned with FERPA, GDPR, and SOC 2 standards, with no student PII stored or used for AI training.
Four principles anchor the product: educators retain final decision-making authority, student privacy is protected, feedback is prioritized over grades, and evaluation standards stay consistent across assignments and graders.
The product is built the way I run a study: every feature ships behind an A/B test, and design decisions are driven by field-experiment evidence rather than intuition.
Learn more or get in touch: www.ednius.com
Call for Research Collaboration
Ednius sits at the intersection of generative AI, higher education, assessment, and human-in-the-loop decision systems, and it raises research questions I can't answer on my own. If you're a researcher, doctoral student, or practitioner working on any of the directions below (or something adjacent), I'd like to hear from you. I'm open to co-authored studies, field experiments run on the platform, and student projects.
- Trust calibration in human–AI grading. When does an instructor over-trust an LLM's suggested score, when do they under-trust it, and what interface cues push that calibration toward the truth? A natural setting for online experiments with real graders.
- Feedback that actually changes behavior. Not all feedback is acted on. Which structural features of AI-generated feedback (specificity, framing, timing relative to the learning moment) measurably improve subsequent student performance? A field-experiment question that can run on a live platform.
- Fairness and bias auditing in LLM-assisted assessment. Do LLM scoring patterns differ across student writing styles, English fluency, or disciplinary conventions in ways that traditional rubric analysis would miss? Has direct implications for policy and accreditation review.
If any of this resonates, or if you have a research idea I haven't listed and want a live platform and dataset to test it on, please reach out.