PosterAn Experts’ Report on Academic Integrity in the AI Era
A thought-leadership report exploring how institutions can move beyond AI detection toward transparency, shared responsibility, and process-based academic integrity.
PosterPoster Discussion at Charleston Conference 2025, USA by Rebecca Bryant
Beyond Detection: Restoring Confidence in Academic Integrity in the Age of AI
PresentationPresentation at Charleston Conference 2025, USA by Rebecca Bryant and Rachel Riffe-Albright
Shaping the Future of Learning: Proactive Strategies for Addressing the Ethical Challenges of AI Use in Higher Education
Who Owns the Work? AI, Integrity, and Student Responsibility in US Higher Education
As AI reshapes higher education, universities face a critical question: Who owns the work? This whitepaper explores practical strategies to strengthen academic integrity.
Key Takeaways:
- Why AI detection cannot determine student work.
- Difference between assistive and substitutive AI use.
- How process visibility builds trust between students and faculty.
- 5 principles for supporting responsible AI use.

The Evolving Landscape of Academic Integrity in Higher Education
As AI reshapes learning, research, and assessment, universities face academic integrity challenges. This whitepaper explores institutional perspectives and strategies to address them.
Key Takeaways:
- How AI is reshaping learning, redefining what constitutes original work in academia.
- Faculty AI literacy gap and inconsistent institutional responses to AI use.
- Crisis of trust that is emerging from over-reliance on detection tools and policy inconsistencies.
- Pedagogical approaches universities can adopt to promote responsible AI use and rebuild trust.

Advancing Multilingual Grammar Correction for Low-Resource Languages
Presented by Divesh Kubal and Apurva Nagvenkar from our Data Science team at the Industry Track of COLING 2025, Dubai.
Key Takeaways:
- Introduces a novel end-to-end architecture utilizing the M2M100 multilingual transformer model.
- Proposes synthetic data generation pipeline, tailored to address language-specific errors.
- Focuses on low-resource languages, like Spanish.
- 88.2% acceptance rate based on user actions.

Comparison of Trinka with Grammarly and LanguageTool on Academic Text
This whitepaper provides a data-backed understanding of how Trinka is best suited for academic writing, balancing accuracy, coverage, and publication-readiness features.
Key Takeaways:
- Specific pain-points of writers, authors, and professional academic writers worldwide.
- Workability and limitations of currently available writing assistants.
- Brief comparison of Trinka vs. Grammarly vs. LanguageTool.
- Trinka edges over all other writing assistants.








