A data-driven, strategic framework for ethical citation, live metric auditing, and publication-ready research workflows, specifically designed for university faculty, research deans, and doctoral supervisors.
A structured, 3-stage blueprint for institutional transition.
High-impact publishing requires distinguishing clearly between mathematical data augmentation (such as using SMOTE or GAN-driven expansion for sparse clinical/engineering datasets) and algorithmic hallucination. Faculty learn to flag, detect, and isolate phantom citations, fabricated bibliographies, and artificially generated metrics before manuscripts enter the peer-review cycle.
Mastering standardized generative AI usage documentation is no longer optional. This module equips researchers with the exact systems needed to transparently disclose specific model iterations, prompt scopes, temperature settings, and validation protocols, satisfying the strictest global publishing mandates and ethical policies.
Real-time auditing of institutional h-index and i10-index profiles across Google Scholar, Scopus, and Web of Science. Participants gain hands-on training in managing shared, multi-author reference environments (via Zotero and Mendeley) while ensuring metadata integrity and neutralizing predatory citation practices.
In modern academic environments, typical plagiarism checkers fail to trace the nuances of synthetic text generation. This case analysis uncovers the direct operational workflow of Turnitin's structural classifiers when scanning raw, non-audited language model outputs.
Direct evidence artifacts showcasing peer engagement and behavioral engineering methodologies inside university hubs.
A comprehensive, high-impact gathering of university faculty, researchers, and doctoral supervisors collaborating on the zero-trust research integrity framework.
Meta's Galactica language model was specifically trained on scientific papers to accelerate research. However, because it prioritized authoritative-sounding prose over raw empirical truth, it generated massive volumes of fake historical dates, phantom math equations, and non-existent citations. The model was pulled down in under 72 hours, serving as a landmark warning: syntactic fluency is never a substitute for rigorous verification.
Highlighting the severe investigation surrounding former MIT researcher Aidan Toner-Rodgers. The immense academic pressure to generate exciting, groundbreaking conclusions led to massive, irreconcilable discrepancies between raw empirical data and generative-assisted findings. This case resulted in immediate paper retraction and internal administrative action, proving that unchecked AI optimizations can collapse even the most prestigious careers.
A catastrophic $40 Million retraction crisis that forced the permanent shuttering of the Hindawi brand. The collapse was accelerated by coordinated paper-mills leveraging generative AI to exploit peer-review bottlenecks. This case study demonstrates how localized failures in doctoral supervisor oversight can scale into structural institutional disaster and massive global academic disgrace.
Empowering researchers and institutions with practical, compliant validation workflows and standardized verification frameworks.
A deep dive analysis on human creative authorship rules across COPE, WIPO, and IEEE core policies, alongside a practical guide to Elsevier's book and commissioned content AI usage regulations.
DOWNLOAD DAY 1 HANDOUT (PDF)Equipped with the complete structural AI Verification Checklist for faculty across all writing phases and the Standardized 4-Part Formula featuring deployment-ready manuscript templates.
DOWNLOAD DAY 2 HANDOUT (PDF)Verified verification assets from workshop evaluations, captured live during the execution of our research ethics and compliance initiatives.
Very informative FDP conducted by Dr Kant and Dr Vishwajeet. Great value addition.
The session was insightful. Very pragmatically delivered with real life cases. There was lots of learning and reflection. Many ethical takeaways. Role play. Very interactive.
The FDP was very well conducted. It was very engaging throughout. Post lunch sessions were equally engaging.
I really liked the way session has been conducted and detailed information shared with us. We want a practical session on writing good paper.
Engaging session, good. Thanks.
The sessions were very interesting and interactive. Great learnings.
Excellent learning. Resource persons are knowledgeable and friendly to discuss, and answerable. I am happy to join this FDP. Best of luck.
Very informative & engaging!!
The FDP was very interesting and engaging. 5/5
It was an excellent session. I gained lot of insights.
Very interesting and engaging session. A lot of content, examples, data, videos, games, polls and role play. An extremely enriching session.
Very well designed. Engaging, relevant and very informative.
Good idea of having Case studies in the afternoon session.
Informative & relevant workshop.
The session was very well organised and placed out well. There was a very good understanding and bond exhibited by both the trainers. Great Job!
Very informative and important session for a beginner like me. Small points are covered which is very useful. Cleared about the ethical considerations about AI.
Good simulation exercise! Should have more of those! Quiz aspect also interesting!
The FDP was very engaging & interesting. Particular the case activity discussed in the class.
Nice efforts. Awareness regarding AI usage while writing research paper was communicated specially role play was good.
We became aware about Ethics in usage AI in Research.
Banning AI tools in academic environments is statistically impossible to enforce and structurally counterproductive. AI is an incredibly powerful research catalyst when used ethically (e.g., for statistical modeling, editing, or literature sorting). By shifting our focus from absolute prohibition to strict AI Auditing, we empower faculty to verify the logical pathways, data structures, and prompt frameworks of their researchers—preserving academic freedom while guaranteeing absolute intellectual honesty.
Spotting generative anomalies requires a highly systematic data-provenance audit. First, we cross-examine the dataset's standard deviation and distribution patterns; synthetic datasets generated via shallow LLMs or rudimentary heuristics often exhibit unrealistically smooth curves lacking organic noise. Second, we run random, deep metadata checks on reference citations to search for non-existent DOIs. This FDP equips supervisors with the exact mathematical and technical checks needed to identify these digital fingerprints.
Yes, absolutely. Rather than disrupting your current administrative workflow, Dr. Gaike’s framework is built to slot seamlessly into mainstream Learning Management Systems (LMS) like Moodle, Canvas, or Blackboard. We achieve this by establishing structured submission portals where researchers upload their raw data, prompt transcripts, and ethical-use checklists in parallel with their draft chapters, creating an easily reviewable compliance history.
Institutional rankings like NIRF, QS, and Times Higher Education rely heavily on citation metrics, h-index depth, and overall academic reputation. A single major retraction or plagiarism scandal can permanently damage an institution's peer-perception score and automatically disqualify them from premium research grants. By securing your university's research integrity at the department level, you directly insulate and optimize the metrics that drive these high-stakes global rankings.