A: Video papers offer several distinct advantages that enhance transparency, reproducibility, and accessibility compared to conventional text‐only publications: Enhanced Clarity & Reproducibility Visual Protocols: Viewers can observe exact experimental setups, assembly steps, and real‐time procedural nuances — details that static figures or written methods often omit. Immediate Context: By showing microfluidic flows, microscope calibration, or nanomaterial manipulations directly, video papers eliminate guesswork and reduce misinterpretation, leading to higher reproducibility across laboratories. Accelerated Learning & Interdisciplinary Adoption Intuitive Demonstrations: Students and researchers unfamiliar with a technique can “see” how it’s done — camera angles, tool handling, timing—rather than relying solely on textual descriptions. This accelerates uptake of new methods, bridging gaps between fields such as biology, materials science, and engineering. Reduced Barriers for Newcomers: Even seasoned researchers transitioning into adjacent disciplines benefit from visual context, which can shorten the learning curve for complex workflows. Improved Engagement & Retention Dynamic Storytelling: Combining narrated explanations with live footage and on‐screen annotations makes scientific narratives more engaging. Users can pause, rewind, or zoom in on critical steps, fostering deeper comprehension. Multimodal Learning: By integrating auditory, visual, and textual information, video papers address diverse learning styles, improving retention of key concepts and protocols. Global Accessibility & Inclusivity Language & Literacy: In regions where English proficiency varies, a demonstration can transcend language barriers. Viewers see exactly what reagents, instruments, and timings are required without relying on complex jargon. Open‐Access Distribution: SciVid’s video papers are free to publish and view, ensuring that high‐quality experimental recordings reach researchers, educators, and students regardless of institutional budget constraints. Rigid Peer Review with Multimedia Standards Dual‐Track Model: SciVid employs both internal editorial checks and formal peer review. Reviewers assess not only scientific rigor and data integrity but also multimedia quality — ensuring that videos faithfully represent experimental conditions. Metadata & DOI Integration: Every video paper receives a DOI and standardized metadata, allowing traditional indexing, citation tracking, and impact metrics to reflect the multimedia contribution. Future‐Ready Scholarly Communication Complementary to Text: Video papers do not replace text but augment it. Authors provide a written summary of results and a detailed video of methods, creating a richer, hybrid record. Foundation for FAIR Science: By making research Findable, Accessible, Interoperable, and Reproducible, video papers align closely with open science initiatives and funder requirements. In summary, video‐based articles overcome many limitations of text‐only publications by offering unambiguous, step‐by‐step visualizations of experimental workflows. This leads to greater reproducibility, faster knowledge transfer across disciplines, and more equitable access to detailed protocols, ultimately accelerating scientific progress and innovation.

A: This workshop is tailored for individuals at every stage of a STEM career who wish to enhance their research and publishing skills using ethical AI tools. It is ideal for: Graduate students, PhD candidates, and postdoctoral researchers seeking to streamline literature reviews, gap analyses, and manuscript drafting; Early-career and senior researchers looking to adopt next-generation AI workflows and interdisciplinary co-authorship practices; Talented high school students enrolled in STEM-focused or extracurricular programs who want to gain hands-on experience with scientific publishing and mentorship; Undergraduate students preparing competitive graduate-school applications and summer research projects; Industry professionals interested in translating technical work into peer-reviewed publications; and Lifelong learners committed to ethical, AI-driven innovation in academia.

A: Ethical AI usage in academic publishing ensures that artificial intelligence enhances and does not undermine — research integrity, transparency, and scholarly rigor. Key principles include: Full Disclosure & Attribution • Whenever an AI tool contributes text, figures, or data analysis, authors must explicitly note its involvement (e.g., “Initial draft outline generated with AI assistance” or “Figure created using an AI visualization platform”). • Details about the specific model/version (e.g., “ChatGPT-4, April 2025 snapshot”) and any prompt parameters should be included so that peers can understand exactly how AI was used. Avoiding Plagiarism & Ensuring Originality • AI can inadvertently reproduce existing text or images. All AI-generated content must be run through a plagiarism checker (e.g., iThenticate) and rephrased or cited appropriately if there is any overlap with published material. • When using AI-generated figures or diagrams derived from copyrighted sources, authors must secure proper permissions or use only content explicitly licensed for reuse. Maintaining Human Oversight & Responsibility • Researchers remain fully accountable for the intellectual content — hypotheses, data interpretation, and conclusions. AI suggestions (e.g., literature summaries or writing prompts) must be critically evaluated and, if necessary, corrected by the author. • Core reasoning tasks (e.g., experimental design or statistical analysis) should never be delegated entirely to an AI without careful human review. Authors must confirm that every claim, method step, and conclusion is valid. Transparency in AI-Assisted Editing & Review • If AI tools (e.g., Grammarly Academic, Writefull, or AI proofreading services) are used to polish grammar, improve clarity, or format references, participants must list these services in the acknowledgments or methods section. • Any AI-assisted edits must be verified to ensure they do not introduce factual errors or bias. All rewrites should maintain technical accuracy and the authors’ original meaning. Mitigating Bias & Ensuring Fairness • AI models often reflect biases present in their training data (e.g., underrepresentation of non-English literature or skewed topic coverage). Authors should not assume AI recommendations are complete and must cross-check AI-identified keywords, citations, or summaries with manual searches. • If AI suggestions appear to favor certain geographic regions, publication types, or author demographics, participants must conduct additional literature reviews to achieve a balanced perspective. Protecting Sensitive Data & Privacy • When processing confidential or unpublished data (e.g., patient images, private lab notebooks), only AI platforms that guarantee data privacy and comply with relevant regulations (e.g., GDPR) should be used. • Raw, sensitive data should not be submitted to public AI services unless explicit permission has been obtained or data has been fully anonymized. Adhering to Journal & Institutional Guidelines • Most peer-reviewed journals now require authors to disclose AI usage in manuscripts. Before submission, participants must review the target journal’s AI policy and follow its rules regarding AI-generated content, authorship, and figure preparation. • If there is any uncertainty, an “AI Disclosure” subsection should be included in the methods section describing which AI tools were used and how outputs were validated. Reproducibility & Verifiability • AI-related code, prompt logs, and any custom scripts must be archived in a public repository (e.g., GitHub or an institutional server). A link to this repository should be provided in the manuscript so reviewers and readers can reproduce AI-assisted steps. • All relevant settings — model version, temperature or randomness parameters, and filtering rules—should be specified so that future researchers can replicate the AI-driven processes. By following these guidelines — documenting AI usage, verifying outputs, protecting data privacy, and maintaining human accountability, authors uphold the core values of academic publishing: honesty, transparency, and reproducibility. This ethical approach strengthens the credibility of research and paves the way for AI to become a trusted partner in scholarly communication.

A: No. While original laboratory or field-work can enhance a submission, SciVid accepts multiple types of video papers that do not rely on wet-lab experiments. Suitable formats include: Computational or Simulation Studies: Record live screen captures of code execution, data analysis pipelines, or finite-element/ molecular-dynamics simulations. Narrate each step so viewers can reproduce your workflow. AI-Assisted Literature Reviews & Gap Analyses: Use AI tools to map citation networks and emerging trends. A narrated video explaining your search strategy, semantic gap scoring, and key insights can itself be a publishable contribution. Method Development Demonstrations: Demonstrate new protocols or software tools (e.g., custom image-processing scripts, novel statistical workflows) by walking the viewer through installation, parameter choices, and sample outputs. Collaborative Projects: If you partner with lab-based colleagues, you can focus on video production and data interpretation while your co-authors provide experimental footage. The final video-text manuscript documents both the experimental procedures and your analytical or narrative contributions. As long as every protocol, analysis, or algorithm is captured on video—whether in a physical lab, a computer simulation, or an AI-driven pipeline—and accompanied by clear text explanations, your paper meets SciVid’s criteria. The essential requirement is transparent, reproducible documentation (text + video), not the specific source of data.

A: Instead of a traditional exam, participants final “test” is the successful co‐authored video+text manuscript draft. Once that draft meets SciViD internal review criteria and participants have fulfilled attendance and participation requirements, they will receive a Certificate of Completion — an official recognition that participants have mastered ethical AI tools, collaborative co‐authorship, and the SciVid video‐publication workflow. This certificate can be used to bolster an academic portfolio, showcase next‐gen publishing skills, and unlock further mentorship opportunities (e.g., advanced training programs in science and innovations) in our ongoing SciVid community.

A: In addition to the full-day workshop, every participant is invited to join our one-month online co-authorship mentorship program, where interdisciplinary teams continue refining their manuscripts under expert guidance. During this month, you will: Attend up to four live virtual sessions to troubleshoot experiments, refine figures, and revise text; Collaborate asynchronously with co-authors via our secure platform (shared folders, version control, discussion threads); Receive personalized feedback on both scientific content and multimedia elements (narration, on-screen annotations, video editing). If you require even more time beyond the initial month, you can maintain a monthly subscription for continuing mentorship and co-authoring support. This ongoing access allows you to: Schedule additional one-on-one consultations with the instructor; Submit revised drafts for incremental review (text and video) at no extra charge; Remain part of the NanoTRIZ/SciVid community, where you can post questions, share progress, and enlist new collaborators. The subscription model is fully flexible — you only pay for the months you actively use, and you may cancel at any time. This ensures that whether you are a busy postdoctoral researcher, a senior scientist balancing multiple projects, or a talented high-school student building a competitive portfolio, you have adequate time and support to complete a high-quality, peer-reviewable video+text manuscript.

A: Co‐authoring a video‐format paper showcases an applicant’s mastery of both cutting‐edge research and innovative communication — qualities that top universities highly value. By producing a hybrid video+text manuscript, an applicant demonstrates: Technical Proficiency & Initiative: Crafting a research video requires not only rigorous experimental design but also skillful video production (camera setup, lighting, narration). This signals to admissions committees that the student is adept at translating complex methods into clear, reproducible demonstrations. Interdisciplinary Collaboration: Video papers often involve teamwork — bringing together peers with complementary expertise (e.g., a biologist, an engineer, a programmer). Successfully co‐authoring and co‐producing such multimedia projects attests to the applicant’s ability to coordinate across disciplines, an increasingly important trait for modern STEM programs. Advanced Communication Skills: Universities seek candidates who can explain and visualize scientific concepts to diverse audiences. A polished video paper highlights the applicant’s capacity for effective oral and visual storytelling, going beyond traditional written papers to show real‐time protocols, data acquisition, and analytical reasoning. Evidence of Research Impact: Having a co‐authored video paper — especially one peer‐reviewed and DOI‐indexed in SciVid — provides tangible proof of scholarly contribution. Admissions committees can directly observe the quality of methods and results, rather than relying solely on abstract descriptions. Stronger Recommendations & Portfolio: Faculty supervisors working with students on video papers can write more concrete, evidence‐based recommendation letters, citing specific contributions to experimental design, data visualization, or multimedia editing. This deepens the credibility of the application and can tip the balance in competitive selection processes. In sum, participation in co‐authored video‐based publishing signals to universities that the applicant possesses technical rigor, interdisciplinary collaboration skills, and exceptional scientific communication — attributes that set them apart in highly selective STEM programs.

A: Participants may request a full refund up to seven days before the workshop’s official start date by emailing founder@nanotriz.com. Cancellations made after that deadline are not eligible for a refund, but participants can request a deferral of enrollment, allowing their payment to be applied to a future cohort of the same program or toward another eligible course offered by NanoTRIZ Innovation Institute.

A: No. While participants receive structured mentorship, AI‐assisted tools, and simulated peer‐review feedback to develop high‐quality manuscripts or video papers, actual publication depends on factors outside the academy’s control, including: Scholarly Rigor & Novelty: Journals assess submissions on methodological soundness, originality, and impact. Even with strong guidance, acceptance is contingent on meeting those standards. Editorial & Peer‐Review Decisions: Once a manuscript is submitted (to SciVid or another journal), peer reviewers and editors determine whether revisions are sufficient or if the paper should be rejected. Scope & Fit: Each journal has specific aims and criteria. A manuscript may be declined if it does not align with that journal’s focus, regardless of its overall quality. Timely Revisions: Publication often requires multiple rounds of revision. If co‐authors cannot address reviewer feedback promptly or satisfactorily, the submission may remain under revision indefinitely. What Is Guaranteed: A Manuscript Draft Meeting Internal Standards: Upon completing the workshop and co‐authorship sessions, participants will have produced a draft (video + text) that satisfies SciVid’s internal review criteria. Ongoing Support for Submission: Participants can continue working with mentors to refine their draft before formal submission, increasing the likelihood of acceptance, but no outcome is assured. Ultimately, final acceptance rests with the chosen journal’s editorial process and reviewer evaluations. By the end of the program, participants will have the skills, tools, and a solid draft, but publication itself cannot be guaranteed.

A: While many workshops cover AI ethics or basic manuscript preparation, our program is uniquely comprehensive and future-focused because it: Integrates Video-Based Publishing (SciVid) from Day One Most AI-ethics trainings stop at text-only manuscripts. We guide participants to produce hybrid video+text papers for SciVid — an open-access, peer-reviewed journal. This hands-on emphasis on video documentation ensures that complex methods are transparent and reproducible, setting it apart from standard publishing courses. Couples Ethical AI with Real-World Co-Authorship Mentorship Instead of a one-off seminar, attendees join a full day of immersive AI-tool training followed by a one-month online co-authoring program. This extended mentorship — rare in short courses — mirrors an authentic publishing workflow, from gap analysis to final submission, under the guidance of an experienced academic. Leverages a Proprietary Semantic-AI Engine for Research-Gap Discovery Rather than teaching only generic literature searches, participants use our in-house semantic-AI platform to cross-reference publications, patents, researcher expertise, and available lab equipment. This precision gap analysis surfaces novel, high-impact topics that typical “AI ethics” workshops cannot replicate. Simulates a Full Peer-Review Process with Multimedia Standards We replicate SciVid’s dual‐track editorial model — internal editing followed by optional full peer review — using tools like Writefull, iThenticate, and Grammarly Academic. Participants draft video and text components, exchange manuscripts for critique, and learn to address multimedia feedback. Few programs offer this depth of peer-review simulation, particularly for video submissions. Provides a Curated Suite of 20+ Advanced AI Tools, Ethically Applied Beyond basic AI introductions, attendees gain hands-on experience with a curated toolkit (Perplexity, SciSpace, ResearchRabbit, Claude 3, ChatGPT-4, ThesisAI, and more), all framed by strict ethical guidelines. We emphasize transparent prompt design, proper citation of AI-generated content, and authorship integrity — an angle many AI‐ethics courses only touch on superficially. Caters to a Broad, STEM-Focused Audience Whether a talented high-school student building a research portfolio, a postdoctoral fellow or an industry researcher pivoting to academic publishing, our modular content scales to all proficiency levels. We explicitly address interdisciplinary teams — engineers, biologists, physicists, and beyond — ensuring each participant can contribute based on their expertise. Emphasizes Post-Workshop Continuity & Community After the workshop, participants remain active in NanoTRIZ’s SciVid community, accessing new AI-tool tutorials, monthly “office hours,” and peer cohorts. This ongoing network fosters collaboration, supports troubleshooting in real time, and keeps researchers updated as SciVid adds DOI indexing and future impact-factor tracking. By weaving video-format publishing, deep AI-driven gap analysis, rigorous ethics training, and extended co-author mentorship into one cohesive curriculum, this workshop transcends what traditional AI-ethics or publishing programs offer — empowering participants to produce truly reproducible, high‐impact research in both text and video formats.

A: This workshop is open to a wide range of STEM enthusiasts — from talented senior‐high‐school students in extracurricular STEM tracks to undergraduate and graduate students, PhD candidates, postdoctoral researchers, and early‐career scientists. While no formal research publications are required, successful participants typically demonstrate: Strong STEM Interest & Curiosity: A genuine passion for science, engineering, or mathematics and a willingness to learn new methods (AI tools, video production, and collaborative writing). Basic Research Literacy: Familiarity with scientific concepts, the structure of a journal article, and fundamental laboratory or field‐work practices. If you have conducted any course‐based or extracurricular research projects, that experience will help you engage more deeply. Proficiency in English: The workshop is conducted in English, and participants must be comfortable reading, writing, and discussing scientific content in English. Technical Comfort with Computers: You should be able to install and operate standard software (web browsers, video‐editing apps, reference managers) and follow step‐by‐step instructions for AI‐tool interfaces. Prior coding experience is not mandatory, but basic file management skills are helpful. Regardless of formal credentials, we welcome any motivated learner — be it a gifted high‐school student preparing a competitive university application, an undergraduate seeking advanced publishing skills, or a postdoctoral fellow aiming to expand into new interdisciplinary areas. The key qualifications are curiosity, academic motivation, and a readiness to collaborate on ethical, AI‐driven scientific communication.