This training program is designed for you to complete on your own time. Each module includes detailed instructional content followed by ten multiple-choice questions with full answer justifications. You should read each section thoroughly before attempting the questions. A score of eighty percent or higher on each module indicates that you have mastered the material.
When you finish this program, you will be able to describe how Generative AI usage has grown in scientific literature following the introduction of widely accessible conversational AI systems. You will know which developers and research domains show the highest adoption rates and what this means for different academic fields.
You will be able to identify many distinct benefits of GenAI for researchers across different stages of the research lifecycle. These stages include literature review, data analysis, manuscript preparation, and public communication. You will also understand the limits of these benefits.
You will recognize the specific risks associated with the use of GenAI in research. These risks include threats to academic integrity, quality assurance systems, and public trust in science, as well as the potential for malicious applications, such as misinformation campaigns.
You will apply established ethical frameworks to evaluate whether a given use of GenAI in research is acceptable, transparent, or requires disclosure. These frameworks include the five moral dimensions of information rights, property rights, accountability, system quality, and quality of life.
You will interpret and compare publisher policies regarding authorship, disclosure statements, reviewer responsibilities, and prohibited uses of GenAI. You will understand why AI cannot be an author and what must be disclosed.
You will detect potential traces of AI-generated text through linguistic patterns. You will understand the limitations of such detection and recognize the implications of undisclosed AI assistance for research integrity.
You will explain the key questions that disclosure statements should answer under the Who, What, Where framework. You will identify the gaps and inconsistencies in current publisher guidelines.
You will assess how GenAI affects education, including concerns about students’ overreliance on AI, the decline in critical thinking skills, and the need for adjusted teaching methods and assessment designs.
This training covers the intersection of large language models and academic work. Topics include practical applications of GenAI for researchers at every stage of the research process. Topics also include risk awareness and ethical dilemmas, publisher and institutional policies, academic integrity considerations, linguistic traces of AI-generated text, and emerging trends in research integrity. The training addresses the cross-impact on education and student learning, including how changes in teaching and assessment affect the pipeline of future researchers.
This training does not cover technical model architecture, programming implementation of AI systems, mathematical foundations of machine learning, or non-research applications of generative AI outside the academic context, such as commercial content generation or entertainment.
This program is intended for several groups of people. Graduate students at the master’s and doctoral levels who are engaged in thesis, dissertation, or manuscript writing need to understand both the opportunities and the boundaries of using AI tools in their research training.
Early-career and established researchers across all academic disciplines should take this training, particularly those in the applied sciences, health sciences, economic and social sciences, and arts and humanities. These researchers are likely to encounter GenAI tools in their daily work or are developing policies for their research groups.
Research integrity officers, academic librarians, and journal editorial staff are responsible for enforcing or advising on policies related to AI use in scholarly publishing. This training will help them understand the current landscape.
University educators seeking to understand how students use AI to affect teaching methods, assessment integrity, and learning outcomes will benefit from this training. Educators need to adapt their pedagogical approaches accordingly.
Learners who successfully complete the program will receive a dated, traceable certificate that provides verifiable proof of their achievement for professional records.
We provide training programs designed to help you meet quality and compliance standards. Our courses cover GMP, GLP, GCP, GEP, GDP, and Quality Assurance.
