Teaching & Learning
- The catalogs do not define a university policy on student use of AI tools for coursework or graded assignments
- They only state that academic integrity policies apply to online coursework and that individual course syllabi govern assignment submission details
not defined
- The catalogs do not define any policy that specifically addresses AI use during exams, quizzes, tests, or other assessments
not defined
- The catalogs include AI as a subject of study in computer science coursework, but they do not define any policy on using AI tools to generate or assist with code for programming assignments
CSCI-450 Artificial Intelligence
This course is a survey of various topics in the field of Artificial Intelligence. This survey covers the
basics of the start of the field of AI from the early thought experiments of Turing to modern AI
techniques of Genetic programming and heuristic searching. This survey will cover both conceptual
and implementation details relevant to the field of AI. Prerequisite: Grade of C or better in CSCI-330 or WPI.
Research
- The catalogs describe research and dissertation writing requirements, but they do not define any policy on using AI to draft, edit, format, or improve research papers, theses, dissertations, or manuscripts
RSCH 760 Dissertation I - The focus of this course is to further develop the student’s dissertation proposal
of the first three chapters of the dissertation and completion of the IRB application. Students will complete
the abstract, introduction, literature review, and methods of their dissertation. It is the goal of this course for
students to receive full approval for the prospectus from their dissertation committee. 3 hours.
RSCH 761 Dissertation II - The focus of this course is continuation and completion of the dissertation.
Students will complete dissertation chapters four and five by writing a complete and correct technical
document.
- The catalogs discuss research methods, data analysis, and dissertation data collection as part of academic programs, but they do not define any policy on the use of AI for research data collection, analysis, statistical processing, synthetic data generation, or interpretation of results
NSCI-262 Research Methods II
Students continue to develop their research proposal started in NSCI-261. Course outcomes
include demonstrated knowledge of experimental design, data analysis, and research ethics. Skills
emphasized include reviewing literature and scientific writing. Lecture 1 hour per week.
RSCH 761 Dissertation II - The focus of this course is continuation and completion of the dissertation.
Students will complete dissertation chapters four and five by writing a complete and correct technical
document. This course will emphasis the collection and analysis of research data and the interpretation and
writing of findings.
- The catalogs refer students to general academic integrity policies and mention research ethics and IRB-related requirements in research training, but they do not define any AI-specific research ethics or integrity rules for grant proposals, IRB applications, or related research compliance documents
The student is expected to commit to the highest level of academic integrity when involved in and fulfilling
requirements for all online courses. Academic dishonesty on any level and in any form will not be
tolerated.
NSCI-262 Research Methods II
Students continue to develop their research proposal started in NSCI-261. Course outcomes
include demonstrated knowledge of experimental design, data analysis, and research ethics.
questions, and hypotheses. Students will also develop dissertation project procedures for the protection of
rights of human subjects.
RSCH 760 Dissertation I - The focus of this course is to further develop the student’s dissertation proposal
of the first three chapters of the dissertation and completion of the IRB application.
Academic Integrity
- The catalogs do not define any disclosure, citation, or attribution requirements for AI use in submitted work
not defined
- However, the catalogs do not define any AI-specific detection tools or enforcement procedures related to AI-generated work
- The university states in both catalogs that academic dishonesty in online courses is not tolerated and may lead to an F, dismissal, or expulsion
The student is expected to commit to the highest level of academic integrity when involved in and fulfilling
requirements for all online courses. Academic dishonesty on any level and in any form will not be
tolerated. This applies not only to active involvement but also to passive knowledge. Any student involved
in academic dishonesty may be assigned a grade of “F” for the course. Furthermore, academic dishonesty
may result in the dismissal or expulsion of the student from the program and/or the University.
The student is expected to commit to the highest level of academic integrity when involved in and fulfilling
requirements for all online courses. Academic dishonesty on any level and in any form will not be
tolerated. This applies not only to active involvement but also to passive knowledge. Any student involved
in academic dishonesty may be assigned a grade of “F” for the course. Furthermore, academic
dishonesty may result in the dismissal or expulsion of the student from the program and/or the University.
Institutional & Administrative
- The catalogs mention FERPA by referring readers to the Student Handbook, but they do not define any AI-related data protection rules, approved AI platforms, or restrictions on what university information may be entered into AI tools
Family Education Rights and Privacy Act (FERPA)
See Student Handbook.
Family Education Rights and Privacy Act (FERPA)
See Student Handbook.
DocuMark: Responsible AI Use for Academic Integrity
Knowing your institution's AI policy is step one. DocuMark helps enforce it fairly by empowering universities to manage AI-generated content, prevent cheating, and support student writing through responsible AI use.
Common Questions About Alderson Broaddus University's AI Policies
Alderson Broaddus University has defined AI policies in 9 of 12 categories, with an overall coverage score of 75%.
The catalogs do not define any disclosure, citation, or attribution requirements for AI use in submitted work.
The university states in both catalogs that academic dishonesty in online courses is not tolerated and may lead to an F, dismissal, or expulsion. However, the catalogs do not define any AI-specific detection tools or enforcement procedures related to AI-generated work.
The catalogs mention FERPA by referring readers to the Student Handbook, but they do not define any AI-related data protection rules, approved AI platforms, or restrictions on what university information may be entered into AI tools.