Teaching & Learning
- However, the reviewed sources do not confirm a university-wide explicit rule governing AI use across all coursework and graded assignments
- Bucknell’s generative AI guidelines identify academic integrity as an important consideration when using AI tools, and the university also maintains student-facing academic responsibility and AI resource pages
There are important considerations to keep in mind when using these tools, including information security and data privacy, compliance, copyright, and academic integrity.
- No explicit university-wide AI policy for examinations and assessments was confirmed from the reviewed sources
- Bucknell’s academic responsibility resources and student FAQ pages indicate that general academic integrity expectations apply to assessed work, but examination-specific AI rules were not established in the extracted evidence
Bucknell academic responsibility resources including the student FAQ and tips pages exist but were not fully reviewed for examination-specific AI policy content.
- However, the reviewed sources do not confirm a university-wide policy expressly permitting or restricting specific forms of AI-based study help
- Bucknell maintains a dedicated library AI research guide and related resources, indicating institutional curation of AI resources for learning and study assistance
A dedicated Bucknell library research guide on AI exists at https://researchbysubject.bucknell.edu/AIatBucknell with associated resources at https://researchbysubject.bucknell.edu/AIatBucknell/resources and https://researchbysubject.bucknell.edu/c.php?g=1415716&p=10490900, indicating institutional curation of AI learning resources, but specific policy language from these pages was not captured in the extraction.
Research
- However, the reviewed sources do not confirm a university-wide policy specifically governing AI use in manuscript drafting, authorship, or submission
- Bucknell’s generative AI guidelines identify copyright and academic integrity as important considerations when using AI tools, which are directly relevant to research writing and manuscript preparation
There are important considerations to keep in mind when using these tools, including information security and data privacy, compliance, copyright, and academic integrity.
- Bucknell provides guidance supporting AI-assisted qualitative analysis in research
- The guidance presents AI as a tool that can be used to code, categorize, summarize, and analyze textual data, but it requires human review and manual validation of AI-generated themes and coding for reliability and accuracy
This guide is designed to assist beginners and seasoned professionals in conducting qualitative research using AI tools. It provides step-by-step instructions, best practices, and examples to analyze textual information efficiently.
Artificial intelligence (AI) provides researchers with innovative solutions to tackle the challenges of qualitative research. By automating repetitive tasks like coding, categorization, and sentiment analysis, AI reduces the time and effort needed to analyze large datasets.
However, researchers still play a central role in this process by examining the accuracy of the results since LLMs could still make mistakes and introduce bias.
A manual validation step is crucial to ensure the reliability and accuracy of AI-generated themes and coding. We strongly advise pairing AI-generated insights with human judgment to ensure that any assigned themes accurately capture the underlying data.
- The research guidance that is provided focuses on qualitative analysis workflows rather than research-ethics submission rules
- The provided sources do not define a specific Bucknell policy on AI use in grant proposals, IRB applications, ethics declarations, or other formal research-integrity filings
not defined
Academic Integrity
- Bucknell’s reviewed sources do not confirm a university-wide requirement for students or researchers to disclose or cite AI use in academic work
- The generative AI guidelines reference academic integrity as an important consideration, and the Division of Marketing & Communications does impose a disclosure requirement for AI-powered interactive tools by requiring them to be clearly identified as AI-powered
When AI is used in interactive applications, such as chatbots or automated agents, these tools will be clearly identified as AI-powered.
There are important considerations to keep in mind when using these tools, including information security and data privacy, compliance, copyright, and academic integrity.
- For admissions materials, Bucknell requires originality and states that false or fraudulent submissions can lead to significant sanctions
- Bucknell’s provided sources describe general academic-misconduct enforcement and penalties, but they do not define an AI-specific detection policy or stance on AI-detection tools
Find answers to students' most-asked questions about academic responsibility.
Learn what will happen when a faculty member files an allegation of academic misconduct against a student, including possible penalties and how they affect a student's academic record.
Each applicant is required to certify that any and all information furnished to the University is accurate and complete. In addition, any material submitted (including but not limited to the application itself, essays and supplemental materials) must be the applicant’s original work. Any applicant for admission or financial aid who knowingly submits false or fraudulent information, conceals material information, or intentionally misleads or misinforms the University, may be subject to actions including (but not limited to) denial of admission, revocation of an award of financial aid and repayment of dispersed funds, dismissal from the University, revocation of admission or revocation of a conferred degree.
Institutional & Administrative
- The provided sources do not define a broader university-wide faculty policy for grading, lesson planning, or recommendation letters
- Bucknell permits AI use by at least some professional staff for institutional content creation, but requires professional oversight, human accountability, and review of all AI-assisted output before publication
The Division of Marketing & Communications creates content through human expertise, creativity and judgment, guided by our ethical storytelling framework. Professional staff drive all strategies and creative decisions. The division will use artificial intelligence as a tool to aid and improve our work, but always under professional oversight and with human accountability for all final work.
When AI tools assist in any aspect of content development, creative professionals will thoroughly review, edit and validate all output before publication. Our staff remains fully accountable for the quality, accuracy and appropriateness of all work, regardless of whether AI tools were used in the process.
- The sources do not identify a specifically approved generative AI platform
- Bucknell prohibits entering institutional data and FERPA-protected information into generative AI tools and states that no generative AI tool meets university standards for handling anything other than publicly available information
- The university’s data-classification policy distinguishes public, sensitive, and confidential data, requires sensitive cloud data to be stored only on university-managed or university-contracted systems, and limits confidential-data disclosure on a strict need-to-know basis
Protect Confidential Data: No Generative AI tool meets the University’s security, privacy, and compliance standards for handling anything besides publicly available information in accordance with the University's Data Classification Policy and Appropriate Use Policy. You should never enter any institutional data or information protected by the Family Education Rights and Privacy Act (FERPA) into a Generative AI tool or service in accordance with the University’s Student Records Policy.
Entering data into a Generative AI tool or service is like posting that data on a public website.
Data that is created, processed, collected, or maintained by the University will be classified into the following three categories:
1. Public – Public data is institutional information that may or must be freely available to the general public. Such information has no local, national, international, or contractual restrictions on access or usage.
2. Sensitive – Sensitive data is institutional information that must be guarded due to proprietary, ethical, privacy, or business process considerations. Sensitive data must be protected from unauthorized access, modification, transmission, storage, or release.
3. Confidential – Confidential data is institutional information protected by law, government regulations, statutes, industry regulations, contractual obligations, or specific university policies.
- If stored in the cloud, stored only on cloud-based information systems managed or contracted by the University.
Confidential data is only to be disclosed to individuals and business partners within the university on a strict need to know basis.
- Bucknell has institution-level guidance for the appropriate use of generative AI and division-level principles for ethical AI use in Marketing & Communications
- The university frames AI use around information security, data privacy, compliance, copyright, academic integrity, professional oversight, accountability, source verification, and transparency, but the provided sources do not describe a university-wide AI committee, task force, or formal adoption roadmap
These guidelines set forth guidance for the appropriate use of generative AI tools at Bucknell.
There are important considerations to keep in mind when using these tools, including information security and data privacy, compliance, copyright, and academic integrity.
The Division of Marketing & Communications creates content through human expertise, creativity and judgment, guided by our ethical storytelling framework. Professional staff drive all strategies and creative decisions. The division will use artificial intelligence as a tool to aid and improve our work, but always under professional oversight and with human accountability for all final work.
The division will utilize source review in line with our ethical storytelling principles to ensure the accuracy of all content prior to publication. When AI tools are used, their outputs will be treated as drafts requiring verification, not as factual information.
The division will never use AI to knowingly mislead or misrepresent Bucknell or its people.
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 Bucknell University's AI Policies
Bucknell University has defined AI policies in 11 of 12 categories, with an overall coverage score of 92%.
Bucknell’s reviewed sources do not confirm a university-wide requirement for students or researchers to disclose or cite AI use in academic work. The generative AI guidelines reference academic integrity as an important consideration, and the Division of Marketing & Communications does impose a disclosure requirement for AI-powered interactive tools by requiring them to be clearly identified as AI-powered.
Bucknell’s provided sources describe general academic-misconduct enforcement and penalties, but they do not define an AI-specific detection policy or stance on AI-detection tools. For admissions materials, Bucknell requires originality and states that false or fraudulent submissions can lead to significant sanctions.
Bucknell prohibits entering institutional data and FERPA-protected information into generative AI tools and states that no generative AI tool meets university standards for handling anything other than publicly available information. The university’s data-classification policy distinguishes public, sensitive, and confidential data, requires sensitive cloud data to be stored only on university-managed or university-contracted systems, and limits confidential-data disclosure on a strict need-to-know basis. The sources do not identify a specifically approved generative AI platform.