College of Charleston AI Policy

Cheating: the actual giving or receiving of unauthorized, dishonest assistance that might give one student an unfair advantage over another in the performance of any assigned, graded academic work, inside or outside of the classroom, and by any means whatsoever, including but not limited to fraud, duress, deception, theft, talking, making signs, gestures, copying, electronic messaging, photography, unauthorized reuse of previously graded work, unauthorized dual submission, unauthorized collaboration and unauthorized use or possession of study aids, memoranda, books, data, or other information, including the use of generative artificial intelligence (GAI) tools when not authorized by the instructor(s).

(d) Allowing any other person or organization (including Generative AI) to prepare work which one then submits as their own, unless explicitly authorized by the instructor(s).

Cheating: the actual giving or receiving of unauthorized, dishonest assistance that might give one student an unfair advantage over another in the performance of any assigned, graded academic work, inside or outside of the classroom, and by any means whatsoever, including but not limited to fraud, duress, deception, theft, talking, making signs, gestures, copying, electronic messaging, photography, unauthorized reuse of previously graded work, unauthorized dual submission, unauthorized collaboration and unauthorized use or possession of study aids, memoranda, books, data, or other information, including the use of generative artificial intelligence (GAI) tools when not authorized by the instructor(s).

The committee developed assessment and training in AI literacy and ethics to the First-Year Experience Seminar, ensuring that all CofC students can quickly gain a basic understanding of AI’s uses and limitations.

To address AI literacy, students will build a stronger understanding of how AI and large language models work, practice writing effective prompts and learn to recognize important limitations and risks such as bias, hallucinations and overreliance on AI-generated responses.

To address AI ethics, students will explore what responsible AI use looks like in practice, including the importance of transparency, accuracy, accountability, context and the protection of personal information when using AI tools.

(d) Allowing any other person or organization (including Generative AI) to prepare work which one then submits as their own, unless explicitly authorized by the instructor(s).

• Storing Institutional Data: Storing institutional, confidential, or restricted data on unauthorized/unsupported 3rd party storage solutions (also see Data Classifications Policy).

Data users are CofC employees who have been granted authorization by the data managers to access institutional data. Authorization is granted for a specific level of access, as defined by the data management policies, solely for the conduct of institutional business.

Responsibilities include:

Following the policies and procedures established by the data stewards for the responsible use of the College data.

Using institutional data only as required to conduct College business.

Ensuring the privacy of data by viewing and storing data, and the information derived from data, under secure conditions.

3. The University’s proprietary information including but not limited to intellectual research findings, intellectual property, financial data, and donor and funding sources.

(a) The verbatim repetition, without acknowledgement, of the writings of another author. All significant phrases, clauses, or passages, taken directly from source material must be enclosed in quotation marks and acknowledged in the text itself and/or in footnotes/endnotes.

(b) Borrowing without acknowledging the source.

(c) Paraphrasing the thoughts of another writer without acknowledgement.

(d) Allowing any other person or organization (including Generative AI) to prepare work which one then submits as their own, unless explicitly authorized by the instructor(s).

including the use of generative artificial intelligence (GAI) tools when not authorized by the instructor(s).

(d) Allowing any other person or organization (including Generative AI) to prepare work which one then submits as their own, unless explicitly authorized by the instructor(s).

(a) The grade of XXF means failure due to academic dishonesty. If a student is found responsible for an act of “serious” academic dishonesty, the Registrar’s Office will insert the XXF grade for that course after notice from the Dean of Students. The XXF remains on the student’s official transcript for a minimum of 2 years. After 2 years, the student can petition the Honor Board for removal of the XX. The F grade will remain on the transcript.

Class 1 – act involves significant premeditation; conspiracy and/or intent to deceive, e.g., purchasing a research paper. Penalties: XXF and either suspension or expulsion assigned if student found responsible by Honor Board.

Class 2 – act involves deliberate failure to comply with assignment directions, some conspiracy and/or intent to deceive, e.g., use of the Internet when prohibited, some fabricated endnotes or data, copying several answers from another student’s test. Penalties: XXF and other sanctions assigned if student found responsible by Honor Board.

Class 3 – act mostly due to ignorance, confusion and/or poor communication between instructor and class, e.g., unintentional violation of the class rules on collaboration or the rules of citation. Penalties: The instructor sets the penalty and discusses it with the student.

Over the past year, the College has made some exciting progress with its quality enhancement plan for AI literacy and ethics in student learning.

The QEP has made exciting progress over the past year, bringing together campus leaders, faculty, staff and students to advance a shared vision for AI literacy and ethics in student learning.

• Storing Institutional Data: Storing institutional, confidential, or restricted data on unauthorized/unsupported 3rd party storage solutions (also see Data Classifications Policy).

Institutional data that have no access restrictions are available to the general public. These data will be designated as unrestricted or public data.

Internal Use Data

Information used in the College's daily operations that is not confidential or legally protected but should not be made public and should only be disclosed under limited circumstances. Other users must be granted specific authorization by the data owner or data steward to access the data since the data's unauthorized disclosure, alteration, or destruction may cause perceivable damage to the institution.

Confidential data are data that, if disclosed to unauthorized individuals, could significantly harm the organization, its stakeholders, or individuals. This data type requires stringent protection measures to ensure its confidentiality, integrity, and availability.

Highly sensitive information in use by the College and is protected by statutory penalties if disclosed in an unauthorized manner.

2. Family Educational Rights and Privacy Act of 1974 (FERPA);

a. FERPA protects the rights of students by controlling the creation, maintenance, and access to educational records. It guarantees students' access to their academic records while prohibiting unauthorized access by others.

1) Academic Information: Grades, transcripts, class lists, student course schedules, and academic performance records.

Over the past year, the College has made some exciting progress with its quality enhancement plan for AI literacy and ethics in student learning.

The QEP has made exciting progress over the past year, bringing together campus leaders, faculty, staff and students to advance a shared vision for AI literacy and ethics in student learning.

During the spring semester, the QEP Proposal Development Committee expanded its efforts by forming four subcommittees focused on AI literacy, AI ethics, QEP writing and microcredentials. Together, these groups helped move the initiative from planning into active development.

To address AI ethics, students will explore what responsible AI use looks like in practice, including the importance of transparency, accuracy, accountability, context and the protection of personal information when using AI tools.