AI Statement for Academic Work
This is a living statement, authored and maintained by the Towson University AI Institute. It will evolve as understanding matures and as people respond, and we welcome feedback from across the university.
Artificial intelligence has entered academic work. Some in our community have embraced it, some have deliberately declined it, and by now none of us can quite ignore it. What remains the same is the set of values of our university: intellectual inquiry, the creation and stewardship of knowledge, and service to the public. But AI tests how we live up to those commitments. It can be harder now to know when work is a person's own, whether a cited source is real, or who is answerable when a system takes part in a decision. How much AI helps or harms depends on the choices we make about it, including the choice not to use it. These principles are offered to help the Towson community reason through those choices.
HOW TO READ THESE PRINCIPLES
They are meant to orient, not to mandate. They apply the university's long-standing values to this new technology. The principles do not replace disciplinary judgment or existing university policy. They do not change the academic-integrity code: it continues to govern, including where AI-generated work is presented as a person's own. Nothing here is an enforcement instrument. These principles should not be cited on their own in evaluation, integrity, or personnel proceedings. Departments and programs are encouraged to develop more specific guidance consistent with them, as some already have.
The principles grow from Towson's mission of intellectual inquiry, critical thinking, and preparing graduates to serve as effective leaders for the public good.
Our Principles
- Professional judgment and academic freedom
The freedom to choose one's methods carries the responsibilities it always has: honesty, accountability, and care for others. - Opportunity and capability
We intend for Towson to be among the institutions putting AI to ambitious and thoughtful use, guided by our educational mission and by the world our graduates will enter. - Rigor and critical inquiry
We treat what AI produces as a claim to check rather than an answer to trust, and we choose uses that deepen inquiry and build judgment rather than replace the effort by which expertise is formed. - Human accountability for the public good
Responsibility must track control. No one should answer for an outcome they had no real power to shape, and no one should hand a decision of consequence to a system that can answer for nothing. - Integrity and honest attribution
Responsibility cannot be handed off to a tool; credit belongs to the judgement we bring to what it produces. - Transparency and trust
We disclose AI's role, and we prefer a culture of openness and trust to one that treats people as suspects. - Access, equity, and inclusion
We choose uses that broaden opportunity, we work to keep student access to the tools a course requires, and we guard against the biases AI can introduce. - Responsible stewardship
We protect people's data, keep human judgment in decisions that affect people, weigh what AI use means for the people who do the work, and treat its material and human costs as our concern rather than someone else's problem.
The Principles Across Our Mission
In teaching.
Our aim is to develop human judgment, not to produce finished work. Instructors decide whether and how AI figures in their courses, according to the standards of their discipline and learning outcomes; prohibiting it is as legitimate a choice as requiring it. Students are entitled to know clearly what each course expects. Where AI use is required, it should be integral to the learning rather than incidental. Requiring a tool also means ensuring it is provided, or its cost made known before students enroll; where a student still cannot access it, the outcome is met through support rather than a lower bar. Instructors should not ask students to put their own or others' personal work into tools that do not protect it. Where it is relevant to a course, AI literacy is worth teaching, including a clear-eyed sense of what these tools can and cannot do, alongside the forms of practice and productive struggle a field depends on.
In research.
AI is a legitimate research instrument, and using it well is part of good scholarship. As with any instrument, human authors remain accountable for the validity and integrity of their work. AI is not an author; authorship carries responsibilities only a person can discharge. We treat AI-assisted results as findings to check and characterize, not to trust, and we check what AI gives us before it enters the record: a reference we have not verified is not a citation. For human subjects research, we disclose proposed uses of AI in research to the Institutional Review Board. We disclose material AI use as the norms of our field, venue, and funder require, and we document our methods, so others can follow and, where the tools allow, reproduce them. We use AI to accelerate the work while keeping the interpretive judgment at the heart of scholarship. And we honor the terms under which data and others' unpublished work are entrusted to us: confidential manuscripts, proposals under review, and protected data do not go into tools that would expose them.
In service and administration.
Where we use AI in service and administration, we use it to reduce burden, not to offload responsibility. We do not enter confidential personnel or student data into tools not approved for it, and we follow the university's data-protection requirements. In decisions that materially affect a person's education, employment, or standing, including admission, hiring, the promotion and tenure process, and aid, a responsible person makes the decision and can be held accountable for it. If AI tools are used to assist such decisions, its effect is examined for bias before deployment and reviewed again as the tool or the use changes. We are transparent, in a manner appropriate to the context, when AI materially shapes an official decision or communication. And much of this work is carried out by staff, whose jobs these tools can change; their perspective belongs in the decision to adopt them.
In professional preparation.
Some programs prepare students for professions (for example, teaching, nursing, social work) that carry duties of care toward the people they will serve, including minors, and standards set by external accreditors and licensure boards. In these programs, using AI well includes learning the legal and ethical obligations of the field, protecting the data of the people students serve, and modeling the practice students will carry into it. Students on placement are also bound by the rules of the site that hosts them, which may differ from Towson's; programs should prepare students to ask what applies rather than assume.