Teaching
AI Toolkit
Faculty are not required to use AI in their teaching, but those interested in exploring it may find value in understanding its capabilities, limitations, and potential applications. AI can support personalized learning, streamline tasks, and enhance classroom engagement while helping students develop awareness of technologies increasingly used in everyday life and the workplace.
This toolkit is designed to support faculty at any stage of AI adoption. It includes overviews of AI tools, sample syllabus statements, assignment integration examples, and practical guidance to help instructors make informed decisions and clearly communicate expectations to students.
Faculty Guidance on AI Detection
Jacksonville State University recognizes that generative AI is reshaping how students learn, write, and engage in academic work. In response, JSU affirms the importance of AI literacy, ethical authorship, academic integrity, and transparent communication, guided by clearly defined course expectations.
Students are expected to follow instructor policies regarding AI use and disclose assistance when required. Unauthorized or misrepresented AI use may constitute a violation of academic integrity standards.
At the same time, JSU has chosen not to adopt AI detection software. Current research indicates that AI detection software is not consistently reliable, particularly when evaluating hybrid writing (human + AI), which is now common in student work. Studies show that these tools may produce false positives, demonstrate bias (especially against non-native English writers), and lack transparency in how results are generated. In addition, reliance on detection tools as sole evidence may introduce procedural and legal risks in academic misconduct cases.
For these reasons, JSU does not recommend using AI detection software as definitive evidence of academic misconduct. If detection tools are used at all, they should be considered preliminary indicators that prompt further review—not as proof.
When questions about authorship arise, faculty should interpret detection results within a broader context that includes:
- the student’s prior work
- drafts and revision history
- citation practices and documentation of sources
- evidence of writing process and development
A formative conversation with the student is the preferred first step in addressing concerns about AI use. These conversations provide an opportunity to clarify expectations, support student learning, and discuss ethical engagement with AI tools. In cases where a student’s use of AI violates course policies or academic integrity standards, the situation may be addressed through the University’s Scholars’ Code, which provides a formal framework for handling academic dishonesty.
JSU encourages faculty to prioritize pedagogical approaches that make student thinking visible over time, including process-based writing, staged assignments, revision, and reflection. These approaches better support the evaluation of critical thinking, rhetorical awareness, and metacognition, while reducing reliance on uncertain determinations of authorship.
Ultimately, academic integrity decisions must be grounded in human judgment, due process, and multiple forms of evidence. The university does not condone faculty use of any AI tool to detect unauthorized AI in student work. Use of AI tools to detect AI-generated work may violate student rights and expose faculty to legal ramifications.
In alignment with its mission, JSU seeks not only to address potential misuse, but to prepare students to engage with AI responsibly, ethically, and effectively in academic and professional contexts.
Faculty Strategies for Identifying and Addressing AI Student Writing
AI detection software is not the only—or best—way to respond when student writing raises questions. In many cases, the most useful evidence comes from the teaching practices faculty already use: drafts, reflections, conferences, source work, revision history, and knowledge of students’ writing over time.The goal is not simply to catch AI use. The goal is to create learning experiences where students’ thinking, decisions, and development are visible
Design Assignments that Demonstrate Process
Assignments are easier to evaluate when students show how their thinking develops. Consider building in stages such as topic proposals, annotated bibliographies, outlines, drafts, peer review, revision plans, and final reflections. Short in-class writing checkpoints can also help establish a record of students’ own language, reasoning, and progress.
Reflection prompts are especially useful. Faculty might ask students to explain what they revised, why they made certain choices, how they used sources, or whether/how AI tools contributed to the work.
Use Existing Tools to Review / Track Writing Development
Faculty do not need AI detection software to look for evidence of process. Tools already available through Word, Google Docs, Canvas, and Turnitin’s plagiarism features can help faculty review source use, revision history, and consistency across assignments.
For example, version history may show whether a paper developed gradually or appeared in large blocks of pasted text. Prior discussion posts, in-class writing, or informal reflections can also provide helpful points of comparison.
Look for Patterns, Not Single Signs
No single feature proves AI use. However, several concerns together may warrant a follow-up conversation. These might include writing that is unusually polished but generic, citations that do not exist or do not support the claims being made, sudden shifts in tone or vocabulary, surface-level analysis, or work that does not reflect course readings, discussions, or assignment requirements.
Faculty should be especially cautious about drawing conclusions from writing style alone, since students’ language can vary for many reasons.
Talk with the Student First
When concerns arise, the preferred first step is a formative conversation. Faculty might ask the student to explain how they developed the argument, how they selected and used sources, what revisions they made, or how a specific paragraph connects to the assignment. In some cases, asking the student to revise or explain a passage during the conversation can provide useful insight.
The goal of this conversation is not to accuse, but to understand the student’s process and clarify expectations for ethical AI use.
Make AI Expectations Visible
Clear course policies reduce confusion. Faculty are encouraged to state when AI use is allowed, limited, or prohibited, and to explain what kind of disclosure is expected. Some instructors may allow AI for brainstorming or outlining while prohibiting AI-generated final prose. Others may design assignments that require students to document prompts, outputs, and revisions. Faculty should make note that, on the student section of this website, students are being instructed “[they] must be aware that, when using AI tools for assignments, papers, theses, and dissertations, they must both acknowledge the AI tool used and cite the information gathered while using it.” Therefore, faculty should address how they expect students to acknowledge and cite AI use in their classes.
Focus on Learning, Not Catching
The strongest long-term response is assignment design that makes student thinking visible. Process-based assessment, reflective writing, staged submissions, and opportunities for students to explain their choices make it easier to evaluate authentic learning while reducing the need to rely on uncertain authorship judgments.