Guidance on the Use of Generative Artificial Intelligence to Support Program-level Assessment Activities

March 3, 2025

Generative artificial intelligence (AI) technology is evolving rapidly and advances in AI are providing new and powerful tools to enhance our work. The following guidance is intended to help WSU programs and faculty navigate these new opportunities in the context of program-level learning outcomes assessment activities, ensuring we use AI responsibly. Note: Teaching with AI and policies concerning the acceptable use of AI by students in courses is beyond the scope of this guidance. See the additional resources at the end of this guidance for more information.

What is Generative AI?

Generative AI produces new data, including text, images, and videos, based on user prompts and existing data within a defined training set (such as internet accessible information). Unlike traditional AI, which might classify data or make predictions, generative AI models use patterns learned from large datasets to produce original outputs that resemble the training data.

This technology leverages complex algorithms and neural networks to generate content that can be surprisingly creative and human-like. However, it still lacks genuine creativity and understanding. While AI can enhance productivity and assist with various tasks, it cannot replicate the full depth of human reasoning, judgement, or creativity.

Guiding Principles for Use of AI in Program-level Assessment

WSU’s Office of the Provost supports the use of AI in work processes that can help faculty and staff be more efficient and effective. However, individuals must use AI responsibly with constant attention to the security of private and protected data. Responsible use includes making sure that users understand the applications and limitations of AI while also ensuring that any use of AI is done ethically and is consistent with university policies and regulations.

1. Human involvement is required:

All work should emphasize regular human involvement, with humans actively making critical decisions about how and when to use AI. AI can empower and augment the work of professionals by streamlining repetitive tasks, generating insights, and assisting with data analysis, but it cannot replace human knowledge, judgement, experience, emotion, and imagination.

Additionally, users play a critical role in engineering prompts. Think carefully and strategically about the output you are looking for when writing your prompts. You may need to make several attempts to get the desired output and the more you expect of an AI tool, the more time you need to take to ensure it delivers your expected output.

2. Accuracy and accountability:

AI tools are not perfect. They have been known to “hallucinate,” generating false information that appears plausible but has no basis in actual data or reality. As WSU employees, we are accountable for the work we produce, even when it is developed with the assistance of AI tools. All AI-generated material must be thoroughly reviewed, edited, and approved before it is used.

Increasingly, AI functionality is being built into commonplace applications and services, such as Zoom and Adobe Creative Cloud. Care should be taken to review and independently verify content generated by these applications as well, even if they are not considered stand-alone AI tools.

3. Beware of bias:

Users of generative AI must be vigilant for bias in responses because these systems learn from vast datasets that can contain existing prejudices and stereotypes, leading the AI to perpetuate or even amplify these biases in its outputs. Ensuring fairness and accuracy requires careful oversight and continuous evaluation of the generated content.

4. Data security and privacy:

WSU faculty and staff are urged to follow all applicable laws, regulations, and WSU policies when entering various forms of data into AI tools, including the university-provided resource Microsoft Copilot for Web.

Generally speaking, WSU employees should not enter WSU internal, confidential, or regulated data (i.e., non-public data) into AI tools (see WSU’s Executive Policy #8 for more information about data classifications). However, with Microsoft Copilot for Web (with WSU Login), WSU faculty and staff are permitted to use some non-public data types, including internal and student education records (see the WSU Cloud Acceptable Use Matrix for more information).

Additionally, individual WSU employees and units are not authorized to enter into service agreements or otherwise use AI technology with non-public WSU data, absent an ITS Security Assessment and prior written authorization from the WSU System Privacy Officer.

Examples of Potential Uses of AI to Support Program Assessment Activities

AI tools have the potential to support a variety of program assessment activities throughout the assessment cycle. Note: This list provides a range of examples, but is not intended to be fully exhaustive.

Program-level Student Learning Outcomes (SLOs):

AI could brainstorm, offer suggestions, and help programs take stock of what is happening in their discipline or field to facilitate faculty discussions around program-level SLOs. Additionally, AI could help programs align program-level SLOs with industry standards or employment expectations. AI could also provide constructive feedback and help faculty refine language so that SLOs are easily understood by students, faculty, staff, etc.

Curriculum Maps:

AI could help with curriculum mapping by using syllabi and/or key assignment prompts to align courses and program-level SLOs into a draft map for faculty to review, discuss, and refine.

Assessment Plans:

AI could help draft an assessment plan by creating an outline to organize content into a cohesive structure. AI could also help provide editing assistance, offering suggestions for rewording or reorganizing.

Assessment Measures:

AI could help programs and faculty draft assessment tools (such as rubrics and survey questions) for faculty to review, discuss, and revise. AI could also help draft emails with instructions or requests for participation. For text that is already written, AI tools could offer suggestions for edits or rewording.

Programs and faculty could also consider using AI to score student work with a program rubric to serve as a second rater alongside faculty and help confirm rater reliability.

Assessment Data Analysis:

AI could help programs analyze quantitative assessment data (e.g., rubrics scores) and create visualizations (e.g., charts, tables, etc.). AI could also help summarize qualitative data (e.g., open comment responses from an exit survey).

AI could even help programs draft summary reports, presentations, and memos to facilitate faculty review and discussion of assessment data. For text that is already written, AI tools could offer suggestions for edits or rewording. AI could also help tailor content for different audiences, such as students, faculty, or the public.

Use of Assessment to Inform Decision-making:

Faculty could use AI to help draft or revise assignment prompts, syllabi, and other course materials to support student learning. AI could also brainstorm and offer suggestions around potential changes or interventions to help facilitate faculty discussions and action planning. AI could even suggest potential questions or objections from stakeholders, allowing for better preparation in advance of discussions.

Additionally, using Zoom’s AI Companion to create meeting summaries, capture highlights, and identify action items with next steps could help programs document conversations around assessment data/findings, decision-making, and action planning.

ACE Support for Program Assessment Efforts

As always, ACE staff are available to answer questions and consult with programs on their assessment efforts. We’d be happy to partner with undergraduate programs interested in exploring the use of AI to support program assessment activities. For information about who to contact, see our ACE Staff page.