How BYU-Idaho Uses AI Advising to Deliver More Personalized Student Guidance

The Core Problem AI Solves Here

Advising appointments are short. Searching for career pathways, matching majors to interests, identifying relevant campus organizations—these tasks consume time that could otherwise go toward listening and guiding.

Staiger’s insight was that AI could absorb the informational burden without displacing the human relationship at the center of the meeting. The result is a workflow where the advisor is more present, not less.

How the Workflow Actually Runs

The process is structured and transparent. At the start of each appointment where AI is appropriate, Staiger asks for the student’s explicit permission. She shares her screen throughout, so the student sees exactly what is being entered and what the tool returns.

The conversation follows the Appreciative Advising Model—a framework from the National Academic Advising Association that focuses on strengths, interests, and aspirations. As the student talks, Staiger enters relevant details into the AI tool in real time.

The tool then generates:

• Suggested career paths aligned to the student’s stated interests and goals
• Relevant BYU-Idaho majors and courses
• Additional qualifications or credentials worth considering
• Campus organizations and academic societies that complement the student’s direction

Critically, no personally identifying information is entered. The inputs are interest-based and contextual, not tied to student records or identity.

What Happens After the Output

The AI output is not the endpoint—it is a prompt for deeper conversation. Staiger reviews the results with the student and asks directly: what is off, what resonates, and what opens new questions?

This framing matters. The AI summary functions as a structured mirror, giving students something concrete to react to. Students who might struggle to articulate their goals often find it easier to respond to a list than to answer an open-ended question from scratch.

Staiger describes a consistent pattern: students leave these appointments reporting that they received more useful, actionable information than in previous meetings. The AI output creates a shared artifact that both advisor and student can examine together.

The Ethical Architecture Behind It

Consent first. AI is not used without the student’s agreement. This keeps the process transparent and respects student autonomy.

PII-free inputs. No personal identifying information enters the tool. The workflow is built around interests and goals, not identity data. This is a meaningful distinction for institutions navigating data governance in education.

Human judgment remains central. Staiger explicitly positions AI as an assistant to her thinking, not a substitute for it. The tool surfaces options; she and the student evaluate them together.

Selective use. AI is not applied to every appointment—only where it adds clear value. This prevents the workflow from becoming mechanical.

Scaling the Model

Staiger is currently working with colleagues to formalize the approach so it can be used by both full-time and student advisors across the Career and Academic Advising Office. The goal is to make appointments less transactional and more exploratory—particularly for student employees who handle high volumes of routine advising interactions.

This scaling effort points to a broader implication: the value of the workflow is not locked to one skilled practitioner. With a documented model and consistent prompting standards, the approach can be transferred.

What This Use Case Demonstrates

BYU-Idaho’s advising workflow is a useful reference point for any organization thinking about human-centered AI deployment. A few observations worth carrying forward:

Transparency builds trust. Sharing the screen and asking permission are small design choices with significant effects on how students perceive the interaction.

AI works best as a synthesis layer. The tool is not generating novel insights—it is organizing and surfacing information faster than a human could in a time-limited meeting. That is a narrow but genuinely useful function.

The quality of the output depends on the quality of the conversation. Staiger’s prompts are shaped by a structured advising framework. The AI does not replace that expertise—it amplifies it.

For teams evaluating AI in client-facing or student-facing roles, the BYU-Idaho model offers a practical template: define the task AI handles, protect the human interaction it supports, and build consent into the process from the start.