AI is changing how quickly an idea can become a working piece of software.
For higher education institutions, that presents an interesting opportunity. A member of an institutional effectiveness team can describe a workflow, ask an AI coding tool to build it, make changes through conversation, and have something functional to demonstrate in a matter of hours or days.
For teams working with limited time and resources, that can be incredibly useful.
But it can also create a difficult question:
When does a useful prototype become an institutional system that needs to be governed, maintained and trusted?
That distinction matters, particularly when software is being considered for institutional effectiveness, continuous quality improvement (CQI), assessment, or accreditation.
“Vibe coding” generally describes using conversational AI tools to generate and modify software through natural-language prompts, often with the user writing little or none of the underlying code.
The appeal is easy to understand.
AI can help turn an idea into something tangible quickly. An institutional researcher could experiment with a dashboard. An assessment team could prototype an evidence-tracking workflow. A department could test whether a particular process could be automated before asking IT to invest significant development time.
That ability to experiment has real value.
AI-assisted development can reduce the cost of discovering that an idea isn't worth building. It can also help people who understand an institutional problem communicate that problem more clearly to technical teams.
For low-risk experimentation, that can be a significant advantage.
The important distinction is between AI-assisted software engineering and unmanaged vibe coding.
AI can generate code, tests, documentation and configuration. But an institution still needs people to define requirements, validate data, review the code, test the result, manage security and approve what ultimately reaches users.
The moment a prototype becomes a production application, those responsibilities become much harder to ignore.
Writing the first version of an application may be the easiest part.
A production system needs much more.
Someone needs to determine exactly what the application is supposed to do. Data needs to be sourced, mapped and validated. Users need appropriate access. Security and privacy requirements need to be addressed. The application needs to be tested against expected and unexpected situations.
It also needs documentation, monitoring, backups, accessibility testing, support and a plan for what happens when the original creator moves on.
For institutional effectiveness, there is another layer: institutional meaning.
An AI tool can build a retention dashboard. It cannot decide what your institution's approved definition of retention should be.
That definition might depend on the cohort, census date, stop-out treatment, reporting requirements and whether the number is intended for internal planning, an accreditor, a governing board or an external reporting agency.
A technically correct calculation can still be institutionally wrong.
This is where the apparent simplicity of vibe coding can become misleading.
The cost of AI-assisted coding itself may be relatively small compared with the cost of everything that follows.
Using a planning model for a small institutional application serving approximately 100–250 internal users, the research behind this article estimates a five-year cost of approximately $279,000 to $597,000, with a midpoint of about $438,000.
The initial AI-assisted development represents only a small portion of that total.
At the midpoint, approximately 6% of the five-year cost is associated with the initial AI-generated code. Around 23% goes toward production hardening, while roughly 72% comes from four years of ongoing sustainment.
That aligns with a long-standing software engineering reality: maintenance and sustainment can account for a substantial majority of a system's lifecycle cost.
The lesson isn't that AI makes software expensive. It is that getting software to work and keeping institutional software working are two very different jobs.
The cost model is illustrative rather than a universal price tag. Actual costs will vary according to institutional labor rates, infrastructure, scope, integrations, users and support requirements. But it provides a useful way to think beyond the initial development estimate.
Institutional effectiveness depends on more than having information available.
Institutions need to be able to understand where evidence came from, how it was analyzed, what action followed and whether that action resulted in improvement.
That makes traceability and trust particularly important.
An AI-assisted application could be very useful for collecting assessment plans, organizing evidence, tracking improvement actions, managing program reviews or providing read-only views of institutional data. It could help create a clearer cycle:
Goal → Measure → Evidence → Analysis → Action → Outcome → Improvement
Used this way, AI can reduce friction and help teams spend less time managing administrative processes.
The risk comes when an unmanaged application becomes the source of institutional truth.
What happens if the metric definition is buried inside generated code? What happens if the underlying data changes? Can the institution reproduce last year's result? Is there an audit trail? Who approves changes? What happens when the person who built the application leaves?
A dashboard that produces the wrong result more quickly does not improve institutional effectiveness.
For accreditation in particular, evidence needs to be credible, explainable and defensible. The technology supporting that evidence should strengthen those qualities, not introduce another layer of uncertainty.
The answer probably isn't to ban it.
AI-assisted development has too much potential to dismiss. Used appropriately, it can help institutions explore ideas, prototype solutions, automate low-risk tasks and accelerate software development.
The better question is where should the boundaries be?
A useful approach is to treat AI-generated software differently depending on its consequences.
A prototype using synthetic data in a sandbox is very different from an application containing institutional records. A read-only dashboard is different from a system that writes back to a student information system. And a temporary workflow experiment is very different from software that becomes part of an institution's accreditation process.
For anything moving into production, institutions should establish clear requirements around ownership, data definitions, source control, testing, security, privacy, accessibility, documentation, backups and ongoing support.
Most importantly, someone needs to remain accountable for the system after the excitement of building it has passed.
Because an application without institutional ownership isn't free software.
It's an unfunded obligation.
Higher education doesn't need to choose between innovation and responsible technology.
There is a middle ground: encourage experimentation, provide safe environments for AI-assisted development, and apply stronger controls as software becomes more consequential.
For Institutional Effectiveness, CQI and accreditation, the goal should be to use AI where it can make institutional work easier without allowing it to obscure the things that make that work trustworthy.
This is also where the value of a purpose-built institutional platform becomes clear.
When planning, assessment, evidence, improvement actions and accreditation activities are connected within a governed system, institutions aren't starting from scratch every time they need to demonstrate progress. The technology supports the process rather than creating another layer of systems, spreadsheets and disconnected data to maintain.
AI can certainly play a role in that environment. It can help teams work faster, surface information and reduce administrative effort. But the underlying definitions, evidence and institutional record still need to be controlled by the institution.
The question isn't simply whether AI can build the application.
It can.
The more important questions are whether your institution can understand it, validate it, support it, reproduce its results and trust it five years from now.
And if you're still not sure?
Don't take our word for it. Ask AI.
Whether your institution is focused on Planning, Budgeting, Assessment, Credentialing, Accreditation, or a combination of several areas, SPOL can help bring greater structure and visibility to the work already underway.