# Course-Tuned AI for Calculus and Physics at AUS

> Source: https://ibl.ai/case-study/american-university-of-sharjah

## Case Study

How the American University of Sharjah deployed ibl.ai agents in gateway STEM courses — grounded in faculty-approved sources, able to compute and plot, and measured against published accuracy targets before any rollout.

In math-heavy courses a confident wrong answer is worse than no answer. AUS's agents for Calculus I and Physics 101 were built to compute and visualize, not just converse — with inline citations back to faculty-approved sources so students can check the work.

### Quick Stats

- **Gateway STEM** Calculus I and Physics 101
- **≥95%** Weekly graphing-accuracy target
- **≥4.2 / 5** Monthly explanation-quality score
- **≥70%** Adoption target among enrolled students

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## The Deployment

### Prove it in the hardest courses first

AUS chose gateway STEM courses — Calculus I (Math 103) and Physics 101 — deliberately. The work was designed to validate instructional impact, technical fit, and day-to-day faculty and student workflows in the courses where accuracy matters most.

**Course-specific agents** — Each agent carries AUS-specific prompts, tone, and guardrails, grounded in faculty-approved texts and OER, returning inline citations to those sources so learning stays transparent and verifiable.

**Cost under the university's control** — Per-student usage controls let AUS manage consumption directly, and they can be adjusted at any point by the university rather than renegotiated with a vendor.

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## Model Independence

### The university's keys, with a fallback ready

The setup is model-agnostic and runs on AUS-provided API keys by default, with a pre-selected secondary LLM standing by if service quality fluctuates. Switching does not require changing the agents — the model is configuration, not architecture.

Why this matters mid-term: a semester is long enough for a model to be deprecated, repriced, or degraded. Being able to fail over without rebuilding the course agent is what keeps a term on schedule.

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## Code Interpreter

### To be useful in math, it has to actually compute

The agents run a secure code-execution environment, which is what separates a genuinely useful STEM assistant from a fluent one. Text prediction alone produces plausible-sounding but wrong mathematics.

**Plot and render** — Precise graphs of equations and vector fields, produced as images students can reference later.

**Check work numerically** — Verify limits and derivatives, evaluate integrals, and test boundary conditions rather than asserting a result.

**Sanity-check symbolic steps** — Sample values to spot algebraic slips and compare equivalent forms, catching errors a student would otherwise inherit.

The effect is fewer confidently wrong answers and clearer visual feedback — especially valuable in early calculus and mechanics, where a small algebraic slip propagates through everything after it.

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## How Impact Is Measured

### Targets published up front, not after the fact

| Measure | Target | How it's checked |
|---|---|---|
| Graphing accuracy | ≥95% pass | Weekly 25-item checklist; critical issues resolved within five business days |
| Explanation quality | avg ≥4.2 / 5 | Monthly sampling on correctness, clarity, and alignment to sources |
| Student perception | ≥80% helpful | Students rating the agent "helpful" or "very helpful" |
| Adoption | ≥70% of enrolled students | Unique users, sessions, and messages per session |

Quality is monitored continuously through monthly response audits, targeted spot-checks on graphing, and in-product flagging — faculty, and optionally students, can flag any response. Issues are triaged, fixed with precise prompt or dataset changes, and tracked in a shared log.

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## Access and Support

**Inside the LMS students already use** — Access arrives through secure links or LTI, with the technical groundwork — HTTPS, CSP allow-listing, standard LTI claims for user, role, and course — handled up front so access is smooth across browsers and sections.

**Faculty enablement** — Hands-on working sessions per course for setup, testing, and deployment, plus asynchronous support throughout the term for prompt tuning and dataset adjustments, and an end-of-term debrief on usage, satisfaction, and accuracy.

Clear division of responsibility: AUS provides API keys, approves source materials or OER substitutes, and names a lead faculty member per course. Issues follow a defined path — contain, fix, verify, log — with response targets by severity.

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## Get Started

### Run this in your hardest courses

Course-tuned agents grounded in your own materials, computing rather than guessing, on your API keys — with targets agreed before anyone calls it a success.

- Book a Demo: https://cal.com/iblai/30min
- Get Started Free: https://ibl.ai/join
- Download & Own Your AI: https://ibl.ai/download