--- title: "American University of Sharjah × ibl.ai: Course-Tuned AI Mentors for Calculus & Physics" slug: "american-university-of-sharjah-iblai-course-tuned-ai-mentors-for-calculus-physics" author: "Jeremy Weaver" date: "2025-09-18 16:18:26.637548" category: "Premium" topics: "American University of Sharjah AUS AI mentor ibl.ai partnership mentorAI Calculus mentorAI Physics AI code interpreter Graph equations AI STEM AI tutoring AI course assistants Higher education AI pilot UAE university AI LLM in STEM education AI for calculus students AI for physics students Course-tuned AI mentors AI-generated graphs AI problem-solving support Pedagogically aligned AI Transparent cited answers University AI mentoring" summary: "AUS and ibl.ai are launching a fall pilot of course-tuned AI mentors for Calculus and Physics that use a code interpreter to compute, visualize, and cite instructor-approved resources—helping students learn reliably and transparently." banner: "" thumbnail: "" --- We’re excited to share that ibl.ai is partnering with the **American University of Sharjah (AUS)** on a focused Fall-2025 pilot of mentorAI in two gateway STEM courses—**Calculus I (Math 103)** and **Physics 101**. The pilot is designed to validate instructional impact, technical fit, and day-to-day faculty and student workflows before AUS considers a broader rollout. --- # What We’re Building Together - **Two course-specific student mentors** tuned for Math 103 and Physics 101 with AUS-specific prompts, tone, and guardrails. Each mentor is grounded in faculty-approved texts/OER and returns **inline citations** to those sources to keep learning transparent and verifiable. - **Model-agnostic setup** using AUS-provided API keys by default, with a pre-selected secondary LLM ready as a fallback if service quality fluctuates—no mentor changes required. A per-student usage cap (initially 50 messages/term) helps AUS manage consumption and can be adjusted during the pilot. # Why The Code Interpreter Matters For STEM Reliability To be genuinely useful in math-heavy classes, an assistant has to **compute and visualize**, not just chat. AUS’s mentors will use a secure code-execution environment (“code interpreter”) to: - **Plot functions and render precise graphs** of equations and vector fields (as images students can reference later). - **Check work numerically** (e.g., verify limits/derivatives, evaluate integrals, test boundary conditions). - **Sanity-check symbolic steps** by sampling values, spotting algebraic slips, and comparing equivalent forms. This dramatically reduces “plausible-sounding but wrong” answers, and it gives students clear, visual feedback—especially vital in early calculus and mechanics. # Simple Student Access In AUS’s LMS To make the mentors easy to reach where students already work, we’ll provide **LMS integration** options—secure links or LTI—plus lightweight onboarding guidance. Technical items such as HTTPS, CSP allow-listing, and passing standard LTI claims (user/role/course) are covered up front so access is smooth across browsers and sections. # How We’ll Measure Impact The pilot focuses on a few concrete targets and a tight feedback loop: - **Graphing accuracy**: ≥95% pass on a weekly 25-item checklist, with critical issues resolved within five business days. - **Explanation quality**: Monthly sampling scored on correctness, clarity, and alignment to sources (avg ≥4.2/5), and ≥80% student “helpful/very helpful.” - **Adoption & engagement**: ≥70% of enrolled students use the mentor at least once (tracking unique users, sessions, messages/session). Quality is continuously monitored via monthly response audits, targeted spot-checks for graphing, and **in-product flagging** (faculty—and optionally students—can flag any response). Issues are triaged and addressed through precise prompt/dataset tweaks and tracked in a shared log. # Faculty Enablement And Support AUS instructors will receive up to **two working sessions per course** (setup, testing, and deployment strategies), plus asynchronous support during the term for prompt tuning, dataset adjustments, and minor configuration toggles. At term’s end, we’ll host a debrief and deliver a **brief pilot report** (≤5 pages) summarizing methods, usage, satisfaction, notable accuracy issues/resolutions, and recommendations—with cost/scale implications for Spring 2026. # Roles, Responsibilities, And Risk Management AUS will provide API keys and approve source materials (or OER substitutes), identify a lead faculty member for each course, and coordinate internal approvals. We’ll operate to clear service levels: rapid acknowledgement for issues, defined response targets by severity, and a straightforward resolution path (contain → fix → verify → log). # What Success Looks Like—And What’s Next Success is defined by **accuracy, adoption, and instructional fit** (a brief rubric on alignment, ease of in-class use, out-of-class study support, and time/overhead). If targets are met, AUS and ibl.ai will agree on a **path to expansion** for Spring 2026 across additional courses/programs based on evidence from this pilot. --- # Conclusion We’re honored to collaborate with the **American University of Sharjah** on this measured, student-first approach to AI mentoring—and we look forward to sharing what we learn together this fall. If you’re interested in an ibl.ai pilot for your institution, visit **ibl.ai/contact** to learn more!