Education & EdTech software development & AI engineering

Requirements include FERPA for student education records, COPPA for services used by children under 13, state-level student data privacy laws, and Section 508/WCAG accessibility standards for publicly funded education technology.

Yes — predictive models analyzing attendance, grades, and engagement signals can flag at-risk students earlier, allowing advising staff to intervene before a student disengages completely, though outreach and support decisions remain with human advisors.

Generative AI produces content — lesson plans, feedback drafts, progress reports. Agentic AI takes multi-step actions autonomously, like monitoring student data across systems and flagging risk indicators, often incorporating generative AI as one step within a broader workflow.

Adaptive learning platforms adjust content difficulty and sequencing based on a student's demonstrated performance and mastery, using algorithms that respond to real-time assessment data rather than presenting a fixed curriculum path to every student.

It depends on the use case, but common components include LTI-compliant LMS integration, PostgreSQL for structured student data, vector databases like Qdrant for semantic search over curriculum content, and cloud infrastructure configured for FERPA-aligned data handling.

It depends on whether the need is a commodity function (standard LMS features, common assessment tools) — typically better bought — or a workflow specific to the district or institution's academic model, which usually justifies custom development or deep integration work.

Widespread student access to generative AI has forced institutions to redesign assessments around process-based evaluation and AI-aware assignment design, since detection tools alone have proven unreliable as a sole safeguard against AI-assisted work.

ROI typically shows up as reduced document processing time, faster financial aid turnaround, and improved enrollment conversion rates tied to more responsive communication during peak enrollment periods.

Bias mitigation requires diverse and representative training data, ongoing model performance monitoring across student demographic subgroups, and keeping final intervention decisions with human counselors and advisors rather than automating them fully.

LTI (Learning Tools Interoperability) is a standard that allows third-party educational tools to integrate directly within an LMS, enabling single sign-on and grade passback without requiring students or teachers to use separate, disconnected platforms.

IEP management software structures documentation, compliance deadlines, and progress monitoring for individualized education plans, helping special education staff meet regulatory requirements while reducing manual tracking burden.

This includes WCAG 2.1 compliant interface design, screen reader compatibility, keyboard navigation support, and accessibility testing throughout development rather than as a final compliance check, since retrofitting accessibility is significantly more costly than building it in from the start.

Institutions use predictive models and Agentic workflows to flag degree progress issues, course registration reminders, and at-risk indicators for advisors, allowing proactive outreach rather than waiting for students to request help.

Pilots for well-scoped use cases like lesson planning support or grade passback automation often move from pilot to production within a semester, while broader adaptive learning or unified data platform initiatives typically take longer due to the number of systems involved and academic calendar constraints.

Look for demonstrated experience with LMS and SIS integration standards, a track record of passing district or university procurement and security review, clear awareness of FERPA/COPPA and accessibility requirements, and a delivery process that respects academic calendars and educator training needs.