Kuliso

Kuliso is a 3-in-1 AI teaching assistant for K-12 multilingual classrooms. In every session, students simultaneously: Learn academic content aligned to grade-level standards (Common Core, TEKS, CPALMS, NGSS, etc.) Practice communication skills through interactive, conversational tutoring Develop English language proficiency via interwoven translation that bridges their home language and classroom English Teachers set the curriculum. Students learn in the language they think in. The AI weaves English vocabulary and phrasing into native-language instruction — so students aren't just memorizing answers, they're building real English fluency alongside academic mastery. Core Value Proposition One tool replaces three. Districts don't need separate products for academic tutoring, communication practice, and ELD. Kuliso does all three in a single student session — saving money, reducing tool fatigue, and producing better outcomes. What Makes Kuliso a Game Changer It Thinks Like a Teacher, Not a Chatbot Most EdTech tools react to what a student types. Kuliso anticipates what each student needs next. Its auto-differentiation engine monitors every student in real time — detecting fatigue (response time doubling, accuracy drops, rapid guessing) and misunderstanding (repeated errors on the same concept, CPA stage mismatch) — and automatically reassigns activities at the right level and modality. No teacher input needed. The teacher gets flagged only when the AI has exhausted its own strategies. It Protects Productive Struggle Unlike platforms that optimize for engagement metrics ("keep the kid clicking"), Kuliso optimizes for learning — even when that means letting a student sit with discomfort. The system distinguishes between a student who's struggling to learn (exploring, improving slowly, thinking deeply) and a student struggling to survive (frozen, guessing randomly, shutting down). It holds off when struggle is productive and intervenes with graduated support when it isn't: encouragement first, scaffolding second, modality switch third, teacher flag last. It Keeps Parents in the Loop Without Replacing Them Technology should amplify parenting, not replace it. Kuliso's Parent Engagement Suite turns screen time into family conversation — daily "Ask Me About..." nudges give parents a conversation starter in their language after every session, and weekly accountability check-ins let parents review progress and set goals with their child. The kid teaches the parent what they learned at dinner. That's real engagement. It Arms Schools with Data That Speaks Louder Than Opinions When IEP/504 accommodations are on the table, Kuliso provides objective evidence that keeps decisions grounded in what the student actually needs. Auto-generated evidence packets show before/after performance data for every accommodation. An immutable decision documentation trail logs every add, modify, or remove — with the data snapshot at the time. If an accommodation is removed against the evidence, that's flagged permanently. No admin has to make decisions based on pressure when the numbers are right there on screen. Key Features Intelligent Tutoring SoBot AI tutor — visual aids, step-by-step instruction, Gradual Release (I Do/We Do/You Do), adaptive language bridging, and Socratic questioning Adaptive skill assessment — diagnostic quiz tailors AI difficulty to each student's level 20+ tutoring languages with auto-detect + "Other" option RTL support for Arabic, Dari, and Pashto SEL integration woven cross-curricularly into academic content Test stamina builder for state/provincial exam preparation Learning Targets Engine Teachers create bilingual "I can..." objectives tied to state standard codes (CCSS, TEKS, CPALMS, NGSS, SOLs, etc.) Targets display automatically before every SoBot session, Reading Hub article, Unit Prep, and Benchmark activity SoBot references the active target Socratically during tutoring — keeping students aware of why they're learning what they're learning Post-activity self-assessment — students rate their own understanding (👍 / 👉 / 👎) after each activity Teacher analytics — per-target data on display count, SoBot references, ELL engagement, and student self-assessment trends Auto-generated Evidence Portfolio text ready to copy for evaluations and compliance documentation Auto-Differentiation System Real-time monitoring of every student during activities Fatigue detection: session exceeds grade-band attention limits (K-2: 15 min, 3-5: 20 min, 6-8: 30 min, 9-12: 40 min), response time doubling, accuracy drops 40%+, rapid guessing patterns Misunderstanding detection: 3+ consecutive wrong answers on the same standard, CPA stage mismatch, student thumbs-down self-assessment Automatic reassignment: fatigue → different modality on the same standard; misunderstanding → step down scaffolding or targeted reteach; both → screen break