ACC 2026 · 参赛项目
The intelligence layer for education.
AGENT STATUS
已回传
最近同步：2026/10/24 14:50:20
01 / BUILDER
选手 / 负责人
Zarrar Hassan
个人定位
Founder of plain. Building the intelligence layer for education.
个人介绍
I'm Zarrar Hassan, founder of plain. We're building the intelligence layer for education—technology that gives institutions a continuous understanding of how students learn. I lead the company's product, engineering, and strategy. We've built an operating platform connecting assessment, learning diagnostics, and personalized prac tice, now being validated in university classrooms. Our work extends into the Adaptive Learning Intelligence Engine, a proposed diagnostic architecture documented in a filed US provisional patent application. We're starting with assessment because that's where much of the evidence of learning already exists. The ambition is to make that evidence useful across the entire educational system.
职业背景
Founder and product engineer of plain, leading product architecture, software engineering, strategy, commercialization, and institutional development. Lead inventor of the Adaptive Learning Intelligence Engine, documented in a filed US provisional pat ent application. plain received the Most Potential Award at Startup Alliance Venture Builders 2026 i n Beijing and was among five of 14 roadshow ventures selected for an opportunity to advance towards I TEC. At EdUHK's AIREA 2026 competition, plain received the Excellence in Educational Impact Award followi ng GCC Top 3 recognition and advancement to the international finals. Selected for Saudi NeLC's AI Sandbox; technical review passed and pilot testing upcoming. Venture-development experience: YSpace Venture Catalyst, Italia360, YSpace GBA Startup Sprint, and T MU Startup Society. Pursuing Mechatronics Engineering at Beijing Institute of Technology on a first-year full-tuition sc Holarship
所在城市
Beijing, China
AI + Education · Educational Technology
02 / PRODUCT
plain is building the intelligence layer for education: a continuous connection between evidence of student understanding and the next instructional decision. Its operating teacher web platform and student iOS application connect assessment, AI-assisted diagn ostics, learning analytics, and personalized practice. Teachers generate assessments from instructional materials, including PowerPoint presentations. Entry Cards capture understanding before instruction; Exit Cards reveal what changes afterward. AI-assisted grading and paired assessment analysis help surface misconceptions, knowledge gaps, and patterns of understanding. Teacher dashboards guide instructional follow-up; the student app supports targeted practice. An ongoing Arizona State University classroom pilot has more than 60 active students as of October 2 4, 2026. Classroom use and educator feedback inform development. A Saudi Electronic University pilot is being prepared for the next academic semester. Work with Jazan University concerns research collaboration and pathways towards institutional adoptio n; future deployment scope remains under development. plain was selected for the Saudi National eLea rning Center's six-month AI Sandbox in Digital Learning, passed technical review, and is preparing f or the Pilot & Testing Phase. The proposed Adaptive Learning Intelligence Engine explores diagnostic continuity across assessments , prerequisite gaps, and longitudinal learning context. It is documented in a filed US provisional patent application, distinct from the operating product. The objective is to make understanding actionable across education.
产品阶段
launch
当前版本
plain v1.0.3;
核心功能：Assessment Intelligence · Entry & Exit Cards · Misconception & Knowledge-Gap Detection · Paired Learning A nalysis · Instructional Intelligence Dashboard · Personalized Practice · Connected Teacher & Student Exper Ience
技术标签：React · React Native · Firebase Authentication · Firestore · Cloud Functions · JSZip · OpenAI APIs
03 / MILESTONES
2025-10-07 · 已完成
Selected for YSpace Venture Catalyst
Selected for York University’s YSpace Venture Catalyst Fall 2025 cohort, focused on customer discovery, venture development, commercialization, and growth strategy.
2026-07-16 · 已完成
Excellence in Educational Impact — AIREA 2026
plain received the Excellence in Educational Impact Award at the 2026 International Competition on AI in Education, hosted by The Education University of Hong Kong, following GCC Top 3 recognition and advancement to the international finals.
2026-10-24 · 已完成
Arizona State University — 60+ Active Pilot Students
plain’s ongoing classroom pilot at Arizona State University has more than 60 active students as of October 24, 2026. The platform’s assessment, learning diagnostics, and instructional workflows are being refined through classroom usage and educator feedback.
04 / FROM THE BUILDER
plain｜教育智能层 教育已经拥有完善的内容平台、学习管理系统和考试评价工具，但仍然缺少能够持续理解学习过程的智能基础设施。 plain 正在构建这一智能层。 我们将课堂评估、AI 辅助诊断、知识误区识别、学习分析和个性化练习连接成完整的教学反馈流程。 教师可以通过课前和课后评估了解学生的知识基础与学习变化，识别仍然存在的理解偏差，并据此调整教学和后续练习。 目前，plain 正在亚利桑那州立大学开展课堂试点，已有 60 多名活跃学生参与。我们正在准备沙特电子大学下一学期的试点，并与吉赞大学围绕教育人工智能研究及未来院校级应用开展合作。 plain 曾获得国际人工智能教育赛事的教育影响力奖项，并入选沙特国家电子学习中心 AI Sandbox 项目。2026 年 10 月，plain 还在北京 Startup Alliance Venture Builders 项目中获得 Most Potential Award，并获得 ITEC 后续晋级机会。 我们的目标不只是让教育产生更多数据，而是让学习过程能够被持续理解，并让这些理解真正服务于教学决策。 plain，让理解成为教育的基础能力。 Presenter: Zarrar Hassan, Founder of plain. iOS: plain edu v1.0.3; App Store developer: Ehsan Ahmad. Release note: per-course activation of Personalized Learning Insights through a one-time in-app purchase, providing six months of access per course. iOS only; no ACC release date claimed. Established product evolution (not dated ACC releases): 1. Assessment intelligence: AI-assisted assessment generation from instructional materials, including PowerPoint presentations. 2. Continuous classroom learning loop: Entry and Exit Cards provide comparable pre- and post-instruction evidence of understanding. 3. Misconception and learning-gap diagnostics: AI-assisted analysis identifies knowledge gaps, misconceptions, and instructional areas requiring attention. 4. Teacher instructional intelligence: dashboard with assessment outcomes, paired Entry/Exit comparisons, learning trends, and diagnostic insights. 5. Personalized student intervention: connected iOS experience with assessment feedback and targeted follow-up practice. October 21–24 development changelog: not supplied or verified. 30-day roadmap | October 26–November 24, 2026 Objective: advance from active classroom validation toward a repeatable institutional deployment model. Planned actions/outputs, not completed results or commitments. Days 1–7 — Product reliability: validate teacher/student workflows, improve onboarding and reliability, investigate mainland China authentication/access, and establish a reliable demo environment. Output: documented test results and prioritized fixes. Days 8–14 — Learning intelligence: refine Entry/Exit comparisons, diagnostics, misconception identification, and teacher analytics using authorized pilot observations and educator feedback. Output: documented diagnostic improvements and structured feedback backlog. Days 15–21 — Institutional pilot readiness: prepare SEU next-semester pilot scope, onboarding, technical requirements, teaching workflow, and evaluation measures; continue Jazan research and adoption discussions. Output: pilot-readiness documentation and proposed evaluation framework. Days 22–30 — Commercialization and expansion: develop institutional onboarding/demo materials, refine licensing/pricing assumptions, prioritize university outreach, and set the next 60–90-day roadmap. Output: repeatable institutional pilot package and commercialization priorities. The online URL is the company website; authenticated teacher-demo access is not verified.
目标用户
University educators, students, and institutions.
核心问题
Assessment results are often disconnected from the context needed to understand misconceptions, trac k learning, and decide what should happen next.
当前方案
A teacher web platform and student iOS application connecting assessment generation, Entry and Exit Cards, AI-assisted grading and diagnostics, learning analytics, and targeted practice.
五日目标
Build and validate the intelligence layer for education: a continuous system connecting instructiona l content, assessment, student-response analysis, diagnostic insights, and personalized practice. The immediate objectives are to improve classroom reliability, deepen evidence-based teaching insigh ts, validate the platform through university pilots, and prepare a repeatable model for institutiona I deployment.
BASELINE → FINAL
初始快照 · 10/24 14:50
Private project profile created from founder-approved copy.
最近进展 · 10/24 17:12
Original product demonstration recording, 5 minutes 15.73 seconds. Shows existing teacher and student workflows. Recording date and demonstrated build version are unverified; this upload is not evidence of development during October 21–24. Retain as offline presentation backup. Acceptance instead of a separate 90-second video remains pending organizer confirmation.
FIVE-DAY BUILD LOG
Original product demonstration recording, 5 minutes 15.73 seconds. Shows existing teacher and student workflows. Recording date and demonstrated build version are unverified; this upload is not evidence of development during October 21–24. Retain as offline presentation backup. Acceptance instead of a separate 90-second video remains pending organizer confirmation.
Private project profile created from founder-approved copy.
完成：Founder-approved project profile prepared
下一步计划
阻塞事项