Section A: Project Information
The "EE Mentor Bot" and "IA Mentor Bot" are innovative AI-powered tools designed to revolutionize support for IB Extended Essay (EE) and Internal Assessment (IA) processes, addressing a critical educational challenge: the overwhelming hours-per-student workload for teachers, especially in under-resourced schools. Built on no-code platforms, these bots leverage the strengths of Flint, Poe, DeepSeek, and ChatGPT to automate feedback, source validation, and rubric alignment, saving teachers hours per student while enhancing student research skills and promoting equity.
Key Innovations: The project’s novelty lies in its tailored approach to IB-specific needs, integrating multiple AI models customized by teachers to analyze drafts against the 2025 IB rubrics and syllabus. Unlike generic tools, it adapts to diverse subjects (e.g., Physics’ Mechanics topic) and supports the IB’s 2027 reflection emphasis, ensuring academic integrity with teacher oversight.
Design Concepts: The solution employs computational thinking—abstraction to focus on rubric criteria, decomposition to analyze draft sections, pattern recognition to identify errors, and algorithmic thinking to sequence tasks—within a user-friendly, no-code interface. Teachers customize AI prompts (e.g., “Check syllabus alignment”) via a dashboard, while students upload drafts for real-time feedback.
Technical Principles: AI models process inputs, requiring no coding. Flint provides structured feedback, Poe cross-references rubrics, DeepSeek ensures technical accuracy, or ChatGPT simplifies explanations, delivering outputs.
Potential Impact: By reducing teacher burnout and providing consistent support, the bots enhance student outcomes and equity, scalable to 5,000+ IB schools. A pilot will refine the prototype, aligning with SDG (inclusive education). Ethical safeguards ensure responsible use, making this a transformative IB solution.
Section B: Participant Information
| Title | First Name | Last Name | Organisation/Institution | Faculty/Department/Unit | Phone Number | Contact Person / Team Leader | |
|---|---|---|---|---|---|---|---|
| Mr. | Sanjay | Dey | Keystone Academy | Secondary science teacher | sanjay.dey@keystoneacademy.cn | +8618515942572 |
|
Section C: Project Details
The "EE Mentor Bot" and "IA Mentor Bot" emerged from my experience as an IB teacher at Keystone Academy, where I observed the significant burden of guiding students through Extended Essays (EE) and Internal Assessments (IA). Spending hours per student on repetitive tasks—feedback, rubric alignment, and source validation. This inconsistency leave students, particularly from diverse backgrounds, struggling with unclear research questions and syllabus misalignment, impacting their grades and university prospects. The inspiration struck during the IB’s 2027 EE update discussions, highlighting increased reflection demands, which intensified the workload. I hypothesized that AI could automate these tasks, inspired by tools like ChatGPT, Poe, Deepseek, Flint.
The hypothesis is that an AI-powered, no-code chatbot built, integrating Flint, Poe, DeepSeek, and ChatGPT, can reduce teacher workload hours per student while improving student outcomes. Success hinges on leveraging each AI’s strengths customized by teachers via intuitive prompts. This approach addresses a real educational gap, as IB’s niche requirements (e.g., Physics syllabus alignment) are underserved by generic tools. The project’s relevance lies in its potential to enhance equity, aligning with SDG, by providing consistent support to schools. Ethical safeguards ensures feasibility, making this a transformative solution for IB education.
Technology and Resources: The "EE Mentor Bot" and "IA Mentor Bot" are built on no-code platform, integrating Flint, Poe, DeepSeek, and ChatGPT to analyze IB EE/IA drafts. Interface requires no coding, making development accessible. Resources include free Flint, Poe, DeepSeek, ChatGPT, and the 2025 IB rubrics/syllabus as knowledge bases. Development needs minimal costs (free tiers) and 5–10 hours for setup, testing, and training.
Market Demand Validation: Demand is evident from IB teachers’ workload struggles and the IB’s 2027 EE updates increasing complexity. Engagement with IB World Schools (5,000+ globally) via conferences will further validate need.
Core Functionalities: The bots automate feedback, source validation, and rubric alignment, delivering syllabus-aligned support. Teachers customize AI prompts bots, while students upload drafts via a chat interface.
User Experience and Metrics: A user-friendly chat interface ensures accessibility; 1-hour teacher training and 30-minute student tutorials guarantee adoption. Feedback logs in bot allows teacher oversight. Performance metrics include time saved, student grade improvement, and satisfaction.
Functional Architecture and Workflow: The "EE Mentor Bot" and "IA Mentor Bot" operate on bot’s no-code platform, like Flint, Poe, DeepSeek, and ChatGPT. Students upload EE/IA drafts (PDF/text) through a chat interface. Bot routes inputs to AIs, which analyze drafts against 2025 IB rubrics/syllabus, generating feedback . Feedback is delivered via chat, with logs stored for teacher review. Teachers customize AI prompts bot's dashboard.
Innovative Features Implementation: The customization feature leverages AI strengths. Implementation involves bot’s settings and designing flows (e.g., upload → analyze → feedback) using its drag-and-drop interface, requiring no coding.
Performance Metrics: Effectiveness is measured by time saved, grade improvement, and user satisfaction. A pilot will validate these, refining features for scalability.
Implementation Plan: Phase 1: Setup and integrate AIs (Week 1). Phase 2: Train teachers (1 hour) and students (30 minutes) (Week 2). Phase 3: Pilot and iterate (Weeks 4–12). No conversion plan applies, as the solution is new.
Technology-Function Relationship: Bot’s no-code platform enables the chat interface and AI integration, ensuring accessibility. AIs process drafts (Flint, Poe for rubrics; DeepSeek, ChatGPT for syllabus/explanations), delivering precise feedback. Progress: Prototype ready for pilot, with initial tests showing accurate feedback.
The "EE Mentor Bot" and "IA Mentor Bot" offer an innovative and creative solution to the IB teacher workload crisis, where hours per student are spent on EE/IA feedback, rubric alignment, and source validation, particularly straining under-resourced schools. Unlike generic AI tools, this project uniquely tailors support to the IB’s niche requirements, integrating bot’s no-code platform with Flint, Poe, DeepSeek, and ChatGPT, marking a creative leap in educational technology.
Innovation: The solution’s novelty lies in its customizable AI ensemble, allowing teachers to harness each model’s strengths. This adaptability addresses the IB’s diverse subjects and the 2027 EE reflection focus, an underserved area. The no-code bot design democratizes access, empowering non-technical educators to build and tweak bots, a creative departure from programming-heavy solutions.
Creativity: The project creatively applies computational thinking abstraction to prioritize rubric criteria, decomposition to analyze draft sections, and pattern recognition to spot errors within a user-friendly chat interface. This transforms a repetitive task into an interactive, student-centered experience, enhancing engagement. The bot’s ability to deliver real-time, syllabus-aligned feedback with teacher oversight fosters a collaborative learning environment, a fresh approach to IB support.
Enhanced Effectiveness: These elements reduce teacher burnout by saving hours per student, freeing time for mentoring. Students gain consistent, equitable guidance, improving grades and building research skills, especially in under-resourced settings. The creative integration of multiple AIs ensures robust, tailored support, while the no-code platform scales access globally, aligning with SDG.
To ensure scalability, the "EE Mentor Bot" and "IA Mentor Bot" are built on no-code platforms like Flint, Poe, DeepSeek, and ChatGPT, enabling easy replication, updates, and deployment without extensive developer input. This modular architecture allows seamless scaling across IB schools globally. As user demand increases, load can be distributed across AI APIs, each specializing in rubric alignment, syllabus verification, or feedback refinement. Cloud-based processing and flexible API limits ensure performance remains reliable, while prompt optimization minimizes computational load.
Potential bottlenecks—such as AI model lag or draft complexity—are mitigated by routing tasks to specialized models, parallelizing workflows, and caching repeated queries. Teacher-controlled prompt customization reduces support burden while enabling localized optimization for subjects and grade levels. As more educators adopt the bots, shared prompt libraries will streamline onboarding and standardization.
For environmental sustainability, the project relies on lightweight AI interactions over chat interfaces, significantly reducing resource-intensive synchronous support hours. The minimal development footprint (free-tier tools, low hardware needs) ensures a low-carbon impact. Encouraging digital submissions, iterative feedback, and cloud processing reduces printing, storage, and travel-based mentoring.
Long-term engagement is fostered by a student-centered interface that provides real-time, interactive feedback, increasing motivation and self-efficacy. Teachers remain in control of customization and oversight, promoting professional ownership. As IB requirements evolve (e.g., EE 2027 reflection emphasis), prompt libraries and AI capabilities can be easily updated—ensuring adaptability without rebuilding systems.
The bots are future-proofed by aligning with Sustainable Development Goals (SDGs), especially equitable access to quality education. By reducing teacher burnout and democratizing expert feedback, the solution is designed to scale ethically, sustain impact, and evolve alongside IB pedagogy.
The “EE Mentor Bot” and “IA Mentor Bot” directly address educational inequality and teacher burnout—two persistent social issues within the global IB ecosystem. Many students, particularly in under-resourced or non-native English-speaking contexts, lack consistent, subject-specific feedback. This disproportionately affects their academic outcomes and university prospects. By automating feedback, rubric alignment, and syllabus validation, the bots democratize access to high-quality academic support, fostering equity and inclusion.
The solution enhances the lives of its primary beneficiaries—students and teachers—by redistributing time, reducing stress, and increasing access to individualized, syllabus-aligned feedback. For teachers, it saves hours per student, allowing deeper focus on mentoring and well-being. For students, especially those in underserved regions, it provides reliable academic guidance previously limited to privileged settings. The bots support neurodiverse learners by offering structured, repeatable guidance in a low-pressure chat interface, enhancing educational accessibility.
The project aligns with broader social goals, particularly Quality Education and Reduced Inequalities, by leveling the academic playing field across diverse IB schools. By empowering non-technical educators to build and customize bots without coding, the platform also promotes professional agency and inclusion within educational innovation.
Social impact metrics include: Reduction in teacher workload (measured in hours saved per EE/IA), Improvement in student outcomes, Adoption across diverse socioeconomic and linguistic contexts, Student and teacher satisfaction (via surveys and feedback logs), Retention and reuse of the bot in subsequent academic cycles
Responsiveness is ensured through ongoing pilot feedback, editable prompt libraries, and an open-source mindset encouraging community adaptation. Regular engagement with IB educators, student focus groups, and data logs will drive iterative refinement, ensuring the solution continues to meet evolving academic, linguistic, and cultural needs.
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